Systems and methods for using a potassium sensor to provide therapy management guidance for medication

Abstract

Certain aspects of the present disclosure provide systems and methods for continuously monitoring potassium data and other analyte and / or non-analyte data of a patient to provide personalized therapy management guidance.

Description

SYSTEMS AND METHODS FOR USING A POTASSIUM SENSOR TO PROVIDE THERAPY MANAGEMENT GUIDANCE FOR MEDICATION CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No.63 / 740,091, filed December 30, 2024, which is incorporated by reference herein in its entirety, and is hereby expressly made a part of this specification.INTRODUCTION

[0002] Heart failure (HF), also known as congestive heart failure (CHF), affects about 1-2% or more of the adult population in developed countries. HF is a syndrome caused by an impairment in the heart’ s ability to fill with (diastolic) and / or pump (systolic) blood. HF is common, costly, and potentially fatal. It is the leading cause of hospitalization for people over 65 years old and constitutes the leading disease in terms of healthcare costs in many countries.

[0003] Renin-angiotensin-aldosterone system inhibitors (RAASi) are a group of drugs that act by inhibiting the renin-angiotensin-aldosterone system (RAAS) and include angiotensinconverting enzyme inhibitors (ACE inhibitors), angiotensin-receptor blockers (ARBs), and direct renin inhibitors. Due to their anti-hypertensive qualities, ACE inhibitors and ARBs are commonly used in the treatment of patients with HF, as well as patients with hypertension, certain types of chronic kidney disease (CKD), and patients who have had myocardial infarctions (i.e., heart attacks). However, ACE inhibitors and ARBs, as well as other RAASi medications, pose a significant risk of hyperkalemia, which results from either reduced secretion of aldosterone or increased resistance to aldosterone. Further, many of the conditions for which RAASi medications are recommended further amplify the risk for hyperkalemia in and of themselves.

[0004] Aldosterone, which is produced in the adrenal cortex of the adrenal gland, is the main mineralocorticoid hormone in the body and is responsible for regulating the excretion of potassium. When a patient taking a RAASi medication, or another medication that alters aldosterone levels, develops hyperkalemia (with or without knowledge thereof), the high potassium levels can lead to sudden cardiac death, arrhythmia, and / or other severe health concerns if left untreated.

[0005] In some cases, HF can cause fluid retention and swelling in various areas of the body. In such cases, patients will often use diuretics to manage the buildup of excess fluids. While diuretics can help to remove excess fluids from the body, like RAASi medications, they can also affect potassium levels and cause potassium imbalances, which can lead to various complications (e.g., sudden cardiac death, arrhythmia, etc.) if left untreated.

[0006] Thus, while both RAASi medications and diuretics can be effective in treating HF and other conditions, their impact on potassium homeostasis and the potential for causing additional complications often leads to these therapies being prescribed at ineffective quantities, being prematurely stopped, and / or being complete avoided.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] So that the manner in which the above-recited features of the present disclosure can be understood in detail, a more particular description, briefly summarized above, can be had by reference to aspects, some of which are illustrated in the drawings. It is to be noted, however, that the appended drawings illustrate only certain typical aspects of this disclosure and are therefore not to be considered limiting of its scope, for the description can admit to other equally effective aspects.

[0008] FIG. 1 illustrates aspects of an example therapy management system used in connection with implementing embodiments of the present disclosure.

[0009] FIG. 2 is a diagram conceptually illustrating an example continuous analyte monitoring system including example continuous analyte sensor(s) with sensor electronics, according to certain embodiments of the present disclosure.

[0010] FIG. 3 illustrates example inputs and example metrics that are calculated based on the inputs for use by the therapy management system of FIG. 1, according to certain embodiments of the present disclosure.

[0011] FIG. 4 illustrates a flow diagram of an example method for providing therapy management support using a continuous analyte monitoring system configured to continuously measure at least potassium levels, according to certain embodiments of the present disclosure.

[0012] FIG. 5 illustrates another flow diagram of an example workflow using the method of FIG. 4 for providing therapy management support using a continuous analyte monitoring system configured to continuously measure at least potassium levels, according to certain embodiments of the present disclosure.

[0013] FIG. 6 is a flow diagram depicting a method for training machine learning models to optimize medication doses for a host, according to certain embodiments of the present disclosure.

[0014] FIG. 7 is a block diagram depicting a computing device configured to perform the operations of FIGs.4 and 5, according to certain embodiments of the present disclosure.

[0015] FIGs. 8A-8B depict exemplary enzyme domain configurations for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

[0016] FIGs. 8C-8D depict exemplary enzyme domain configurations for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

[0017] FIG. 8E depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

[0018] FIGs. 9A-9B depict alternative views of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

[0019] FIGs. 9C-9D depict alternative views of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

[0020] FIG. 9E depicts an exemplary dual electrode configuration for a continuous multianalyte sensor, according to certain embodiments of the present disclosure.

[0021] FIG. 10A depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

[0022] FIGs. 10B-10C depict alternative exemplary enzyme domain configurations for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

[0023] FIG. 11 depicts an exemplary enzyme domain configuration for a continuous multianalyte sensor, according to certain embodiments of the present disclosure.

[0024] FIGs. 12A-12D depict alternative views of exemplary dual electrode enzyme domain configurations G1-G4 for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure.

[0025] FIGs. 13A-13B schematically illustrate an example configuration and component of a device for measuring an electrophysiological signal and / or concentration of a target ion in a biological fluid in vivo, according to certain embodiments of the present disclosure.

[0026] FIG. 14 schematically illustrates additional example configurations and component of a device for measuring an electrophysiological signal and / or a concentration of a target ion in a biological fluid in vivo, according to certain embodiments of the present disclosure.

[0027] FIGs. 15A-15C schematically illustrate example configurations and components of a device for measuring an electrophysiological signal and / or concentration of a target analyte in a biological fluid in vivo, according to certain embodiments of the present disclosure.

[0028] FIG. 16 is a diagram depicting an example continuous analyte monitoring system configured to measure target ions and / or other analytes as discussed herein, according to certain embodiments of the present disclosure.

[0029] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements disclosed in one aspect can be beneficially utilized on other aspects without specific recitation.DETAILED DESCRIPTION

[0030] Aspects of the present disclosure relate to systems and methods for continuously monitoring potassium, other analyte data, and / or non-analyte data to provide personalized therapy management guidance, including medication dosing guidance, to hosts, or patients, taking renin-angiotensin-aldosterone system inhibitors (RAASi) (e.g., angiotensin-receptor blockers (ARB)), diuretics, and other medications for conditions such as heart failure (HF) (also known as congestive heart failure (CHF)), chronic kidney disease (CKD), and / or other indications.

[0031] While RAASi medications and diuretics are effective therapy options for hosts suffering from HF and other conditions, their impact on potassium levels makes it difficult toappropriately prescribe and titrate the medications without increasing the risk of dangerous potassium imbalances. For example, usage of RAASi medications can reduce the bodily secretion of aldosterone and / or cause increased resistance to aldosterone, which can lead to abnormally high potassium levels (hyperkalemia). Diuretics, on the other hand, can cause the body to pass more potassium in the urine, which can lead to abnormally low bodily potassium levels (hypokalemia). Further, many of the conditions for which such medications are recommended further amplify the risk of potassium imbalances in and of themselves.

[0032] Although mild hyperkalemic and / or hypokalemic states can be asymptomatic, if left untreated, abnormally high and / or low potassium levels can lead to life-threatening complications such as fatal arrhythmias or respiratory muscle paralysis. Thus, it is critical to monitor for the risk or presence of potassium imbalances in hosts being treated with RAASi medications and diuretics, as well as other therapies affecting potassium levels.

[0033] Currently, to monitor for the risk or presence of potassium imbalance, a host must have their blood recurringly drawn at a medical clinic or other healthcare setting for testing. However, existing clinic-based methods of measuring potassium levels suffer from high rates of sampling error, thereby making such potassium measurements less reliable. And, since potassium measurements cannot be performed at home, frequent visits to the clinic for blood sampling can be a major inconvenience to the host. Furthermore, due to being point-in-time measurements, these methods do not provide any indication of patterns of potassium, so it is difficult to always detect abnormalities in potassium if the change is not drastic enough to be detected as point-in-time measurement.

[0034] Accordingly, existing techniques for determining whether hosts, such as HF patients, are suffering from dangerous potassium imbalances as a result of treatment with RAAS inhibitors, diuretics, and / or other therapies, are limited, inconsistent, and cumbersome. The same applies to hosts suffering from conditions such as CKD, acute kidney failure, and / or adrenal disease, which can cause potassium imbalances in and of themselves.

[0035] Currently, no therapy management systems exist to dynamically provide therapy management guidance to hosts to adjust medication doses and / or prescribe additional medications to avoid dangerously high or low potassium levels. More particularly, current therapy management systems are unable to monitor a host’s potassium levels in real-time and identify dangerous potassium imbalances, provide feedback to the host regarding the cause ofchanges in potassium levels, provide feedback on the effectiveness of a prescribed medication dose, provide therapy management guidance for titrating current medications and / or starting new medications to avoid dangerous potassium imbalances while effectively treating the host’s underlying condition, and / or notify the host of potassium imbalances in real-time to assist the host in identifying symptoms of potassium imbalance.

[0036] Consequently, there is a need in the art for an accurate, continuous analyte monitoring system to monitor a host’s potassium levels to predict and / or determine potassium imbalances. Such imbalances can be caused by medications, such as RAASi medications and / or diuretics for HF patients, therapies for other conditions, and / or by the conditions themselves. Continuous monitoring allows for calculating more effective medication doses to treat the host’s condition(s) while avoiding negative effects of potassium imbalance. Further, determining a cause of a potassium imbalance allows for effectively and accurately determining a treatment regimen to control potassium levels and maintain a host’s therapy regimen, such as medication dose(s), when potassium balance is determined to be unrelated to the medication. Accordingly, the present disclosure addresses the above needs and deficiencies, and more.

[0037] In particular, certain embodiments provided herein are directed to a therapy management system that monitors potassium data of a host via a continuous analyte monitoring system, and in some cases other analyte data and / or non-analyte data associated with the host, to determine when the host is at risk of, or is currently experiencing, a dangerous or potentially dangerous potassium imbalance. In such embodiments, the host can be an HF patient being treated with RAASi medications and / or diuretic medications, or a host suffering from a different condition and / or being treated with a different therapy that affects potassium levels. As used herein, a host’s disease or condition is referred to as their “disease state.”

[0038] In certain embodiments, the therapy management system continuously monitors at least potassium data of a host to provide a determination of a risk, or a presence of, a dangerous or potentially dangerous potassium imbalance for the host. For example, in such embodiments, the therapy management system can provide real-time identification and notification of a risk, or a presence of, a dangerous or potentially dangerous potassium imbalance to the host to assist the host in self-identifying symptoms of potassium imbalance. In such embodiments, otheranalyte data or non-analyte data can be further incorporated into the determination of the risk, or the presence of, a dangerous or potentially dangerous potassium imbalance for the host.

[0039] In some further embodiments, the therapy management system provides a determination of a cause of a future or current potassium imbalance for a host. For example, in such embodiments, the therapy management system can determine whether the future or current potassium imbalance is a result of the host’s disease state and / or any medications taken by the host based on the host’s current potassium data, the host’s historical potassium data, and / or potassium data from a host population.

[0040] In some further embodiments, the therapy management system provides a suggestion of an appropriate therapy for treating their disease state. For example, in such embodiments, the therapy management system can suggest one or more types of medication for treating their disease state while managing the host’s potassium levels.

[0041] In certain embodiments, the therapy management system provides personalized therapy management guidance for managing the host’s potassium levels while treating their disease state. For example, in such embodiments, the therapy management system can provide real-time guidance to the host to titrate the dose of a prescribed medication to control high or low potassium levels. In certain embodiments, the personalized therapy guidance that the therapy management system provides includes guidance to the host to take an additional medication (or a larger dose of an existing medication), such as a diuretic or potassium binder, to lower potassium levels upon determining at least a predetermined high level of potassium for the host (either currently or in the future). In certain embodiments, the personalized therapy guidance that the therapy management system provides includes guidance to the host to take an additional medication (or a larger dose of an existing medication), such as a potassium supplement, to increase potassium levels upon determining at least a predetermined low level of potassium for the host (either currently or in the future).

[0042] In certain embodiments, the personalized therapy guidance that the therapy management system provides includes guidance to the host to stop taking a certain medication (such as a RAASi medication), or to switch to another similar medication that does not raise potassium levels to the degree of the certain medication, to decrease potassium levels upon determining at least a predetermined high level of potassium for the host (either currently or in the future). In certain embodiments, the therapy management system provides real-timeguidance to the host to stop taking a certain medication (such as a diuretic), or to switch to another similar medication that does not lower potassium levels to the degree of the certain medication, to increase potassium levels upon determining at least a predetermined low level of potassium for the host (either currently or in the future).

[0043] In certain embodiments, the personalized therapy guidance that the therapy management system provides includes guidance to the host to implement a diet or lifestyle change (such as decreasing the consumption of potassium-rich foods, exercising to a point where potassium levels decrease in the host, etc.), to decrease potassium levels upon determining at least a predetermined high level of potassium for the host (either currently or in the future). In certain embodiments, the personalized therapy guidance that the therapy management system provides includes guidance to the host to implement a diet or lifestyle change (such as increasing the consumption of potassium-rich foods, exercising to a point where potassium levels increase in the host, etc.), to increase potassium levels upon determining at least a predetermined low level of potassium for the host (either currently or in the future).

[0044] This guidance, when followed by the host, can improve a level of potassium within the host (either by lowering a current high level of potassium, raising a current low level of potassium, or avoiding a predicted high or low level of potassium), which can in turn improve an overall health of the host. In certain embodiments, providing the guidance includes the therapy management system automatically adjusting one or more medication dose parameters (time, quantity, etc.) by sending one or more instructions to a medication administration device (e.g., a medicament pump) to control potassium levels of a host. The therapy guidance is provided in real-time (i.e., as soon as the condition is detected close to the time the condition occurs and / or is sensed by the change in analyte or non-analyte data), or near real-time, or is provided retrospectively where further processing of the analyte data or non-analyte data is required or desired. In some cases, some of the guidance can be provided in real-time and additional or supplemental guidance can be provided retrospectively. In some embodiments, the real-time guidance uses real-time data as well as historical data to determine the guidance.

[0045] Also, as analyte (e.g., potassium, glucose, etc.), medication, exercise / diet, and other data is received from the host over time, the therapy management system can identify the results of earlier therapy management guidance (both for a current host as well as other hosts sharingone or more characteristics with the current host) and can continually refine future therapy management guidance for the current host and other related hosts based at least in part on these results. This can improve the accuracy of guidance generated by the system for all hosts.

[0046] In certain embodiments, in addition to utilizing a continuous potassium monitor (e.g., sensor), the therapy management system includes one or more additional analyte sensors and / or non-analyte sensors to further monitor a host’s response to therapies, such as RAASi medications and / or diuretics, which can change over time. In such embodiments, the change in the host’s response to the therapies can inform therapy management guidance and / or other determinations provided by the therapy management system.

[0047] In certain embodiments, the therapy management system determines a rate of change of potassium levels for a host. In such embodiments, the therapy management system can determine that the potassium levels of the host are rapidly increasing or decreasing, or gradually increasing or decreasing.

[0048] Rapid increases or decreases in potassium levels can indicate the presence of a disease state that requires medical intervention, and in most cases, immediate medical intervention. Examples disease states include kidney disease, liver disease, acute kidney injury, or other kidney or liver dysfunction, heart failure, cancer, cancer therapy, rhabdomyolysis, and / or severe hemolytic anemia. Accordingly, where the therapy management system determines that the potassium levels of the host are rapidly increasing or decreasing, the therapy management system can provide a notification or guidance to the host to seek immediate medical intervention.

[0049] Rapid changes (increases or decreases) in potassium levels can also be caused by insulin administration or presence of high levels of insulin otherwise, since insulin can cause rapid shifts in potassium within 10-20 minutes of administration, and such elevated or decreased potassium levels can last up to about 4 to 6 hours, or more. The therapy management system can first determine the root cause of the rapid change, and can then provide therapy management recommendations according to the root cause. For example, rapid changes in potassium due to a severe medical condition may require seeking immediate medical intervention, whereas changes due to insulin administration or other changes that will naturally reverse may require further monitoring using the therapy management system before they are alerted (to the host, a caretaker of the host, etc.) as needing immediate medical attention. Whenconditions are detected, the therapy management system can, in the meantime, administer or recommend additional medications to deal with the changes in potassium before medical intervention is needed, or in some cases, until medical intervention is received in the cases of severe conditions.

[0050] Gradual changes (increases or decreases) in potassium levels over time can similarly indicate worsening renal function, heart failure, or liver failure. However, gradual changes can also indicate a host’s response to an incorrect (e.g., overly high or ineffectively low) dose of medication (e.g., RAASi medication or diuretics). The therapy management system can determine the root cause of the gradual change in potassium levels using the analyte data and / or non-analyte data of the host and determine, based on patterns and levels, what caused the change in potassium rate of change. If it is determined that the host’s potassium response is caused by medication dosage, the therapy management system can determine an optimal medication response and provide therapy management guidance to the host to correct the medication response. Such guidance can include guidance to adjust a dose, begin taking different or additional medications, and / or implement diet or lifestyle changes.

[0051] In certain embodiments, the therapy management system wirelessly communicates with a medication delivery device (e.g., a pump, pen, or another dosing mechanism) to automatically adjust an administration of one or more medications (e.g., dose, medication type, timing, etc.) based on therapy management guidance.

[0052] After the therapy recommendations are administered (either automatically or by the host, or a combination thereof), in some embodiments, the therapy management system further monitors the host data, including the potassium, other analyte, and / or non-analyte data. The therapy management system then determines an updated disease state for the host. The therapy management system, in certain embodiments, uses the monitoring to determine an adherence to the therapy management guidance by the host, the efficacy of the treatment guidance, and / or any modifications that are needed to the therapy management guidance. Over time, therapy management guidance provided by the therapy management system is configured to improve the health of the host. For example, in some embodiments, by allowing the host to continue a medication regimen, as modified by the therapy management system where necessary, for managing the host’s disease state, while also monitoring for potassium imbalances and providing therapy management guidance to address such imbalances, the host is able to takeadvantage of the medications available for their disease state that may otherwise be underprescribed, avoided, or cause dangerous conditions.

[0053] As used herein, the term “continuous” analyte monitoring refers to monitoring one or more analytes in a fully continuous, semi-continuous, periodic manner, which results in a data stream of analyte values over time. A data stream of analyte values over time is what allows for meaningful data and insight to be derived using the algorithms described herein for monitoring a host’s potassium levels to predict and / or determine potassium imbalances as caused by RAASi medications and / or diuretics for HF patients, as well as potassium imbalances caused by therapies for other conditions and / or as caused by the conditions themselves. In other words, single point-in-time measurements collected as a result of a host visiting their health care professional every few months results in sporadic data points (e.g., that are, at best, months apart in timing) that cannot form the basis of any meaningful data or insight to be derived. As such, without the continuous analyte monitoring system of the embodiments herein, it is simply impossible to continuously monitor a host’s potassium levels to predict and / or determine potassium imbalances as caused by RAASi medications and / or diuretics for HF patients, as well as potassium imbalances caused by therapies for other conditions and / or as caused by the conditions themselves.

[0054] Further, the data stream of analyte values collected over time, with the continuous analyte monitoring system presented herein, include real-time analyte values, which allows for deriving meaningful data and insight in real-time using the systems and algorithms described herein. The derived real-time data and insight in turn allows for monitoring of a host’s potassium imbalances in real-time, managing the host’s heart disease in real-time, providing therapy management guidance based on any possible potassium imbalances in real-time, etc. Real-time analyte values herein refer to analyte values that become available and actionable within seconds or minutes of being produced as a result of at least one sensor electronics module of the continuous analyte monitoring system (1) converting sensor current(s) (i.e., analog electrical signals) generated by the continuous analyte sensor(s) into sensor count values, (2) calibrating the count values to generate at least potassium and / or other analyte concentration values using calibration techniques described herein to account for the sensitivity of the continuous analyte sensor(s), and (3) transmitting measured potassium and / or otheranalyte concentration data, including potassium and / or other analyte concentration values, to a display device via wireless connection.

[0055] For example, the at least one sensor electronics module is configured to sample the analog electrical signals at a particular sampling period (or rate), such as every 1 second (1 Hz), 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, etc., and to transmit the measured potassium and / or other analyte concentration data to a display device at a particular transmission period (or rate), which can be the same as (or longer than) the sampling period, such as every 1 minute (0.016 Hz), 5 minutes, 10 minutes, etc.

[0056] The real-time analyte data that is continuously generated by the continuous analyte monitoring system described herein, therefore, allows the therapy management system to monitor a host’s potassium imbalances in real-time, manage the host’s heart disease in realtime, provide therapy management guidance based on any possible potassium imbalances in real-time, etc., which is technically impossible to perform using existing or conventional techniques or systems. Further, because of the real-time nature of this data, it is also humanly impossible to continuously process a real-time data stream of analyte values over time to derive meaningful data and insight using the algorithms and systems described herein to monitor of a host’s potassium imbalances in real-time, manage the host’s heart disease in real-time, provide therapy management guidance based on any possible potassium imbalances in real-time. In other words, deriving meaningful data and insight from a stream of real-time data that is continuously generated, processed, calibrated, and analyzed, using the algorithms and systems described herein, is not a task that can be mentally performed. For example, executing the algorithms described in relation to FIGs.4 and 5 in real-time and on a continuous basis, which would involve using a stream of real-time data that is continuously generated by a host’s continuous analyte monitoring system and / or significantly large amount of population data (e.g., hundreds or thousands of data points for each one of thousands or millions of hosts in the host population) is not a task that can be mentally performed, especially in real-time at times.

[0057] Another advantage provided by the utilization of a continuous analyte monitoring system is that the analyte data continuously generated by such a system can be more reliable as compared to point-in-time measurements. For example, continuously generated data can be averaged out, or otherwise adjusted, retrospectively or in real-time, to account for contextual factors that may otherwise affect analyte measurements, including consumption of meals bythe host, performance of physical activity by the host, other behaviors of the host, etc. Still further, continuously generated data can facilitate the determination of, or identification of, patterns or trends in analyte levels of the host, as well as analyte baseline and threshold levels personalized to the host, which can be relied upon to provide more accurate and useful therapy management guidance as compared to arbitrary and / or population-based baselines and thresholds.

[0058] Further, certain embodiments herein are directed to a technical solution to a technical problem associated with analyte sensor systems. In particular, each analyte sensor system that is manufactured by a sensor manufacturer might perform slightly differently. As such, there might be inconsistencies between sensors and the measurements they generate once in use. Accordingly, certain embodiments herein are directed to determining the performance of an analyte sensor system during a manufacturing calibration process (in vitro), which includes quantifying certain sensor operating parameters, such as a calibration slope (also known as calibration sensitivity), a calibration baseline, etc.

[0059] Generally, calibration sensitivity refers to the amount of electrical current produced by an analyte sensor of an analyte sensor system when immersed in a predetermined amount of a measured analyte. The amount of electrical current can be expressed in units of picoAmps (pA) or counts. The amount of measured analyte can be expressed as a concentration level in units of milligrams per deciliter (mg / dL), and the calibration sensitivity can be expressed in units of pA / (mg / dL) or counts / (mg / dL). The calibration baseline refers to the amount of electrical current produced by the analyte sensor when no analyte is detected, and can be expressed in units of pA or counts.

[0060] The calibration sensitivity, calibration baseline, and other information related to the sensitivity profile for the analyte sensor system can be programmed into the sensor electronics module of the analyte sensor system during the manufacturing process, and then used to convert the analyte sensor electrical signals into measured analyte concentration levels. For example, the calibration slope (calibration sensitivity) can be used to predict an initial in vivo sensitivity (Mo) and a final in vivo sensitivity (Mf), which are programmed into the sensor electronics module and used to convert the analyte sensor electrical signals into measured analyte concentration levels.

[0061] In certain embodiments, during in vivo use, the sensor electronics module of an analyte sensor system samples the analog electrical signals produced by the analyte sensor to generate analyte sensor count values, and then determines the measured analyte concentration levels based on the analyte sensor count values, the initial in vivo sensitivity (Mo), and the final in vivo sensitivity (Mf). For example, measured analyte concentration levels can be determined using a sensitivity function M(t) that is based on the initial in vivo sensitivity (Mo) and the final in vivo sensitivity (Mf). The sensitivity function M(t) can be expressed in several different ways, such as a simple correction factor that is not dependent on elapsed time (ti) of in vivo use, a linear relationship between sensitivity and time (ti), an exponential relationship between sensitivity and time (ti), etc. Equation 1 presents one technique for determining a measured analyte concentration level (ACL) from an analyte sensor count value (count) at a time ft:ACL = count / M(ti) Eq. 1

[0062] A calibration baseline (baseline) can also be used to determine a measured analyte concentration level (ACL) from an analyte sensor count value (count) at a time ti, and Equation 2 presents one technique:ACL = (count - baseline) / M(ti) Eq. 2Example Therapy Management System Including an Example Continuous Analyte Sensor

[0063] FIG. 1 illustrates an example therapy management system 100 for (1) determining a risk or presence of a potassium imbalance for host(s) 102 (individually referred to herein as a host and collectively referred to herein as hosts) that are being treated with a medication that can affect potassium levels, and / or (2) notifying hosts(s) 102 of a risk or presence of a potassium imbalance for host(s) 102 that are being treated with medications that can affect potassium levels, and / or (3) determining a cause of a potassium imbalance for host(s) 102, and / or (4) providing personalized therapy management guidance, including adjusting therapy, to stabilize potassium levels for host(s) 102, using a continuous analyte monitoring system 104 configured to continuously measure, at least, potassium levels. A host, in certain embodiments, can be a patient or, in some cases, the patient’s caregiver. A host, in certain embodiments, can be a patient suffering from cardiovascular disease, such as heart failure (HF), or any other disease state that is treated by medication(s) that can impact potassium levels of the host.

[0064] As utilized herein, “therapy” can refer to the administration of medication for the treatment of a disease state of a host, such as the administration of a medication for treating HF, which can secondarily affect the potassium levels of a host. Accordingly, “therapy management” can refer to the management or adjustment of the administration of such medication, including a timing and dose of the medication, which can be provided to facilitate treatment of the disease state of the host while avoiding or mitigating the occurrence of potassium imbalance.

[0065] Potassium is an essential mineral constituent of the human body and is the chief cation found within the intracellular fluid of all cells. As a major intracellular cation, potassium acts to maintain the isotonicity and electrodynamic cellular function. It activates many enzymatic reactions within our body. Potassium plays an essential role in the transmission of nerve impulses, contraction of cardiac muscles, contraction of skeletal and smooth muscles, tissue synthesis, gastric secretion, and renal function. Too much (hyperkalemia), or too little (hypokalemia), potassium in the body can lead to adverse health effects. For example, abnormally high and / or low potassium levels can lead to life-threatening complications such as fatal arrhythmias or respiratory muscle paralysis. Accordingly, in the systems and methods described herein, potassium can be continuously monitored to continuously assess parameters such as potassium levels, absolute minimum and maximum potassium levels, potassium level rates of change, potassium baselines, potassium baseline rates of change, potassium clearance rates, potassium trends, etc.

[0066] In certain embodiments, therapy management system 100 includes continuous analyte monitoring system 104, including, at least, a continuous potassium monitor (CPM), a display device 107 that executes application 106, a host database 110, a historical records database 112, a training server system 140, and a therapy management engine 114, each of which is described in more detail below.

[0067] The term “analyte” as used herein is a broad term used in its ordinary sense, including, without limitation, to refer to a substance or chemical constituent in a biological fluid (for example, blood, interstitial fluid, cerebral spinal fluid, lymph fluid or urine) that can be analyzed. Analytes can include naturally occurring substances, artificial substances, metabolites, and / or reaction products. Analytes for measurement by the devices and methods can include, but may not be limited to, potassium, glucose, endogenous insulin,acarboxyprothrombin; acylcamitine; exogenous insulin; adenine phosphoribosyl transferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); androstenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; calcium, carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-P hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcohol dehydrogenase, alpha 1 -antitrypsin, glucose-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab, hepatitis B virus, HCMV, HIV-1, HTLV-1, MCAD, RNA, PKU, Plasmodium vivax, 21 -deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diptheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycines; free P-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3); fumarylacetoacetase; galactose / gal-1-phosphate; galactose- 1 -phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione perioxidase; glycocholic acid; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B / A-l, P); lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin; phytanic / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3); selenium; serum pancreatic lipase; sisomicin; somatomedin C; specific antibodies recognizing any one or more of the following: adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus, Aujeszky's disease virus, dengue virus, Dracunculus medinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, leptospira, measles / mumps / rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosoma cruzi / rangeli, vesicular stomatis virus, Wuchereria bancrofti, and yellow fever virus); specific antigens(hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine-binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A; white blood cells; and zinc protoporphyrin.

[0068] Salts, sugar, protein, fat, vitamins, and hormones (e.g., insulin) naturally occurring in blood or interstitial fluids can also constitute analytes in certain implementations. The analyte can be naturally present in the biological fluid, for example, a metabolic product, a hormone, an antigen, an antibody, and the like. Alternatively, the analyte can be introduced into the body or exogenous, for example, a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, or a drug or pharmaceutical composition, including but not limited to insulin; glucagon, ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin, Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine); depressants (barbiturates, methaqualone, tranquilizers such as Valium, Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogs of fentanyl, meperidine, amphetamines, methamphetamines, and phencyclidine, for example, Ecstasy); anabolic steroids; and nicotine. The metabolic products of drugs and pharmaceutical compositions are also contemplated analytes. Analytes such as neurochemicals and other chemicals generated within the body can also be analyzed, such as, for example, ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC), Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and 5-Hydroxyindoleacetic acid (FHIAA), and intermediaries in the Citric Acid Cycle.

[0069] While the analytes that are measured and analyzed by the devices and methods described herein include potassium, and in some cases, glucose and / or lactose, in some cases, other analytes listed above, and / or other analytes, can also be considered and measured by, for example, analyte monitoring system 104.

[0070] In certain embodiments, continuous analyte monitoring system 104 is configured to continuously measure one or more analytes. The analyte measurements (either in raw form, ora processed form that represent the measurements) can be transmitted via the analyte monitoring system 104 or the display device 107 to an electronic medical records (EMR) system (not shown in FIG. 1). An EMR system is a software platform which allows for the electronic entry, storage, and maintenance of digital medical data. An EMR system is generally used throughout hospitals and / or other caregiver facilities to document clinical information on patients over long periods. EMR systems organize and present data in ways that assist clinicians with, for example, interpreting health conditions and providing ongoing care, scheduling, billing, and follow up. Data contained in an EMR system can also be used to create reports for clinical care and / or disease management for a patient. In certain embodiments, the EMR is in communication with therapy management engine 114 (e.g., via a network) for performing the techniques described herein. In particular, as described herein, therapy management engine 114 can obtain data associated with a host, use the obtained data as input into one or more trained model(s), and output a prediction. In some cases, the EMR provides the data to therapy management engine 114 to be used as input into the one or more models. Further, in some cases, therapy management engine 114, after making a prediction, provides the output prediction to the EMR.

[0071] In certain embodiments, continuous analyte monitoring system 104 is configured to continuously measure one or more analytes and transmit the analyte measurements to display device 107 for use by application 106. In certain embodiments, continuous analyte monitoring system 104 transmits the analyte measurements to display device 107 through a wireless connection (e.g., Bluetooth connection). In certain embodiments, display device 107 is a smart phone. However, in certain other embodiments, display device 107 can instead be any other type of computing device such as a laptop computer, a smart watch, a smart band, smart glasses, a tablet, a phablet, or any other computing device capable of executing application 106. Continuous analyte monitoring system 104 is described in more detail with respect to FIG. 2.

[0072] Application 106 is a mobile health application that is configured to receive and analyze analyte measurements from analyte monitoring system 104. For example, application 106 stores information about a host, including the host’ s analyte measurements, in a host profile 118 of the host for processing and analysis as well as for use by the therapy management engine 114 to provide therapy management support recommendations or guidance to the host.

[0073] Therapy management engine 114 refers to a set of software instructions with one or more software modules, including data analysis module (DAM) 116. In certain embodiments, therapy management engine 114 executes entirely on one or more computing devices in a private or a public cloud. In such embodiments, application 106 communicates with therapy management engine 114 over a network (e.g., Internet). In certain other embodiments, therapy management engine 114 executes partially on one or more local devices, such as display device 107, and partially on one or more computing devices in a private or a public cloud. In certain other embodiments, therapy management engine 114 executes entirely on one or more local devices, such as display device 107. As discussed in more detail herein, therapy management engine 114, provides therapy management support recommendations to the host via application 106. Therapy management engine 114 provides therapy management support recommendations based on information included in host profile 118.

[0074] Host profile 118 can include information collected about the host from application 106. For example, application 106 provides a set of inputs 130, including the analyte measurements associated with one or more analytes received from continuous analyte monitoring system 104 that are stored in host profile 118. In certain embodiments, inputs 130 provided by application 106 include other data in addition to analyte measurements. For example, application 106 can obtain additional inputs 130 through manual host input, one or more other non-analyte sensors or devices, other applications executing on display device 107, etc. Non-analyte sensors and devices include one or more of, but are not limited to, an insulin pump, respiratory sensor, sensors or devices provided by display device 107 (e.g., accelerometer, camera, global positioning system (GPS), heart rate monitor, electrocardiogram (ECG), etc.) or other host accessories (e.g., a smart watch, a continuous positive airway pressure (CPAP) machine, or a fitness tracker), or any other sensors or devices that provide relevant information about the host. Inputs 130 of host profile 118 provided by application 106 are described in further detail below with respect to FIG. 3.

[0075] DAM 116 of therapy management engine 114 is configured to process the set of inputs 130 to determine one or more metrics 132. Metrics 132, discussed in more detail below with respect to FIG. 3, can, at least in some cases, be generally indicative of the health or state of a host, such as one or more of the physiological state of a host, trends associated with the health or state of a host, etc. In certain embodiments, metrics 132 can then be used by therapymanagement engine 114 as input for providing guidance to a host. As shown, metrics 132 are also stored in host profile 118.

[0076] Host profile 118 also includes demographic information 120, physiological information 122, disease information 124, and / or medication information 126. In certain embodiments, such information is provided through host input or obtained from certain data stores (e.g., electronic medical records (EMRs), etc.). In certain embodiments, demographic information 120 includes one or more of the host’s age, ethnicity, gender, etc. In certain embodiments, physiological information 122 includes one or more of the host’s height, weight, cardiovascular health, and / or body mass index (BMI). In certain embodiments, disease information 124 includes information about one or more diseases of a host, including relevant information pertaining to the host’s cardiovascular disease or congenital heart defects (CHDs), kidney disease, kidney dysfunction, and / or acute kidney injury, liver disease and / or liver dysfunction, diabetes, lung diseases, adrenal gland disorders, and / or other health conditions, syndromes, or diseases. In certain embodiments, disease information 124 also includes the length of time since diagnosis, disease progression information, the level of disease control, level of compliance with disease management therapy, other types of diagnoses (e.g., obesity, hormone imbalances, hyperkalemia or hypokalemia, etc.), and the like. In certain embodiments, disease information 124 includes hospitalizations and / or surgical history. In certain embodiments, disease information 124 includes other measures of health (e.g., heart rate, heart rhythm, blood pressure, stress, sleep, etc.) or fitness (e.g., cardiovascular endurance, metabolic state, muscular endurance, and other measures of fitness), and / or the like.

[0077] In certain embodiments, medication information 126 includes information about the amount, frequency, and / or type of a medication taken by, or prescribed to, a host. In certain embodiments, the amount, frequency, and type of a medication taken by a host is time-stamped and correlated with the host’s analyte levels, thereby, indicating the impact the amount, frequency, and type of the medication had on the host’s analyte levels. In certain embodiments, medication information 126 includes information about the consumption of one or more drugs, including renin-angiotensin-aldosterone system inhibitors (RAASi), mineral corticoid receptor antagonists (MRAs), angiotensin-converting enzyme inhibitors (ACE inhibitors), angiotensinreceptor blockers (ARBs), other heart drugs such as amiodarone and clopidogrel, diuretics (which may be used to treat excessive fluid accumulation caused by, for example, HF, liverfailure, and / or nephritic syndrome) such as loop diuretics, thiazide and thiazide-like diuretics, and potassium- sparing diuretics, antibiotics such as amoxicillin / clavulanate, clindamycin, erythromycin, nitrofurantoin, rifampin, sulfonamides, tetracyclines, trimethoprim / sulfamethoxazole, vancomycin, and drugs used to treat tuberculosis (isoniazid and pyrazinamide), anticonvulsants such as tarbamazepine, thenobarbital, phenytoin, and valproate, antidepressants such as bupropion, fluoxetine, mirtazapine, paroxetine, sertraline, trazodone, and tricyclic antidepressants such as amitriptyline, antifungal drugs such as ketoconazole and terbinafine, antihypertensive drugs (e.g., drugs used to treat high blood pressure or sometimes kidney or heart disorder) such as captopril, enalapril, irbesartan, lisinopril, losartan, and verapamil, antipsychotic drugs such as phenothiazines (e.g., such as chlorpromazine) and risperidone, hormone regulation drugs such as anabolic steroids, birth control pills (oral contraceptives), and estrogens, pain relievers such as acetaminophen and nonsteroidal anti-inflammatory drugs (NSAIDs), and other drugs such as acarbose (e.g., used to treat diabetes), allopurinol (e.g., used to treat gout), antiretroviral therapy (ART) drugs (e.g., used to treat human immunodeficiency virus (HIV) infection), baclofen (e.g., a muscle relaxant), cyproheptadine (e.g., an antihistamine), azathioprine (e.g., used to prevent rejection of an organ transplant), methotrexate (e.g., used to treat cancer), omeprazole (e.g., used to treat gastroesophageal reflux), PD-1 / PD-L1 inhibitors (e.g., anticancer drugs), statins (e.g., used to treat high cholesterol levels), ademetionine, avatrombopag, dehydroemetine, entecavir, glecaprevir and pibrentasvir, lamivudine, lithium, metadoxine, methionine, sofosbuvir, velpatasvir, and voxilaprevir, telbivudine, tenofovir, trientine, tacrolimus and other calcineurin inhibitors, ursodeoxycholic acid, many types of chemotherapies, including immune checkpoint inhibitors, drugs for treatment of acute hyper- and / or hypokalemia, such as insulin, dextrose, and glucose, and the like.

[0078] In certain embodiments, host profile 118 is dynamic because at least part of the information that is stored in host profile 118 can be revised or updated over time and / or new information can be added to host profile 118 by therapy management engine 114 and / or application 106. Accordingly, information in host profile 118 stored in host database 110 provides an up-to-date repository of information related to the host.

[0079] Host database 110, in certain embodiments, refers to a storage server that operates, for example, in a public or private cloud. Host database 110 is implemented as any type ofdatastore, such as relational databases, non-relational databases, key-value datastores, file systems including hierarchical file systems, and the like. In some exemplary implementations, host database 110 is distributed. For example, host database 110 can include a plurality of persistent storage devices, which are distributed. Furthermore, host database 110 can be replicated so that the storage devices are geographically dispersed.

[0080] Host database 110 includes host profiles 118 associated with a plurality of hosts, including hosts who similarly interact or have interacted in the past with application 106 on their own devices. Host profiles stored in host database 110 are accessible to not only application 106, but therapy management engine 114 as well. Host profiles in host database 110 are accessible to application 106 and / or therapy management engine 114 over one or more networks (not shown), such as one or more wireless networks. As described above, therapy management engine 114, and more specifically data analysis module (DAM) 116 of therapy management engine 114, can fetch inputs 130 from a host’s profile 118 stored in host database 110 and compute one or more metrics 132 which can then be stored as application data 128 in the host’s profile 118.

[0081] In certain embodiments, host profiles 118 stored in host database 110 is also stored in historical records database 112. Host profiles 118 stored in historical records database 112 can provide a repository of up-to-date information and historical information for each host of application 106. Thus, historical records database 112 essentially provides all data related to each host of application 106, where data is stored using timestamps. The timestamp associated with any piece of information stored in historical records database 112 identifies, for example, when the piece of information was obtained and / or updated.

[0082] Further, historical records database 112 can include data collected for one or more hosts over a period of time, includes hosts who are hosts of continuous analyte monitoring system 104 and / or application 106, as well as hosts who are not hosts of continuous analyte monitoring system 104 and / or application 106. For example, historical records database 112 can include information (e.g., user profile(s)) related to one or more hosts analyzed by, for example, a healthcare physician (or other known method), and not previously diagnosed with heart failure, kidney disease, and / or other indications, as well as information (e.g., user profile(s)) related to one or more hosts who were analyzed by, for example, a healthcarephysician (or other known method) and were previously diagnosed with (varying types and stages of) heart failure, kidney disease, and / or other indications.

[0083] Data stored in historical records database 112 is referred to herein as population data, which could include hundreds or thousands of data points for each one of thousands or millions of hosts in the host population. In other words, data stored in historical records database 112 and used in certain embodiments described herein could include gigabytes, terabytes, petabytes, exabytes, etc. of data.

[0084] Data related to each host stored in historical records database 112 can provide time series data collected over the lifetime of the host, a period of the lifetime of the host, and / or a disease lifetime of the host. For example, the data can include information about the host prior to being diagnosed with HF, kidney disease, and / or other indications, and information associated with the host during the lifetime of the disease, including information related to each stage of the disease as it progressed and / or regressed in the host, as well as information related to other diseases, such as hyperkalemia, hypokalemia, diabetes, or similar diseases that are co-morbid in relation thereto. The data can also include physiological information (e.g., height and weight), as well as non-analyte sensor data (e.g., heart rate, respiratory rate, etc.). Such data can indicate physiological states of the host, potassium levels of the host, glucose levels of the host, lactate levels of the host, insulin levels of host, other hormone levels of the host, states / conditions of one or more organs of the host, habits of the host (e.g., activity levels, food consumption, etc.), medication prescribed throughout the lifetime of the disease, as well as progress of outcomes such as weight loss and cardiovascular health over time, etc.

[0085] Although depicted as separate databases for conceptual clarity, in certain embodiments, host database 110 and historical records database 112 operate as a single database. That is, historical and current data related to hosts of continuous analyte monitoring system 104 and application 106, as well as historical data related to hosts that were not previously hosts of continuous analyte monitoring system 104 and application 106 and / or application 111, is stored in a single database. The single database can be a storage server that operates in a public or private cloud.

[0086] As mentioned previously, therapy management system 100 is configured to determine a risk or presence of a potassium imbalance, as well as provide therapy management guidance to maintain or stabilize potassium levels, for host(s) 102 that are being treated withmedications that can affect potassium levels, using continuous analyte monitoring system 104, including, at least, a continuous potassium sensor. In certain embodiments, therapy management engine 114 is configured to (1) provide real-time and or non-real-time therapy management guidance (e.g., guidance) to the host and or others, including but not limited, to healthcare providers, family members of the host, caregivers of the host, etc., and / or (2) provide real-time instructions to an automated medication delivery device for automatically adjusting medication administration, including dose and timing parameters, for the host. Therapy management support is intended to provide optimal medication administration guidance to facilitate potassium homeostasis and treat the indication (e.g., prevent development and / or progression of the indicated disease state) of the administered medication (e.g., HF or other conditions).

[0087] In particular, therapy management engine 114 is used to collect information associated with a host in host profile 118 stored in host database 110, to perform analytics thereon to determine a risk or presence of potassium imbalance of the host. Based on the determination, the therapy management engine 114 optimizes therapy for a disease state of the host while also facilitating potassium homeostasis for the host. Optimizing therapy, as described above, includes providing optimized therapy management guidance to the host and / or automatically controlling the operations of a medication pump or dialysis machine, such as by adjusting a flow rate, dose, etc. of the therapy, as well as the timing of the therapy. Host profile 118 is accessible to therapy management engine 114 over one or more networks (not shown) for performing such analytics.

[0088] In certain embodiments, therapy management engine 114 utilizes one or more rulebased algorithms or trained machine learning models capable of performing analytics on information that therapy management engine 114 has collected / received from host profile 118. In the illustrated embodiment of FIG. 1, therapy management engine 114 can utilize trained machine learning model(s) provided by a training server system 140. Although depicted as a separate server for conceptual clarity, in certain embodiments, training server system 140 and therapy management engine 114 operates as a single server or system. That is, the model can be trained and used by a single server, or can be trained by one or more servers and deployed for use on one or more other servers or systems. In certain embodiments, the model is trainedon one or many virtual machines (VMs) running, at least partially, on one or many physical services in relational and or non-relational database formats.

[0089] Training server system 140 is configured to train the machine learning model(s) using training data, which can include data (e.g., from host profiles) associated one or more hosts (e.g., hosts or non-hosts of continuous analyte monitoring system 104 and / or application 106) previously diagnosed with, for example, varying stages of cardiovascular (or other) disease, as well as hosts not previously diagnosed with cardiovascular (or other) disease. The training data can be stored in historical records database 112 and can be accessible to training server system 140 over one or more networks (not shown) for training the machine learning model(s).

[0090] The training data refers to a dataset that has been featurized and labeled. For example, the dataset can include a plurality of data records, each including information corresponding to a different host profile stored in host database 110, where each data record is featurized and labeled. In machine learning and pattern recognition, a feature is an individual measurable property or characteristic. Generally, the features that best characterize the patterns in the data are selected to create predictive machine learning models. Data labeling is the process of adding one or more meaningful and informative labels to provide context to the data for learning by the machine learning model.

[0091] As an illustrative example, each relevant characteristic of a host, which is reflected in a corresponding data record, can be a feature used in training the machine learning model.

[0092] Such features can include demographic information (e.g., age, gender, ethnicity, etc.), analyte information (e.g., potassium levels (e.g., potassium baseline, potassium threshold, potassium clearance rate, and / or potassium rate of change during and after administration of a medication, etc.)), non-analyte sensor information (e.g., heart rate, temperature, etc.), cardiovascular health information (e.g., cardiovascular disease diagnosis (e.g., HF) and staging), comorbidities (e.g., kidney disease), and / or any other information relevant to optimizing therapy for the hosts. In addition, the data record is labeled with information the corresponding model is being trained to predict. In one example, if a model is being trained to output optimized therapy parameters, then the data records in the training dataset are labeled with one or more of such parameters. Note that, in one example, such a model can be a multiinput single-output (MISO) model, configured to predict only one optimized therapyparameters (e.g., dosage), in which case additional MISO models can be trained for each predicting one of other therapy parameters (e.g., timing, etc.). In another example, such a model can be a multi-input multi-output (MIMO) model, configured to predict multiple optimized therapy parameters (e.g., dosage and time, etc.).

[0093] The model(s) are then trained by training server system 140 using the featurized and labeled training data. In particular, the features of each data record can be used as input into the machine learning model(s), and the generated output can be compared to label(s) associated with the corresponding data record. The model(s) compute a loss based on the difference between the generated output and the provided label(s). This loss is then used to modify the internal parameters or weights of the model. By iteratively processing each data record corresponding to each historical host, the model(s) can be iteratively refined to generate accurate determinations of a host’s risk or presence of potassium imbalance, optimized therapy parameters to treat a disease state of the host and simultaneously stabilize or maintain potassium levels, etc.

[0094] As illustrated in FIG. 1, training server system 140 deploys these trained model(s) to therapy management engine 114 for use during runtime. For example, therapy management engine 114 can obtain host profile 118 associated with a host and stored in host database 110, use information in host profile 118 as input into the trained model(s), and output a determination indicative of the host’s risk or presence of potassium imbalance, and / or suggested therapy parameters to stabilize or maintain potassium levels while treating a disease state of the host (e.g., shown as output 144 in FIG. 1). Output 144 generated by therapy management engine 114 can also indicate improvement in the host’s potassium levels, or general health, over time. Output 144 can be provided to the host (e.g., through application 106), to a caretaker of the host (e.g., a parent, a relative, a guardian, a teacher, a physical therapist, a fitness trainer, a nurse, etc.), to a physician or healthcare provider of the host, or any other individual that has an interest in the wellbeing of the host for purposes of improving the health of the host, such as, in some cases by effectuating recommended therapy. Output 144 generated by therapy management engine 114 is stored in host database 110 and is utilized to train or re-train the trained model(s).

[0095] In certain embodiments, output 144 generated by therapy management engine 114 is stored in host profile 118. Output 144 stored in host profile 118 is continuously updated bytherapy management engine 114. Accordingly, for example, previous potassium imbalance determinations and / or suggested therapy parameters, originally stored as outputs 144 in host profile 118 in host database 110 and then passed to historical records database 112, can provide an indication of the progression of the physiological status (e.g., disease state) of a host over time, as well as provide an indication as to the effectiveness of different recommendations to achieve or maintain potassium homeostasis.

[0096] In certain embodiments, a host’s own historical data is used by training server system 140 to train a personalized model for the host that provides therapy management support and insight around the host’s disease or potassium levels. For example, in certain embodiments, a model trained based on population data is used to provide optimized therapy parameters to the host. However, after collecting personalized information (e.g., analyte sensor information, non-analyte sensor information, etc.) associated with the host during one or more administrations of therapy, the personalized information can be used for further personalizing the model. For example, information obtained during a prior administration of therapy for the host can be used to optimize therapy parameters for future administrations of such therapy.

[0097] In certain embodiments, a model is trained to provide food, lifestyle, and other types of therapy management support recommendations to help the host achieve or maintain potassium homeostasis based on the host’s historical data, including how different types of food and / or activities impacted the host’s potassium levels in the past. In certain embodiments, a model is trained to predict the underlying cause of certain improvements or deteriorations in the host’s potassium levels. For example, application 106 can display a user interface with a graph that shows the host’s potassium levels with trend lines and indicate, e.g., retrospectively, how potassium levels were affected at certain points in time.

[0098] FIG. 2 is a diagram 200 conceptually illustrating an example continuous analyte monitoring system 104 including example continuous analyte sensor(s) with sensor electronics, in accordance with certain aspects of the present disclosure. For example, system 104 can be configured to continuously monitor one or more analytes of a host, in accordance with certain aspects of the present disclosure.

[0099] Continuous analyte monitoring system 104 in the illustrated embodiment includes sensor electronics module 204 and one or more continuous analyte sensor(s) 202 (individually referred to herein as continuous analyte sensor 202 and collectively referred to herein ascontinuous analyte sensors 202) associated with sensor electronics module 204. Sensor electronics module 204 is in wired or wireless communication (e.g., directly or indirectly) with one or more of display devices 210, 220, 230, and 240. In certain embodiments, sensor electronics module 204 is also in wired or wireless communication (e.g., directly or indirectly) with one or more medical devices, such as medical devices 208 (individually referred to herein as medical device 208 and collectively referred to herein as medical devices 208), and / or one or more other non-analyte sensors 206 (individually referred to herein as non-analyte sensor 206 and collectively referred to herein as non-analyte sensor 206).

[0100] In certain embodiments, a continuous analyte sensor 202 includes one or more sensors for detecting and / or measuring analyte(s). The continuous analyte sensor 202 can be a multi-analyte sensor configured to continuously measure two or more analytes, a collection of multi-analyte or single- analyte sensors in communication with one another or a central application, or an individual single-analyte sensor configured to continuously measure a single analyte as a non-invasive device, a subcutaneous device, a transcutaneous device, a transdermal device, and / or an intravascular device. In certain embodiments, the continuous analyte sensor 202 is configured to continuously measure analyte levels of a host using one or more techniques, such as enzymatic techniques, chemical techniques, physical techniques, electrochemical techniques, spectrophotometric techniques, polarimetric techniques, calorimetric techniques, iontophoretic techniques, radiometric techniques, immunochemical techniques, and the like. The term “continuous,” as used herein, can mean fully continuous, semi-continuous, periodic, etc. In certain aspects, the continuous analyte sensor 202 provides a data stream indicative of the concentration of one or more analytes in the host. The data stream can include raw data signals, which are then converted into a calibrated and / or filtered data stream used to provide estimated analyte value(s) to the host.

[0101] In certain embodiments, the continuous analyte sensor 202 is configured to continuously measure multiple analytes in a host’s body. For example, in certain embodiments, the continuous analyte sensor 202 is a single multi-analyte sensor configured to measure potassium, lactate, glucose, calcium, creatinine, ketones (e.g., 3-beta-hydroxybutyrate, acetoacetate, acetone, etc.), glycerol, and / or free fatty acids in the host’s body.

[0102] In certain embodiments, one or more multi-analyte sensors can be used in combination with one or more single analyte sensors. As an illustrative example, a multi-analyte sensor can be configured to continuously measure potassium, glucose, and / or lactate, and can, in some cases, be used in combination with an analyte sensor configured to measure only calcium, ketones, creatinine, or another analyte. Information from each of the multianalyte sensor(s) and single analyte sensor(s) can be combined to provide therapy management support using methods described herein. In further embodiments, other non-contact and or periodic or semi-continuous, but temporally limited, measurements for physiological information are integrated into the system such as by including weight scale information or non-contact heart rate monitoring from a sensor pad under the host while in a chair or bed, through an infra-red camera detecting temperature and / or blood flow patterns of the host, and / or through a visual camera with machine vision for height, weight, or other parameter estimation without physical contact.

[0103] In certain embodiments, the continuous analyte sensor(s) 202 include a percutaneous wire that has a proximal portion coupled to the sensor electronics module 204 and a distal portion with several electrodes, such as a measurement electrode and a reference electrode. The measurement (or working) electrode can be coated, covered, treated, embedded, etc., with one or more chemical molecules that react with a particular analyte, and the reference electrode can provide a reference electrical voltage. The measurement electrode can generate an analog electrical signal, which is conveyed along a conductor that extends from the measurement electrode to the proximal portion of the percutaneous wire that is coupled to the sensor electronics module 204. After the continuous analyte monitoring system 104 has been applied to epidermis of the host, continuous analyte sensor(s) 202 penetrates the epidermis, and the distal portion extends into the dermis and / or subcutaneous tissue under epidermis. Other configurations of continuous analyte sensor(s) 202 can also be used, such as a multianalyte sensor that includes multiple measurement electrodes, each generating an analog electrical signal that represents the concentration levels of a particular analyte.

[0104] Generally, a single-analyte sensor generates an analog electrical signal that is proportional to the concentration level of a particular analyte. Similarly, each multi-analyte sensor generates multiple analog electrical signals, and each analog electrical signal is proportional to the concentration level of a particular analyte. As an illustrative example, continuous analyte sensor 202 includes a single-analyte sensor configured to measure potassium concentration levels, and another single-analyte sensor configured to measureglucose or lactate concentration levels of the host. As another illustrative example, continuous analyte sensor(s) 202 includes a single-analyte sensor configured to measure potassium concentration levels, and one or more multi-analyte sensors configured to measure glucose concentration levels, lactate concentration levels, ketone concentration levels, creatinine concentration levels, etc. As yet another illustrative example, continuous analyte sensor(s) 202 includes a multi-analyte sensor configured to measure potassium concentration levels, glucose concentration levels, lactate concentration levels, calcium concentration levels, ketone concentration levels, creatinine concentration levels, etc.

[0105] Accordingly, continuous analyte sensor(s) 202 is configured to generate at least one analog electrical signal that is proportional to the concentration level of a particular analyte, and sensor electronics module 204 is configured to convert the analog electrical signal into an analyte sensor count values, calibrate the analyte sensor count values based on the sensitivity profile of the continuous analyte sensor(s) 202 to generate measured analyte concentration levels, and transmit the measured analyte concentration level data, including the measured analyte concentration levels, to a display device, such as display devices 210, 220, and / or 230, via a wireless connection. For example, sensor electronics module 204 can be configured to sample the analog electrical signal at a particular sampling period (or rate), such as every 1 second (1 Hz), 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, 60 minutes, 4 hours, 8 hours, 12 hours, 24 hours, etc., and to transmit the measured analyte concentration data to the display device at a particular transmission period (or rate), which can be the same as (or longer than) the sampling period, such as every 1 minute (0.016 Hz), 5 minutes, 10 minutes, 30 minutes, 60 minutes, 4 hours, 8 hours, 12 hours, 24 hours, at the conclusion of the wear period, etc. Depending on the sampling and transmission periods, the measured analyte concentration data transmitted to the display device include at least one measured analyte concentration level having an associated time tag, sequence number, etc.

[0106] In certain embodiments, continuous analyte sensor(s) 202 incorporate(s) a thermocouple within, or alongside, the percutaneous wire to provide an analog temperature signal to the sensor electronics module 204, which is used to correct the analog electrical signal or the measured analyte data for temperature. In other embodiments, the thermocouple is incorporated into the sensor electronics module 204 above the adhesive pad, or, alternatively, the thermocouple contacts the epidermis of the host through openings in the adhesive pad.

[0107] In certain embodiments, the continuous analyte sensor(s) 202 include an aptamer sensor, such as an electrochemical aptamer sensor, configured to continuously measure one or more analytes in a host’s body. Aptamers are peptides or oligonucleotides with high sensitivity and selectivity for the detection of various types of analytes ranging from nucleotides, peptides, proteins, and small molecules to cells. If multiple aptamers are combined into a single continuous analyte sensor 202, the continuous analyte sensor 202 can measure large numbers of different analytes in the host’s body.

[0108] In certain embodiments, one or more aptamer sensors can be used to directly detect and / or measure an amount of medication(s) administered to a host (e.g., a medication that has entered the body of the host), or can be used to detect and / or measure an amount of protein, enzyme, or other metabolite of the medication(s) to indirectly detect and / or measure an amount of the medication(s). Accordingly, in such embodiments, the therapy management system 100 utilizes the one or more aptamer sensors to monitor a host’s medication compliance. For example, HF patients are often prescribed multiple medications for treating their disease state, and each medication can have a different administration schedule. One or more aptamer sensors can therefore enable the therapy management system 100 to monitor one or more of the prescribed medications and determine if, and when, each of the one or more medications was taken, and the dose thereof. Information gathered about the medication(s) and the host’s medical compliance can, in turn, inform therapy management guidance (e.g., a suggested timing and / or dose of medication(s) and / or a recommendation for administration of an additional medication to support potassium control) provided by the therapy management system 100.

[0109] In certain embodiments, the therapy management system 100 utilizes one or more aptamer sensors to assist in titration of one or more medications taken by a host. For example, the one or more aptamer sensors can be used to continuously monitor the effects of the medications on the host (e.g., via measuring one or more levels of the medication itself or a metabolite of the medication). Based on the monitored effects, the therapy management system 100 can provide real-time recommendations to the host and / or a healthcare provider to up-titrate or down-titrate one or more medications to reach a target, or optimal (most effective), medication dose for the host. The recommendations can be based on a measured absolute concentration of a medication detected in a host. In certain embodiments, the measuredconcentration of the medication can be compared to one or more threshold concentrations to identify a risk or presence of potential adverse events associated with threshold concentrations based on historical population data. If the measured concentration of the medication is determined to be above or below a threshold concentration of the one or more threshold concentrations, the host can be directed to up-titrate or down-titrate their medication dose as appropriate to avoid the potential adverse effects. In certain embodiments, one or more aptamer sensors can monitor a relative change in medication concentration relative to a target metric. The target metric can be based on a desired change in medication concentration when the measured concentration of the medication is associated, or mapped, with one or more negative symptoms (e.g., shortness of breath, potassium imbalance, etc.).

[0110] In certain embodiments, the therapy management system 100 utilizes one or more aptamer sensors, in combination with other analyte and / or non-analyte sensors as described above, to prevent hospital readmissions of a host. For example, upon discharge from a hospital, the host can be provided a sensor, such as an aptamer sensor, to monitor medication adherence of the host and the efficacy of the prescribed dosage. The therapy management system 100 or a healthcare provider can then monitor the host’s medication adherence, as well as other analyte and / or non-analyte data, to then identify and predict potential adverse events, and recommend therapy management guidance to the host to prevent the adverse health events from occurring without requiring hospital readmission.

[0111] In certain embodiments, the sensor electronics module 204 includes, inter alia, processor 233, storage element or memory 234, wireless transmitter / receiver (transceiver) 236, one or more antennas coupled to wireless transceiver 236, analog electrical signal processing circuitry, analog to-digital (A / D) signal processing circuitry, digital signal processing circuitry, a power source for continuous analyte sensor(s) 202 (such as a potentiostat), etc.

[0112] Processor 233 can be a general-purpose or application-specific microprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc., that executes instructions to perform control, computation, input / output, etc. functions for the sensor electronics module 204. Processor 233 can include a single integrated circuit, such as a micro processing device, or multiple integrated circuit devices and / or circuit boards working in cooperation to accomplish the appropriate functionality. In certain embodiments, processor233, memory 234, wireless transceiver 236, the A / D signal processing circuitry, and the digital signal processing circuitry are combined into a system-on-chip (SoC).

[0113] Generally, processor 233 can be configured to sample the analog electrical signal using the A / D signal processing circuitry at regular intervals (such as the sampling period) to generate analyte sensor count values based on the analog electrical signals produced by the continuous analyte sensor(s) 202, calibrate the analyte sensor count values based on the sensitivity profile of the continuous analyte sensor(s) 202 to generate measured analyte concentration levels, and generate measured analyte data from the measured analyte concentration levels, generate sensor data packages that include, inter alia, the measured analyte concentration level data. Processor 233 can store the measured analyte concentration level data in memory 234, and generate the sensor data packages at regular intervals (such as the transmission period) for transmission by wireless transceiver 236 to a display device, such as display devices 210, 220, 230, and / or 240. Processor 233 can also add additional data to the sensor data packages, such as supplemental sensor information that includes a sensor identifier, a sensor status, temperatures that correspond to the measured analyte data, etc. The sensor data packages are then wirelessly transmitted over a wireless connection to the display device. In certain embodiments, the wireless connection is a Bluetooth or Bluetooth Low Energy (BLE) connection. In such embodiments, the sensor data packages are transmitted in the form of Bluetooth or BLE data packets to the display device. In some examples, processor 233 can include a processor module having multiple processors and / or processing cores, and that some or all of the functions described herein can be distributed among such processors or cores. In further examples, one or more of the described functions can alternatively be performed by other processors or processing circuitry located elsewhere in the therapy management system 100, such as within a transceiver module, memory module, or one or more components of the display device(s) 210, 220, 230, and / or 240.

[0114] In various embodiments, memory 234 includes volatile and nonvolatile medium. For example, memory 234 can include combinations of random-access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), read only memory (ROM), flash memory, cache memory, and / or any other type of non-transitory computer-readable medium. Memory 234 can store one or more analyte sensor system applications, modules, instruction sets, etc.for execution by processor 233, such as instructions to generate measured analyte data from the analyte sensor count values, etc.

[0115] Memory 234 can also store certain sensor operating parameters 235, such as a calibration slope (or calibration sensitivity), a calibration baseline, etc. In particular, the calibration sensitivity, calibration baseline, and other information related to the sensitivity profile for the sensor electronics module 204 can be programmed into the sensor electronics module 204 during the manufacturing process, and then used to convert the analyte sensor electrical signals into measured analyte concentration levels. For example, as discussed above, the calibration slope can be used to predict an initial in vivo sensitivity (Mo) and a final in vivo sensitivity (Mf), which are stored in memory 234 and used to convert the analyte sensor electrical signals into measured analyte concentration levels. In certain embodiments, calibration sensitivity (Mcc) 246 and / or calibration baseline 247 are stored in memory 234.

[0116] In certain embodiments, sensor electronics module 204 includes electronic circuitry associated with measuring and processing the continuous analyte sensor data, including prospective algorithms associated with processing and calibration of the sensor data. Sensor electronics module 204 can be physically connected to continuous analyte sensor(s) 202 and can be integral with (non-releasably attached to) or releasably attachable to continuous analyte sensor(s) 202. Sensor electronics module 204 can include hardware, firmware, and / or software that enable measurement of levels of analyte(s) via continuous analyte sensor(s) 202. For example, sensor electronics module 204 can include a potentiostat, a power source for providing power to the sensor, other components useful for signal processing and data storage, and a telemetry module for transmitting data from the sensor electronics module to, e.g., one or more display devices. Electronics can be affixed to a printed circuit board (PCB), or the like, and can take a variety of forms. For example, the electronics can take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, and / or a processor.

[0117] Display devices 210, 220, 230, and / or 240 are configured for displaying displayable sensor data, including analyte data, which can be transmitted by sensor electronics module 204. Each of display devices 210, 220, 230, or 240 includes a display such as a touchscreen display 212, 222, 232, and / or 242 for displaying sensor data to a host and / or for receiving inputs from the host. For example, a graphical user interface (GUI) can be presented to the host for suchpurposes. In certain embodiments, the display devices include other types of user interfaces such as a voice user interface instead of, or in addition to, a touchscreen display for communicating sensor data to the host of the display device and / or for receiving host inputs. Display devices 210, 220, 230, and 240 can be examples of display device 107 illustrated in FIG. 1 used to display sensor data to a host of the system of FIG. 1 and / or to receive input from the host.

[0118] In certain embodiments, one, some, or all of the display devices are configured to display or otherwise communicate (e.g., verbalize) the sensor data as it is communicated from the sensor electronics module (e.g., in a customized data package that is transmitted to display devices based on their respective preferences), without any additional prospective processing required for calibration and real-time display of the sensor data.

[0119] In certain embodiments, the display devices 210, 220, 230, and / or 240 are configured to display received sensor data at a particular display rate, such as every 1 second, 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, 60 minutes, 4 hours, 8 hours, 12 hours, 24 hours, etc. In certain embodiments, the display rate of the display devices 210, 220, 230, and / or 240 is less, substantially equal to, or longer than the sampling period (or rate) and / or transmission period (or rate) of the sensor electronics module 204. Generally, the display devices 210, 220, 230, and / or 240 can display individual sensor measurements, measurement trends, and / or aggregate measurement statistics such as hourly, daily, weekly, and monthly aggregate values and deviations from trends.

[0120] The plurality of display devices can include a custom display device specially designed for displaying certain types of displayable sensor data associated with analyte data received from sensor electronics module. In certain embodiments, the plurality of display devices are configured for providing alerts / alarms based on the displayable sensor data. Display device 210 is an example of such a custom device. In certain embodiments, one of the plurality of display devices is a smartphone, such as display device 220 which represents a mobile phone, using a commercially available operating system (OS), and configured to display a graphical representation of the continuous sensor data (e.g., including current and historic data). Other display devices can include other hand-held devices, such as display device 230 which represents a tablet or phablet, display device 240 which represents a smartwatch or fitness tracker, medical device 208 (e.g., a medication administration device or a blood glucose meter), and / or a desktop or laptop computer (not shown).

[0121] Because different display devices provide different user interfaces, content of the data packages (e.g., amount, format, and / or type of data to be displayed, alarms, and the like) can be customized (e.g., programmed differently by the manufacture and / or by an end host) for each particular display device. Accordingly, in certain embodiments, a plurality of different display devices can be in direct wireless communication with a sensor electronics module (e.g., such as an on-skin sensor electronics module 204 that is physically connected to continuous analyte sensor(s) 202) during a sensor session to enable a plurality of different types and / or levels of display and / or functionality associated with the display able sensor data.

[0122] As mentioned, sensor electronics module 204 can be in communication with a medical device 208. Medical device 208 is a passive device in some example embodiments of the disclosure. For example, medical device 208 can be a medicament (i.e., medication) pump for administering one or more medications to a host, such as one or more medications for treating HF or CKD. For a variety of reasons, it can be desirable for such a medication pump to receive and track potassium, lactate, glucose, calcium, creatinine, and / or other analytes transmitted from continuous analyte monitoring systems 104, where continuous analyte sensor 202 is configured to measure potassium, lactate, glucose, calcium, creatinine, and / or other analytes. In certain embodiments, medical device 208 includes an insulin pump.

[0123] Further, as mentioned, sensor electronics module 204 can also be in communication with other non-analyte sensors 206. Non-analyte sensors 206 can include, but are not limited to, an altimeter sensor, an accelerometer sensor, a global positioning system (GPS) sensor, a temperature sensor, a respiration rate sensor, etc. Non-analyte sensors 206 can also include monitors such as heart rate monitors, blood pressure monitors, pulse oximeters, cardiovascular implantable electronic devices (CIEDs) such as implantable cardioverter defibrillators (ICDs), pacemakers (PMs), cardiac resynchronization therapy (CRT) devices, implantable loop recorders (ILRs), and implantable hemodynamic monitors (IHMs), caloric intake monitors, indirect calorimetry devices and medicament administration / delivery devices. One or more of these non-analyte sensors 206 provide data to therapy management engine 114 described further below. In some aspects, a host manually provides some of the data for processing by training server system 140 and / or therapy management engine 114 of FIG. 1.

[0124] In certain embodiments, non-analyte sensors 206 further include sensors for measuring skin temperature, core temperature, sweat rate, and / or sweat composition.

[0125] In certain embodiments, the non-analyte sensors 206 can be combined in any other configuration, such as, for example, combined with one or more continuous analyte sensors 202. As an illustrative example, a non-analyte sensor, e.g., a temperature sensor, can be combined with a continuous potassium sensor 202 to form a potassium / temperature sensor used to transmit sensor data to the sensor electronics module 204 using common communication circuitry. As another illustrative example, a non-analyte sensor, e.g., a temperature sensor, can be combined with a multi-analyte sensor 202 configured to measure potassium, glucose, and / or lactate to form a potassium / glucose / lactate / temperature sensor used to transmit sensor data to the sensor electronics module 204 using common communication circuitry.

[0126] In certain embodiments, a wireless access point (WAP) is used to couple one or more of continuous analyte monitoring system 104, the plurality of display devices, medical device(s) 208, and / or non-analyte sensor(s) 206 to one another. For example, WAP 138 can provide Wi-Fi and / or cellular connectivity among these devices. Near Field Communication (NFC) and / or Bluetooth can also be used among devices depicted in diagram 200 of FIG. 2.

[0127] FIG. 3 illustrates example inputs and example metrics that are calculated based on the inputs for use by the therapy management system of FIG. 1, according to some embodiments disclosed herein. In particular, FIG. 3 provides a more detailed illustration of example inputs and example metrics introduced in FIG. 1.

[0128] FIG. 3 illustrates example inputs 130 on the left, application 106 and DAM 116 in the middle, and metrics 132 on the right. In certain embodiments, each one of metrics 132 corresponds to one or more values, e.g., discrete numerical values, ranges, or qualitative values (high / medium / low, stable / unstable, etc.). Application 106 obtains inputs 130 through one or more channels (e.g., manual host input, sensors, other applications executing on display device 107, an EMR system, etc.). As mentioned previously, in certain embodiments, inputs 130 are processed by DAM 116 to output a plurality of metrics, such as metrics 132. Inputs 130 and metrics 132 can be used by training server system 140 and therapy management engine 114 to both train and deploy one or more machine learning models for determining a risk or presence of a potassium imbalance, providing therapy management guidance or treatment to maintainor stabilize potassium levels for hosts 102 being treated with medications that can affect potassium levels, and other functionalities described herein.

[0129] In certain embodiments, starting with inputs 130, host statistics, such as one or more of age, height, weight, BMI, body composition (e.g., % body fat or % muscle from a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, dual-energy X-ray absorptiometry (DEXA) scan, etc.), stature, build, or other information are also provided as an input. In certain embodiments, host statistics are provided through a user interface, by interfacing with an electronic source such as an electronic medical record, and / or from measurement devices. In certain embodiments, the measurement devices include one or more wireless devices, e.g., Bluetooth-enabled, weight scale and / or camera, which can, for example, communicate with the display device 107 to provide host data.

[0130] In certain embodiments, medication / treatment information is also provided as an input. Medication information can include information about the type, dose, and / or timing of when one or more medications are to be taken by the host. As mentioned elsewhere herein, the medication information can include information about one or more medications prescribed to the host for treating one or more symptoms of cardiovascular disease (e.g., HF), kidney disease, diabetes, and / or other conditions, and that have a known effect on potassium levels of the host. In certain embodiments, medication information includes information about administration of insulin, dextrose, and / or glucose, and / or other drugs prescribed for treatment of acute hyper-and / or hypokalemia or other acute conditions. Treatment information can further include information regarding different lifestyle habits, surgical procedures, dialysis, and / or other invasive or non-invasive procedures recommended by the host’s physician. For example, the host’s physician can recommend a user increase / decrease their potassium intake, or exercise for a minimum of thirty minutes a day, to reduce hyper- and / or hypokalemic episodes, etc. In certain embodiments, medication / treatment information is provided through manual host input.

[0131] In certain embodiments, analyte sensor data is also provided as input, for example, through continuous analyte monitoring system 104. In certain embodiments, analyte sensor data includes potassium data (e.g., a host’s potassium values) measured by at least a potassium sensor (or multi-analyte sensor) in continuous analyte monitoring system 104. In certain embodiments, analyte sensor data includes glucose data measured by at least a glucose sensor (or multi-analyte sensor) in continuous analyte monitoring system 104. For hosts undergoingintensive insulin therapy, glucose data can be used as a predictor of how and when the host would be dosing insulin, and such data can then also be used as an input 130 to forecast potassium metrics. In certain embodiments, analyte sensor data includes lactate data (e.g., a host’s lactate values) measured by at least a lactate sensor (or multi-analyte sensor) in continuous analyte monitoring system 104. In certain embodiments, analyte sensor data includes other analyte data, such as calcium data, creatinine data, BUN data, ammonia data, C-peptide data, or cystatin C-data, measured by a sensor (or multi-analyte sensor) in continuous analyte monitoring system 104.

[0132] In certain embodiments, input is also received from one or more non-analyte sensors, such as non-analyte sensors 206 described with respect to FIG. 2. Input from such non-analyte sensors 206 can include information related to heart rate, heart rate variability, electrocardiogram (ECG) data, respiration rate, oxygen saturation, blood pressure, blood volume, blood volume / host weight, accelerometer data, urine output, or a body temperature (e.g., to detect illness, physical activity, etc.) of a host. In certain embodiments, electromagnetic sensors also detect low-power radio frequency (RF) fields emitted from objects or tools touching or near the object, which provide information about host activity or location.

[0133] In certain embodiments, input received from non-analyte sensors includes input relating to a user’s medication administration / delivery. In particular, input related to the user’s medication administration can be received, via a wireless connection on a smart pen, via user input, and / or from a medication pump or other device. Medication administration information can include one or more of medication volume, time of delivery, etc. Other parameters, such as medication action time or duration of medication action, can also be received as inputs.

[0134] In certain embodiments, input received from non-analyte sensors includes input gathered from squeeze test measurements. Generally, squeeze test measurements can be utilized as inputs to determine whether a host is experiencing muscle weakness, which can be a side effect of hyper- or hypokalemia. A squeeze test measures the amount of force a host is able to exert, and therefore, measures the host’s muscle strength. If the host exhibits a decline in squeeze force during a squeeze test, muscle weakness of the host can be inferred, which can be a result of potassium imbalances.

[0135] In certain embodiments, input received from non-analyte sensors also includes input gathered from sensory neurological tests, such as input related to a host’s response to hot stimuli, cold stimuli, mechanical stimuli, and / or vibrations.

[0136] In certain embodiments, input received from non-analyte sensors also includes input gathered from reflex tests. For example, delayed responses in reflex tests can be indicative of a host experiencing hyperkalemia.

[0137] In certain embodiments, inputs 130 also includes food consumption information, including information about one or more of meals, snacks, and / or beverages, such as one or more of the size, content (carbohydrate, fat, protein, etc.), sequence of consumption, and time of consumption. In certain embodiments, food consumption is provided by a host through manual entry, by providing a photograph through an application that is configured to recognize food types and quantities, and / or by scanning a bar code or menu. In various examples, meal size can be manually entered as one or more of calories, quantity (“three cookies”), menu items (“Royale with Cheese”), and / or food exchanges (1 fruit, 1 dairy). In some examples, meal information can be received via a convenient user interface provided by application 106 and / or application 111.

[0138] In certain embodiments, food consumption information (the type of food (e.g., liquid or solid, snack or meal, etc.) and / or the composition of the food (e.g., carbohydrate, fat, protein, etc.) is determined automatically based on information provided by one or more sensors. Some example sensors include body sound sensors (e.g., abdominal sounds can be used to detect the types of meal, e.g., liquid / solid food, snack / meal, etc.), radio-frequency sensors, cameras, hyperspectral cameras, and / or analyte (e.g., potassium, insulin, glucose, lactate, calcium, creatinine, etc.) sensors to determine the type and / or composition of the food.

[0139] In certain embodiments, food consumption entered by a user relates to potassium consumed by the user. Potassium for consumption can include any natural or designed food or beverage that contains potassium, such as apricot juice, a banana, or potatoes, for example.

[0140] In certain embodiments, medical history and / or disease diagnoses (e.g., cardiovascular disease, kidney disease, diabetes, liver disease, hypertension, etc.) is provided as an input. For example, the host can have an existing diagnosis of HF and this diagnosis can be provided through manual host input. In certain embodiments, disease diagnoses are also provided by interfacing with an electronic source such as an EMR.

[0141] In certain embodiments, exercise / activity information is also provided as an input. Exercise information can include any information surrounding activities requiring physical exertion by the host. For example, exercise information can range from information related to low intensity (e.g., walking a few steps) and high intensity (e.g., five-mile run) physical exertion. In certain embodiments, the exercise information includes information related to HIIT, resistance training, or Zone 2 training. In certain embodiments, exercise information is also provided through manual host input suggesting the host will begin a specific exercise type and / or with certain exercise parameters. In certain embodiments, exercise information is provided or determined based on information provided, for example, by non-analyte sensors 206 (e.g., a temperature sensor, a heart rate monitor, a wearable blood pressure monitor, an accelerometer sensor on a wearable device such as a watch, fitness tracker, and / or patch, etc.). In certain embodiments, exercise information is provided or determined based on information provided, for example, by continuous analyte sensor system 104 (e.g., it can be deduced that the host engaged in exercise based on their potassium, glucose, lactate, and / or ketone data). The exercise information provided by analyte and non-analyte sensors can be used as input into a model trained for predicting whether the host is engaging in exercise and / or predicting the types and / or parameters of such exercise.

[0142] In certain embodiments, environmental information is provided as an input. Environmental information can include weather and / or environmental temperature (indoor and / or outdoor) information, as such environmental factors can influence metabolic activity and cause metabolic variability of the host.

[0143] In certain embodiments, date / time information is also provided as an input. Date and / or time information can be processed by DAM 116 independently of other inputs 130, or can be dependent upon (e.g., associated with) another input 130. Time information can include time of day or time from a real-time clock. For example, in certain embodiments, input analyte data is timestamped to indicate a date and time when the analyte measurement was taken for the host.

[0144] In certain embodiments, date / time information is also provided as an input. Date and / or time information can be processed by DAM 116 independently of other inputs 130, or can be dependent upon (e.g., associated with) another input 130. Time can include time of dayor time from a real-time clock. For example, in certain embodiments, input analyte data is timestamped to indicate a date and time when the analyte measurement was taken for the host.

[0145] Host input of any of the above-mentioned inputs 130 can be provided through continuous analyte sensor system 104, non-analyte sensors 206, and / or a user interface, such a user interface of display device 107 of FIG. 1. As described above, in certain embodiments, DAM 116 determines or computes the host’s metrics 132 based on inputs 130. An example list of metrics 132 is shown in FIG. 3.

[0146] In certain embodiments, potassium metrics are calculated by DAM 116 based on inputs 130. Potassium metrics can include potassium levels, potassium baselines, maximum and minimum potassium levels, potassium rates of change, potassium baseline rates of change, potassium clearance rates, and / or potassium trends.

[0147] In certain embodiments, potassium levels are determined from sensor data (e.g., potassium measurements obtained from a CPM of continuous analyte monitoring system 104, sweat sensor configured to measure potassium in sweat, where the sweat sensor is one of non-analyte sensor(s) 206). For example, potassium levels refer to time-stamped potassium measurements or values that are continuously generated and stored over time.

[0148] In certain embodiments, a potassium baseline is determined from sensor data (e.g., potassium measurements obtained from a continuous potassium sensor of continuous analyte monitoring system 104). A potassium baseline represents a host’s normal potassium levels during periods where significant fluctuations in potassium production are typically not expected. A host’s potassium baseline is generally expected to remain constant or within a narrow “normal range” over time, unless challenged through an action such as by the consumption of potassium or potassium rich foods (e.g., diet), performance of exercise, sweating, or administration of a medicament (e.g., insulin), or changed as a result of declining kidney health or kidney dysfunction. Generally, increasing fluctuation from the host’s potassium baseline indicates a loss of potassium regulation, which can put the host at an increased risk of hypo- or hyperkalemia.

[0149] Further, each host can have a different potassium baseline. In certain embodiments, a host’s potassium baseline is determined by calculating an average of potassium levels of the user over a specified amount of time where significant fluctuations are not expected. For example, the baseline potassium for a host can be determined over a period of time when thehost is sleeping, sitting in a chair, or other periods of time where the host is sedentary and not consuming food or medication which would reduce or increase potassium levels (e.g., where no external conditions exist that would affect the potassium baseline exist). In certain embodiments, DAM 116 continuously calculates a potassium baseline, time-stamps the calculated potassium baseline, and stores the corresponding information in the host’s profile 118.

[0150] In certain embodiments, an absolute maximum potassium level is determined from sensor data (e.g., potassium measurements obtained from a CPM of continuous analyte monitoring system 104), health / sickness metrics (e.g., described in more detail below), and / or disease stage metrics (e.g., described in more detail below). The absolute maximum potassium level represents a host’s maximum potassium level determined to be safe over a period of time (e.g., hourly, weekly, daily, etc.). In certain embodiments, the absolute maximum potassium level is consistent across all hosts (e.g., set to 5.5 mmol / L based on current medical guidelines). In certain other embodiments, each host has a different absolute maximum potassium level. In certain embodiments, the absolute maximum potassium level per host changes over time. For example, a host can be initially assigned an absolute maximum potassium level based on clinical input. This assigned absolute maximum potassium level can be adjusted over time based on other sensor data, disease stages, comorbidities, etc., for the host.

[0151] For example, a host’s absolute maximum potassium level can vary over time as a user’s kidney function, kidney disease, and / or one or more other diseases progress and / or improve. In certain embodiments, a first absolute maximum potassium level is determined for periods of time where no external conditions exist that would affect the potassium level, and a second absolute maximum potassium level can be determined for periods of time where external conditions do exist that would affect the potassium level (e.g., during periods of time when the host is consuming potassium, exercising, taking medication that affects potassium levels, etc.).

[0152] In certain embodiments, an absolute minimum potassium level is determined from sensor data (e.g., potassium measurements obtained from a CPM of continuous analyte monitoring system 104), medication / treatment metrics, and / or medical history / disease diagnosis metrics. The absolute minimum potassium level represents a host’s minimum potassium level determined to be safe over a period of time (e.g., hourly, weekly, daily, etc.).In certain embodiments, the absolute minimum potassium level is consistent across all hosts (e.g., set based on current medical guidelines). In certain other embodiments, each host has a different absolute minimum potassium level. In certain embodiments, the absolute minimum potassium level per host changes over time. For example, a host can be initially assigned an absolute minimum potassium level based on clinical input. This assigned absolute minimum potassium level can be adjusted over time based on other sensor data, disease stages, comorbidities, etc. for the host.

[0153] For example, a host’s absolute minimum potassium level can vary over time as a host’s kidney function, kidney disease, and / or one or more other diseases progress and / or improve. In certain embodiments, a first absolute minimum potassium level is determined for periods of time where no external conditions exist that would affect the potassium level, and a second absolute minimum potassium level is determined for periods of time where external conditions do exist that would affect the potassium level (e.g., during periods of time when the host is consuming potassium, exercising, taking medication that affects potassium levels, etc.).

[0154] In certain embodiments, potassium level rates of change are determined from sensor data (e.g., potassium measurements obtained from a CPM of continuous analyte monitoring system 104 over time). For example, a potassium level rate of change refers to a rate that indicates how one or more time-stamped potassium measurements or values change in relation to one or more other time-stamped potassium measurements or values. Potassium level rates of change can be determined over one or more seconds, minutes, hours, days, etc.

[0155] In certain embodiments, determined potassium level rates of change are marked as “increasing rapidly” or “decreasing rapidly.” As used herein, “rapidly” describes potassium level rates of change that are clinically significant and pointing towards a trend of the potassium level of the host likely breaching the absolute maximum potassium level or the absolute minimum potassium level within a next period of defined time. In other words, a predictive trend (e.g., produced by therapy management engine 114 using one or more trained models), in some cases, indicates that a host is likely to hit, for example, the absolute maximum potassium level within a specified time period (e.g., one or two hours) based on the determined potassium level rate of change. Accordingly, such a potassium level rate of change can be marked as “increasing rapidly.” Similarly, a predictive trend (e.g., produced by therapy management engine 114 using one or more trained models) can, in some cases, indicate that ahost is likely to hit the absolute minimum potassium level within a specified time period (e.g., one or two hours) based on the potassium level rate of change determined. Accordingly, such a potassium level rate of change can be marked as “decreasing rapidly.”

[0156] In certain embodiments, potassium baseline rates of change are determined from potassium baselines determined for a host over time. For example, a potassium baseline rate of change refers to a rate that indicates how one or more time-stamped potassium baselines for a host change in relation to one or more other time-stamped potassium baselines for the same host. Potassium baseline rates of change can be determined over one or more seconds, minutes, hours, days, etc.

[0157] In certain embodiments, a potassium clearance rate are determined from sensor data (e.g., potassium measurements obtained from a CPM of continuous analyte monitoring system 104) following the consumption of a known, or estimated, amount of potassium. Potassium clearance rates analyzed over time can be indicative of kidney function. In particular, the slope of a curve of potassium clearance during a first time period (e.g., after consuming a known amount of potassium) compared to the slope of a curve of potassium clearance during a second time period (e.g., after consuming the same amount of potassium) can be indicative of a kidney’s ability to function, and more particularly, to maintain potassium homeostasis (e.g., a potassium clearance rate can be slower when a host’s kidney is impaired than when a host’s kidney is healthy).

[0158] In certain embodiments, the potassium clearance rate is determined by calculating a slope between an initial potassium value (e.g., during a period of increased potassium levels) and a potassium baseline associated with the host. In certain embodiments, a potassium clearance rate is calculated over time until the increased potassium levels of the host reach some value relative to the host’s potassium baseline (e.g., % of a host’s potassium baseline). Potassium clearance rates calculated over time can be time-stamped and stored in the host’s profile 118.

[0159] In certain embodiments, potassium metrics include intracellular and / or extracellular potassium shifts. While potassium clearance can indicate a total body potassium overload, potassium shifts can indicate temporary shifting of potassium into / out of cells as caused by insulin. To distinguish between potassium clearance and potassium shifts, additional inputs such as known administered insulin or diuretic doses, glucose data (as higher glucose wouldindicate a likely higher amount of insulin, and / or potassium measurements from urine samples can be used in addition to potassium measurements from blood or interstitial fluids. In certain embodiments, potassium trends are determined based on potassium levels over certain periods of time. In certain embodiments, potassium trends are determined based on potassium baselines over certain periods of time. In certain embodiments, potassium trends are determined based on absolute potassium level minimums over certain periods of time. In certain embodiments, potassium trends are determined based on absolute maximum potassium levels over certain periods of time. In certain embodiments, potassium trends are determined based on potassium level rates of change over certain periods of time. In certain embodiments, potassium trends are determined based on potassium baseline rates of change over certain periods of time. In certain embodiments, potassium trends are determined based on calculated potassium clearance rates over certain periods of time.

[0160] In certain embodiments, potassium variability is determined from sensor data (e.g., potassium measurements obtained from a CPM of continuous analyte monitoring system 104 over time). For example, potassium variability refers to a total amount of variation, or a total delta, in potassium levels that can occur in a given time period. Potassium variability can be determined over one or more seconds, minutes, hours, days, etc. For example, potassium variability can be determined over a 4-hour, 8-hour, 12-hour, or 24-hour time period, or more. Generally, when a host exhibits a relatively low total delta in a given time period, the host is likely in a healthy, potassium-regulated state. Conversely, when the host exhibits a relatively high total delta in a given time period, potassium levels of the host are not well regulated, and the host can be at risk for hypo- or hyperkalemia.

[0161] In certain embodiments, the host’s metrics 132 further include metrics for other analytes. For example, in certain embodiments, metrics 132 include glucose levels, glucose baselines, maximum and minimum glucose levels, glucose rates of change, glucose baseline rates of change, glucose clearance rates, glucose trends, lactate levels, lactate baselines, maximum and minimum lactate levels, lactate rates of change, lactate baseline rates of change, lactate clearance rates, lactate trends, calcium levels, calcium baselines, maximum and minimum calcium levels, calcium rates of change, calcium baseline rates of change, calcium clearance rates, calcium trends, ketone levels, ketone baselines, maximum and minimum ketone levels, ketone rates of change, ketone baseline rates of change, ketone clearance rates,ketone trends, creatinine levels, creatinine baselines, maximum and minimum creatinine levels, creatinine rates of change, creatinine baseline rates of change, creatinine clearance rates, creatinine trends, and / or levels, baselines, maximum and minimum levels, rates of change, baseline rates of change, clearance rates, and / or trends of one or more other analytes of the host.

[0162] In certain embodiments, meal state metrics indicate the state the host is in with respect to food consumption. For example, the meal state can indicate whether the host is in one of a fasting state, pre-meal state, eating state, post-meal response state, or stable state. In certain embodiments, the meal state also indicates nourishment on board, e.g., meals, snacks, or beverages consumed, and is determined, for example from food consumption information, time of meal information, and / or digestive rate information, which can be correlated to food type, quantity, and / or sequence (e.g., which food / beverage was eaten first).

[0163] In certain embodiments, meal habits metrics are based on the content and the timing of a host’s meals. For example, if a meal habit metric is on a scale of 0 to 1, the better / healthier meals the host eats the higher the meal habit metric of the host will be to 1, in an example. Also, the more the host’s food consumption adheres to a certain time schedule or a recommended diet, the closer their meal habit metric will be to 1, in the example.

[0164] In certain embodiments, body temperature metrics are calculated by DAM 116 based on inputs 130, and more specifically, non-analyte sensor data from a temperature sensor. In certain embodiments, heart rate metrics (e.g., including heart rate and heart rate variability) are calculated by DAM 116 based on inputs 130, and more specifically, non-analyte sensor data from a heart rate sensor. In certain embodiments, respiratory metrics are calculated by DAM 116 based on inputs 130, and more specifically, non-analyte sensor data from a respiratory rate sensor.

[0165] In certain embodiments, health and sickness metrics are determined, for example, based on one or more of host input (e.g., pregnancy information or known sickness information), from physiological sensors (e.g., temperature), activity sensors, or a combination thereof. In certain embodiments, based on the values of the health and sickness metrics, for example, a host’s state is defined as being one or more of healthy, ill, rested, or exhausted.

[0166] In certain embodiments, medication habit metrics are based on the host’s prescribed medications and a determination of whether the prescribed medications have an effect on thehost’s analyte levels. For example, by analyzing a host’s medication habits, DAM 116 can determine whether the host’s medications impact the host’s analyte measurements at a particular time. Based on the host’s medication habits, DAM 116 can determine whether the host’s analyte levels are a result of medication consumption or another cause, such as worsening organ (e.g., liver or kidney) function, for example. Medication habit metrics can be time-stamped so that they can be correlated with the host’s analyte levels at the same time.

[0167] In certain embodiments, treatment habit metrics are based on the host’s lifestyle habits, surgical procedures, and / or other non-invasive procedures recommended by the host’s physician, and a determination of whether the treatment habits have an effect on the host’s analyte levels. For example, by analyzing a host’s treatment habits, DAM 116 can determine whether the host’s treatment habits impact the host’s analyte measurements at a particular time. Based on the host’s treatment habits, DAM 116 can determine whether the host’s analyte levels are a result of treatment habits or another cause. Treatment habit metrics can be time-stamped so that they can be correlated with the host’s analyte levels at the same time.

[0168] In certain embodiments, medication adherence is measured by one or more metrics that are indicative of how committed the host is towards their medication regimen. In certain embodiments, medication adherence metrics are calculated based on one or more of the timing of when the host takes medication (e.g., whether the host is on time or on schedule), the type of medication (e.g., is the host taking the right type of medication), and the dosage of the medication (e.g., is the host taking the right dose).

[0169] Similarly, treatment adherence is measured by one or more metrics that are indicative of how committed the host is towards their treatment regimen. In certain embodiments, treatment adherence metrics are calculated based on one or more of the timing of when the host performs certain treatment habits and / or the type of treatment.

[0170] In certain embodiments, the activity level metric indicates the host’s level of activity. In certain embodiments, the activity level is determined, for example based on input from an activity sensor or other physiologic sensors, such as non-analyte sensors 206. In certain embodiments, the activity level metric is calculated by DAM 116 based on one or more of inputs 130, such as one or more of exercise information, non-analyte sensor data (e.g., accelerometer data), time, host input, etc. In certain embodiments, the activity level isexpressed as a step rate of the host. Activity level metrics can be time-stamped so that they can be correlated with the host’s potassium levels at the same time.

[0171] FIG. 4 is a flow diagram depicting an example method 400 for providing therapy management guidance when a host has started, or is undergoing, therapy with a new medication regime, according to certain embodiments of the present disclosure. Generally, the method 400 can be performed, at least in part, by therapy management system 100, including therapy management engine 114. In certain embodiments, therapy management system 100 provides guidance via the method 400 using at least a continuous potassium monitor (CPM), as described with reference to FIGs. 1 and 2.

[0172] As a general overview, in the method 400, the therapy management engine 114 of the therapy management system 100 monitors a host’s analyte and / or non-analyte data. For example, therapy management system 100 monitors the host’s potassium concentration levels to obtain potassium data for the host, which is stored as potassium metrics, as shown in FIG.3. Further, additional potassium metrics can be calculated by therapy management engine 114 and stored, as shown in FIG. 3. In certain embodiments, the therapy management engine 114 then compares one or more of the host’s potassium metrics (e.g., potassium levels and / or potassium rates of change) against each other, with historical potassium metrics for the host, and / or with historical potassium metrics of other patients taking similar medications or having similar disease states.

[0173] For example, if the host’s potassium rate of change is within a threshold or approaches a threshold determined based on the historical data, the therapy management engine 114 determines the host is not experiencing a serious health problem, such as a kidney or liver complication, and continues monitoring the host’s data with or without providing guidance to the host. However, if the host’s potassium rate of change continues to increase or decrease slowly, or begins shifting in an unanticipated direction, the therapy management engine 114 determines that this behavior is a result of the medication regimen of the host. In some examples, if the host’s potassium levels gradually increase with time, the therapy management engine 114 recommends the host to administer additional medication, or adjust a dose of a current medication (e.g., up-titrate or down-titrate over time), or change the current medication to another medication, or stop taking a current medication. These and other features of the method 400 are described in more detail below

[0174] In certain embodiments, the therapy management guidance of the method 400 is provided to the host through a user interface of a device of the host, such as through a touchscreen display of any one of display devices 210, 220, 230, and / or 240 of FIG. 2. In certain embodiments, the therapy management guidance of the method 400 is provided to the host as an audible signal, an audible or visual message, an audible or visual alert or notification, or other perceptible signal or indication to the host.

[0175] Generally, where therapy management guidance in the method 400 includes a recommendation to administer a medication, an order for the medication can be automatically generated, or sent to a healthcare provider of a host, for approval or review by the healthcare provider. Further, in certain embodiments where the therapy management guidance in the method 400 includes a recommendation to administer a medication, the therapy management engine 114 can wirelessly communicate with a medication delivery device (e.g., a pump, pen, or another dosing mechanism) to automatically administer, or adjust an administration of, the medication based on the therapy management guidance.

[0176] Turning to FIG.4, the method 400 begins at block 402, where therapy management engine 114 identifies that a host has begun a course of therapy to treat a disease state of the host. In certain embodiments, the host includes a patient suffering from HF, hypertension, CKD, and / or other disease state, and the therapy includes a RAASi medication, a diuretic, and / or other medication.

[0177] The identification at block 402 is based on receiving one or more input data and determining, based on the input data, that a therapy course has been initiated. The inputs can be received in a variety of ways. For example, in certain embodiments, the inputs are received or retrieved from the host profile 118, which includes demographic information 120, physiological information 120, disease information 124, medication information 126, inputs 130, metrics 132, etc. Inputs can also be received as user input through the user interface of a display device 107, or from continuous analyte sensor(s) 202 and / or non-analyte sensor(s) 206. The inputs generally include, but are not limited to, information about the therapy regime of the host. For example, the inputs at block 402 can include a type, identity, and / or dosage of the therapy begun by the host.

[0178] In certain embodiments, the identification of the course of therapy at block 402 is based on receiving one or more user inputs. For example, the therapy management engine 114can generate and provide the host, or a caregiver of the host, with a notification requesting user input about whether the host has or has not begun the course of therapy. The host or caregiver can thereafter acknowledge the notification, or otherwise provide some form of input to the therapy management engine 114, to confirm that the host has or has not begun the course of therapy. The user input(s) can be received via, for example, the display device 107.

[0179] In certain embodiments, at block 402, the identification of the course of therapy is based on analyzing analyte information, such as analyte data associated with the host. In some example embodiments, such analysis of analyte information indicates an atypical analyte state of the host. For example, the therapy management engine 114 can determine that the host is experiencing a potassium rate of change, and / or rate(s) of change of other analyte(s), that indicates an atypical, and / or acute, analyte state of the host as caused by the administration of a course of therapy. As utilized herein, an atypical analyte state of the host refers to a state wherein the host’s analyte levels are outside of minimum and / or maximum analyte threshold levels for the host, and / or wherein the host’s analyte levels are above or below a baseline analyte level for the host, and / or wherein the host’s analyte rate of change is outside of minimum and / or maximum analyte rate of change thresholds for the host. Based on the atypical analyte state, the therapy management engine 114 can determine that the host has indeed begun the course of therapy. In certain embodiments, the potassium rate of change, and / or rate(s) of change of other analyte(s), are also utilized to determine what type of therapy has been administered, as different types of medication will cause different rate of change profiles for potassium and / or other analytes.

[0180] In certain embodiments, the input data for the detection of the course of therapy at block 402 includes data received by the therapy management engine 114 from a clinician or other healthcare provider, such as prescription information and / or other medication information (e.g., medication identity, type, and / or dosage). In certain embodiments, the input data received at block 402 includes data from a sensor, such as an aptamer sensor as discussed above, that can detect the presence of medication in the body (e.g., bloodstream) of the host. In certain embodiments, the input data for detection at block 402 includes information from a medication delivery device (e.g., a pump, pen, or another dosing mechanism).

[0181] At block 404, upon identifying that the host has begun a course of therapy to treat a disease state of the host, the therapy management engine 114 continuously or non-continuously monitors at least one analyte of the host during a time period to obtain analyte data, the at least one analyte including at least potassium. The analyte data is used by the therapy management engine 114 as input to determine the host’s response to the therapy identified at subsequent block 406. For example, using at least a continuous potassium monitor (CPM) of the continuous analyte monitoring system 104, the therapy management system 100 continuously measures at least the host’s potassium, and the potassium data is provided to the therapy management engine 114 as input to determine the host’s response to the therapy. Generally, therapies such as RAASi medications and other medications that affect aldosterone levels, as well as diuretics, can negatively affect a host’s potassium levels and lead to various complications (e.g., sudden cardiac death, arrhythmia, etc.) if left untreated. Thus, monitoring at least the host’ s potassium after beginning a course of therapy allows the therapy management engine 114 to determine whether the host is negatively or positively responding to the therapy, and to further provide therapy management guidance based on the response to facilitate favorable treatment outcomes for the host.

[0182] In certain embodiments, the one or more analytes of the host can also be monitored by the therapy management system 100 prior to the identification of the host beginning the course of therapy in order to determine and / or set baseline analyte metrics of the host. In certain embodiments, the one or more analytes can also be monitored by the therapy management system 100 prior to block 404 to provide therapy management guidance to begin a course of therapy, and / or what type of therapy, to treat the disease state of the host, such as HF, hypertension, CKD, and / or another disease state.

[0183] While the main analyte for measurement described herein is potassium, in certain embodiments, other analytes are also monitored at block 404. In particular, combining potassium data with additional analyte data can help to further inform the analysis for determining optimal therapy management guidance. For example, continuously or non-continuously monitoring additional types of analytes, such as glucose, lactate, ketones, calcium, and / or creatinine as measured by the continuous analyte monitoring system 104, can provide additional insight into the host’s response to the therapy and / or supplement information used to determine optimal therapy management guidance. In certain embodiments, the other analyte data is utilized to confirm, or modify, determinations based on potassium measurements. In certain embodiments, the other analyte data are utilized as a surrogate forthe host’s potassium measurements. For example, in certain embodiments, other analyte data, such as glucose data, lactate data, ketone data, calcium data, and / or creatinine data (e.g., current or historical), is correlated with the host’s continuously monitored potassium data (e.g., current or historical). Thereafter, the other analyte data is utilized in place of continuous potassium measurements.

[0184] In addition to monitoring one or more analytes of the host during the time period to obtain analyte data at block 404, in certain embodiments, the method 400 also includes monitoring non-analyte sensor data during the time period using one or more non-analyte sensors or devices. For example, in certain embodiments, measurements from non-analyte sensors of the host are collected by the therapy management engine 114 at block 404 from, e.g., one or more non-analyte sensors or devices of the continuous analyte monitoring system 104. As mentioned elsewhere herein, non-analyte sensors and devices can include one or more of, but are not limited to, an insulin pump, a respiratory sensor, a heart rate monitor, electromyogram (EMG) sensor, an ECG, an accelerometer, an altimeter sensor, a temperature sensor (e.g., thermometer), a blood pressure monitor, a galvanic skin response (GSR) sensor, a sweat sensor, a pulse oximeter, a caloric intake monitor, sensors or devices provided by display device 107 (e.g., accelerometer, camera, global positioning system (GPS), etc.), or any other sensors or devices that provide relevant information about the user and / or the physical activity being engaged by the user.

[0185] In certain embodiments, the non-analyte sensor data is utilized to confirm, or modify, determinations based on analyte data. In certain embodiments, the non-analyte sensor measurements are utilized as a surrogate for the host’s analyte data.

[0186] In certain embodiments, in addition to monitoring one or more analytes of the host during the time period, the therapy management engine 114 further collects other inputs, such as inputs received as user input through the user interface of a display device 107 during the time period. The inputs are not limited to, but can include, information about food consumption by the host, exercise and / or activity information, environmental information, and / or the like.

[0187] In certain embodiments, the time period at block 404 has a duration of 1 or more seconds, minutes, hours, days, weeks, months, or more. For example, in certain embodiments, the time period at block 404 has a duration between about 1 minute and about 24 hours. For example, in certain embodiments, the time period at block 404 has a duration of about 5minutes, about 10 minutes, about 20 minutes, about 30 minutes, about 1 hour, about 2 hours, about 3 hours, about 4 hours, about 5 hours, about 6 hours, about 7 hours, about 8 hours, about 9 hours, about 10 hours, about 11 hours, about 12 hours, about 13 hours, about 14 hours, about 15 hours, about 16 hours, about 17 hours, about 18 hours, about 19 hours, about 20 hours, about 21 hours, about 22 hours, about 23 hours, about 24 hours. In certain embodiments, longer durations are employed. In certain embodiments, the time period at block 404 is determined based on a type, identity, and / or dosage of the therapy begun by the host. For example, where the therapy includes a potassium supplement or insulin, the time period can have a duration of one or more hours, such as two hours, three hours, four hours, five hours, six hours, seven hours, eight hours, nine hours, ten hours, eleven hours, twelve hours, thirteen hours, fourteen hours, fifteen hours, sixteen hours, seventeen hours, eighteen hours, nineteen hours, twenty hours, twenty-two hours, twenty-three hours, twenty-four hours, or more; where the therapy includes a potassium binder, the time period can have a duration of hours to days, such as two hours, three hours, four hours, five hours, six hours, seven hours, eight hours, nine hours, ten hours, eleven hours, twelve hours, thirteen hours, fourteen hours, fifteen hours, sixteen hours, seventeen hours, eighteen hours, nineteen hours, twenty hours, twenty-two hours, twenty-three hours, twenty-four hours, two days, three days, four days, or more; and where the therapy includes a RAASi medication, the time period can have a duration of one or more days, such as two days, three days, four days, five days, six days, seven days, or more.

[0188] At block 406, the method 400 continues with the therapy management engine 114 processing at least the analyte data from the time period at block 404 to determine the host’s response to the therapy identified at block 402. In other words, the data collected or otherwise obtained during the time period is processed to determine the effect of the therapy on the host. Generally, the data, including the potassium data of the host, is processed by the therapy management engine 114 to determine the one or more metrics 132 of the host during the time period, which in turn, can indicate the health or state of the host in response to the therapy, such as one or more of the physiological state of the host, trends associated with the health or state of the host, etc.

[0189] In certain embodiments, the metrics 132 at block 406 include one or more potassium metrics of the host during the time period, such as one or more of potassium levels of the host, minimum potassium levels of the host, maximum potassium levels of the host, potassium ratesof change of the host, potassium clearance rates of the host, potassium shifts of the host, potassium trends of the host, potassium variability of the host, and / or other potassium metrics of the host during the time period. In certain embodiments, the metrics 132 at block 406 include one or more other analyte metrics of the host during the time period. In certain embodiments, the metrics 132 at block 406 include one or more non-analyte sensor metrics of the host during the time period.

[0190] In certain embodiments, at block 406, the therapy management engine 114 compares one or more metrics 132 of the host against each other, with historical potassium metrics for the host, and / or with historical potassium metrics of other patients taking similar medications or having similar disease states, in order to determine the host’s response to the therapy, and / or to determine optimal therapy management guidance for the host. For example, in certain embodiments, block 406 involves comparing potassium levels of the host, minimum potassium levels of the host, maximum potassium levels of the host, potassium rates of change of the host, potassium clearance rates of the host, potassium shifts of the host, potassium trends of the host, potassium variability of the host, and / or other potassium metrics of the host to potassium baselines of the host, potassium baseline rates of change of the host, and / or other potassium thresholds and / or target ranges of the host. In certain embodiments, block 406 involves comparing current potassium levels of the host, current minimum potassium levels of the host, current maximum potassium levels of the host, current potassium rates of change of the host, current potassium clearance rates of the host, current potassium shifts of the host, current potassium trends of the host, current potassium variability of the host, and / or other current potassium metrics of the host to historical potassium levels of the host, historical minimum potassium levels of the host, historical maximum potassium levels of the host, historical potassium rates of change of the host, historical potassium clearance rates of the host, historical potassium shifts of the host, historical potassium trends of the host, historical potassium variability of the host, and / or other historical potassium metrics of the host. In certain embodiments, block 406 involves comparing potassium levels of the host, minimum potassium levels of the host, maximum potassium levels of the host, potassium rates of change of the host, potassium clearance rates of the host, potassium shifts of the host, potassium trends of the host, potassium variability of the host, and / or other potassium metrics of the host to potassium levels of other patients, minimum potassium levels of other patients, maximum potassium levels of other patients, potassium rates of change of other patients, potassiumclearance rates of other patients, potassium shifts of other patients, potassium trends of other patients, potassium variability of other patients, and / or other potassium metrics of other patients.

[0191] In certain example embodiments, at block 406, the therapy management engine 114 determines, based on the potassium metrics of the host during the time period, whether a potassium rate of change of the host during the time period meets, exceeds, or falls below, a threshold potassium rate of change. In certain embodiments, the threshold potassium rate of change includes a predetermined threshold beyond which a measured potassium rate of change of the host is indicative of a current or impending potassium imbalance for the host.

[0192] In certain embodiments, at block 406, the therapy management engine 114 determines, based on the potassium metrics of the host during the time period, whether the host’ s potassium levels during the time period are within a target range. As used herein, “within range” refers to the measured potassium levels being between an absolute minimum potassium level, or minimum potassium level threshold, and an absolute maximum potassium level, or maximum potassium level threshold, for the host, and “out of range” refers to the measured potassium levels being below an absolute minimum potassium level or above an absolute maximum potassium level for the host. Generally, the absolute minimum potassium level represents a minimum potassium level determined to be safe for the host, and the absolute maximum potassium level represents a maximum potassium level determined to be safe for the host. Thus, when the potassium levels of the host are below the absolute minimum potassium level or above the absolute maximum potassium level, the host can be determined to be in a risk state by the therapy management engine 114. Generally, the average minimum and maximum threshold potassium levels are about 3.5 mEq / L and about 5.2 mEq / L, respectively, for adults, and about 3.4 mEq / L and about 4.7 mEq / L, respectively, for children. Accordingly, the absolute minimum and / or maximum potassium levels for the host can, in certain embodiments, be equal to or nearly equal to the average minimum and maximum threshold potassium levels for adults or children. However, such thresholds can be personalized or otherwise adjusted for each host based on host statistics, medication / treatment information, medical history, disease diagnoses, etc.

[0193] In certain embodiments, an absolute minimum potassium level and / or an absolute maximum potassium level is determined from historical or current sensor data (e.g., potassiummeasurements obtained from a CPM of continuous analyte monitoring system 104), medication / treatment metrics, medical history / disease diagnosis metrics, or other historical data of the host. In certain embodiments, the absolute minimum potassium level and / or the absolute maximum potassium level for a host is determined based on current or historical data of the host. In certain embodiments, in addition to or as an alternative to the data of the host, the absolute minimum potassium level and / or the absolute maximum potassium level for the host are determined based on historical population-based data, such as patients with similar medication information (e.g., taking similar medications and / or doses thereof), and / or patients with similar demographic information, and / or patients with similar physiological information, and / or patients with similar disease information.

[0194] In certain embodiments, at block 406, the therapy management engine 114 determines, based on the potassium metrics of the host during the time period, whether the host’s current potassium levels (as determined at the time of block 406) are trending toward a high threshold potassium level or a low threshold potassium level. Generally, this determination can be based on a comparison by the therapy management engine 114 of the host’ s current potassium levels and / or potassium rates of change to one or more potassium level thresholds and / or potassium rate of change thresholds, respectively, and / or can be based on a prediction generated by the therapy management engine 114 using current and / or historical user data of the host and / or historical population-based data of patients with similar medication information. In certain embodiments, the high threshold potassium level includes a predetermined upper threshold above which a measurement of potassium levels is indicative of a current or impending potassium imbalance (e.g., hyperkalemia) for the host. Similarly, in certain embodiments, the low threshold potassium level at 406 includes a predetermined lower threshold below which a measurement of potassium levels is indicative of a current or impending potassium imbalance (e.g., hypokalemia) for the host.

[0195] In certain embodiments, at block 406, the therapy management engine 114 determines, based on the potassium metrics of the host during the time period, whether the host is experiencing an abnormal potassium episode or at least one of a plurality of predetermined symptoms that might indicate a severe health incident requiring medical attention. Examples of such predetermined symptoms include cardiac rhythm abnormalities such as arrhythmias, flutter, sudden cardiac death, chest pain, tingling, and muscle weakness, as well as othersymptoms such as gas or bloating, nausea, decreased reflexes, paralysis, and the like. In certain embodiments, the abnormal potassium is characterized by high potassium levels with a gradual, or slow, and positive (increasing potassium levels) rate of change. Generally, the determination of an abnormal potassium episode and / or predetermined symptom is based on various metrics 132 of the host, including potassium metrics such as potassium levels and potassium rates of change, other analyte metrics, non-analyte sensor metrics such as heart rate and respiratory rate, and other metrics 132. In certain embodiments, the abnormal potassium episode and predetermined symptom is based on a comparison of one or more of the host’s potassium metrics (e.g., potassium levels and / or potassium rates of change) against each other, with historical potassium metrics for the host, and / or with historical potassium metrics of other patients taking similar medications or having similar disease states.

[0196] Returning to FIG. 4, the method 400 continues at block 408 with the therapy management engine 114 generating and outputting therapy management guidance based, at least in part, on the determined response of the host to the therapy at block 406. Generally, therapy management guidance at block 408 includes guidance for the host aimed at stabilizing the host’s potassium levels, which can include guidance facilitating an increase or decrease in potassium levels of the host. In certain embodiments, the therapy management guidance includes alarms or alerts and / or recommendations for immediate action, such as administration of a drug or consumption of food, or seeking immediate medical attention. In certain embodiments, therapy management engine 114 outputs such guidance for treatment to the host (e.g., through application 106). In certain embodiments, the guidance is displayed for viewing by the host on, e.g., display device 107 illustrated in FIG. 1, and display devices 210, 220, 230, and 240 illustrated in FIG. 2.

[0197] Examples of guidance generated by the therapy management engine 114 include: a recommendation for administration of a medication, such as a potassium binder, a potassium supplement, a diuretic, insulin, or other similar medication, which can be the same or different from the identified therapy at block 402; a recommendation for optimal timing, dosage, and / or dose frequency for administration of a medication, and / or a change to the timing, dosage, and / or dose frequency of the medication; a recommendation for consumption of a meal, which can include a timing and / or type of meal; a recommendation to seek immediate medical attention, such as in an emergency healthcare setting; and / or a recommendation to continueperforming one or more actions or lifestyle habits (e.g., where the host is determined to not be at risk, and / or is not experiencing, a current or future potassium imbalance).

[0198] In certain embodiments, after therapy management guidance is generated and output by the therapy management engine 114, the method 400 returns to block 404, where the therapy management engine 114 continues monitoring the at least one analyte of the host for a second time period to determine the efficacy of the therapy management guidance generated at block 408. After further monitoring and processing of the analyte and / or non-analyte data of the host, the therapy management engine 114 can determine whether the previously provided therapy management guidance was effective, and where the therapy management guidance is determined to be ineffective (e.g., failed to stabilize or improve the host’s potassium levels), the therapy management engine 114 can generate and output adjusted therapy management guidance aimed at stabilizing the host’s potassium levels. Where the therapy management guidance is determined to be effective, the therapy management engine 114 can continue monitoring and processing the analyte and / or non-analyte data of the host, either indefinitely or until the host’s potassium levels destabilize. Accordingly, the method 400 is an iterative process, where therapy management guidance can be provided to the host based on the host’s state and then adjusted, as needed, upon further monitoring of the host’s state.

[0001] In certain embodiments, where the host is determined to have an initial positive response to the therapy at block 406, the therapy management engine 114 will not provide any therapy management guidance, and will instead return to block 404 to continue monitoring the at least one analyte of the host. In certain embodiments, where the host is determined to have a positive response to the therapy at block 406, the therapy management engine 114 will provide therapy management guidance including a recommendation to continue performing one or more actions (e.g., administration of medication, consuming a meal, etc.) or lifestyle habits (e.g., exercise, etc.) of the host, and will then return to block 404 to continue monitoring the at least one analyte of the host. As described above, the method of method 400 performs various steps to detect potassium imbalance and provide guidance to the host. At each step, or at one or more steps, the method 400 may generate alarms or alerts to warn the host of the condition of the host, namely the potassium imbalance or other detected conditions. The alarms and alerts may include information about the specific condition such as the level of potassium or other analytes and / or may provide recommendations for further treatment of the condition.The recommendations can include specific instructions to be carried out by the host. The system can then monitor the host for compliance with the instructions and provide further alarms or alerts as needed to ensure that the detected condition of the host is being treated. The alarms or alerts are provided at the sensor electronics and / or the display device for the host. Alarms or alerts can also be shared with a care provider to ensure the host is treated or to inform the care provider of the state of the host.

[0199] FIG. 5 is a flow diagram depicting an example workflow 500 for providing therapy management guidance via the method 400, according to certain embodiments of the present disclosure. The workflow 500 can be performed, at least in part, by therapy management system 100, including therapy management engine 114. In certain embodiments, therapy management system 100 provides guidance via the workflow 500 using at least a continuous potassium monitor (CPM), as described with reference to FIGs. 1 and 2.

[0200] The workflow 500 begins at blocks 502 and 504 at the top of FIG. 5. Generally, the blocks 502 and 504 correspond to blocks 402 and 404 of the method 400, respectively. At block 502, therapy management engine 114 detects that a host has begun a course of therapy to treat a disease state of the host. At block 504, upon detecting that a host has begun a course of therapy to treat a disease state of the host, at least one or more of the host’ s potassium metrics are monitored by the therapy management system 100 over a time period and are provided as input to the therapy management engine 114 to determine the host’s response to the therapy identified at block 502.

[0201] After block 504, the workflow 500 progresses to one or more of blocks 506, 508, 510, 512, 518, and 526, which each generally correspond to the operations performed at block 406 of the method 400. From there, further progression of the workflow 500 leads to one or more of blocks 514, 516, 520, 522, 524, 528, and 530, which each generally correspond to the operations performed at block 408 of the method 400 and include the generation and provision of therapy management guidance to the host.

[0202] Turning to block 506, the therapy management engine 114 determines, based on the measured potassium metrics of the host during the time period, whether a potassium rate of change of the host surpasses, or is greater than, a threshold potassium rate of change. In certain embodiments, the threshold potassium rate of change includes a predetermined and absoluteupper threshold beyond which a measurement of potassium rate of change may be indicative of a current or impending potassium imbalance for the host.

[0203] In certain embodiments, the threshold potassium rate of change is determined based on a type, identity, and / or dosage of the therapy begun by the host. For example, certain therapies such as insulin, dialysis, and potassium supplements are faster acting than other therapies, and so greater increases or decreases in potassium levels after administration of such therapies can be tolerable or acceptable by the host without necessitating the additional action or treatment for potassium imbalance. In such examples, the threshold potassium rates of change can be relatively greater at block 506. However, for other therapies, such as medications like potassium binders and diuretics, greater increases or decreases in potassium levels indicate an impending potassium imbalance requiring further action or treatment. In such examples, the threshold potassium rates of change can be relatively lower at block 506.

[0204] In certain embodiments, to optimize the threshold potassium rate of change for the host, the threshold potassium rate of change is personalized to the host based on the data for the host, including for example the potassium data of the host. For example, the rate of change, in some embodiments, is determined based on historical data of the host, such as historical potassium data of the host. In certain embodiments, in addition to or as an alternative to historical data of the host, the threshold potassium rate of change is determined based on historical population-based data of patients with similar medication information (e.g., taking similar medications and / or doses thereof), and / or patients with similar demographic information, and / or patients with similar physiological information, and / or patients with similar disease information.

[0205] If the therapy management engine 114 determines that the potassium rate of change of the host does not surpass, or is less than, the threshold potassium rate of change at block 506, the therapy management engine 114 continues to block 508. If the therapy management engine 114 determines that the potassium rate of change of the host surpasses, or is greater than, the threshold potassium rate of change at block 506, the therapy management engine 114 continues to block 510.

[0206] Referring to block 508, upon determining that the potassium rate of change of the host is less than the threshold potassium rate of change, the therapy management engine 114 determines whether the host’s current potassium levels (as determined at the time of block 508)meet a range. This determination, in some embodiments, includes retrieving the current potassium levels, and comparing the potassium levels to a predetermined upper and lower limit to determine if the current potassium level is within range (does not exceed the upper threshold or fall below the lower threshold) for the host. In another embodiment, the determination instead checks whether the current threshold value exceeds or falls below a threshold and thus it is out of range.

[0207] If the therapy management engine 114 determines that the host’s potassium levels are within range at block 508, the therapy management engine 114 performs block 512. If the therapy management engine 114 determines that the host’s potassium levels are out of range at block 508, the therapy management engine 114 performs block 518.

[0208] Referring to block 512, in some embodiments, upon determining that the host’s potassium levels are within range, the therapy management engine 114 further determines whether the host’s current potassium levels (as determined at the time of block 512) are trending toward a high threshold potassium level or a low threshold potassium level. In certain embodiments, the high threshold potassium level at block 512 includes a predetermined upper threshold above which a measurement of potassium levels is indicative of a current or impending potassium imbalance (e.g., hyperkalemia) for the host. Similarly, in certain embodiments, the low threshold potassium level at block 512 includes a predetermined lower threshold below which a measurement of potassium levels is indicative of a current or impending potassium imbalance (e.g., hypokalemia) for the host. In certain embodiments, the high and / or low threshold potassium levels are the absolute maximum and / or absolute minimum potassium levels, respectively, utilized at block 508. In certain embodiments, the high and / or low threshold potassium levels are different from the absolute maximum and / or absolute minimum potassium levels, respectively, utilized at block 508.

[0209] If the therapy management engine 114 determines that the host’s potassium levels are not trending toward a high threshold potassium level or a low threshold potassium level at block 512, then the therapy management engine 114 determines that the host is not at risk, and / or is not experiencing, a current or future potassium imbalance, and therefore no therapy management guidance is required at this time. Accordingly, the therapy management engine 114 returns to block 504 to continue monitoring at least the host’s potassium levels. In certain embodiments, after returning to block 504 from block 512, the therapy management engine114 monitors, and / or the therapy management system 100 measures, at least the host’s potassium levels with increased frequency.

[0210] If, however, the therapy management engine 114 determines that the host’s potassium levels are trending toward a high threshold potassium level at block 512, the therapy management engine 114 performs block 514. If the therapy management engine 114 determines that the host’s potassium levels are trending toward a low threshold potassium level at block 512, the therapy management engine 114 performs block 516.

[0211] At block 514, upon determining that the host’s potassium levels are trending toward a high threshold potassium level, the therapy management engine 114 generates therapy management guidance for the host aimed at stabilizing the host’s potassium levels and continues monitoring at least the host’s potassium levels for a specified time period.

[0212] In certain embodiments, the trending of the host’s potassium levels toward the high threshold potassium level is a result of a therapy being administered by the host to treat a disease state of the host. For example, for HF patients, the increasing potassium levels can be caused by a RAASi medication taken by the patient to treat their HF disease state, or by the use of a potassium supplement. In such embodiments, the therapy management engine 114 can determine that the increasing potassium levels are caused by the therapy, and generates therapy management guidance aimed at reducing and stabilizing the host’s potassium levels based on this determination. Generally, therapy management engine 114 can determine that the increasing potassium levels are caused by the therapy based on various metrics 132 of the patient, including potassium metrics such as potassium levels and potassium rates of change, other analyte metrics, non-analyte sensor metrics such as heart rate and respiratory rate, and other metrics 132. In certain embodiments, the determination that the increasing potassium levels of the host are caused by the therapy is based on a comparison of one or more of the host’s potassium metrics (e.g., potassium levels and / or potassium rates of change) against each other, with historical potassium metrics for the host, and / or with historical potassium metrics of other patients taking similar medications or having similar disease states.

[0213] In certain embodiments, the therapy management guidance at block 514 includes a recommendation to administer a potassium binder medication, or other similar medication, for removing excess potassium from the host’s body. In certain embodiments, based on a determined rate of change of the potassium levels of the host (e.g., the host’s potassium rate ofchange at block 506), and a proximity of the host’s potassium levels to the high threshold potassium level, the therapy management guidance includes guidance on an optimal timing and / or dose of the potassium binder medication for effectively stabilizing the host’s potassium levels. Where the therapy management guidance includes the recommendation to administer the potassium binder medication, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the potassium binder medication.

[0214] In certain embodiments, if the host’s potassium levels continue trending toward the high threshold potassium level after the provided therapy guidance in block 514, the therapy management engine 114 generates a new therapy management guidance for the host at block 514. The new therapy management guidance, in some examples, includes an increase to the dose or a dose frequency of the potassium binder medication or other similar medication, administering a different type of medication (i.e., change medications), and / or to seek immediate medical attention (e.g., in an emergency healthcare setting) for potential kidney dysfunction and / or kidney injury. In such embodiments, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the therapy management guidance. In certain embodiments, if the host’s potassium levels continue trending toward the high threshold potassium level after the provided therapy guidance, the therapy management engine 114 monitors at least the host’s creatinine levels to determine whether there is a risk or presence of kidney dysfunction and / or kidney injury for the host. If kidney dysfunction and / or kidney injury is determined, the therapy management engine 114 generates therapy management guidance for the host including the recommendation to seek immediate medical attention.

[0215] In certain embodiments, if the host’s potassium levels continue trending toward the high threshold potassium level after one or more of the recommendations above is provided to the host, the therapy management engine 114 generates therapy management guidance for the host at block 514 including a recommendation to decrease a dose or a dose frequency of the current therapy for their disease state, change to a different therapy for their disease state. For example, in certain embodiments, if the patient is using a potassium supplement, and the therapy management engine 114 determines that the host’s potassium levels are still trending toward the high threshold potassium level even after one or more recommendations is providedto the host, the therapy management engine 114 generates and provides guidance to the host to further decrease a dose or dose frequency of the potassium supplement, or to take a diuretic to pass more potassium in their urine. In certain embodiments, the patient can be provided with a recommendation for a new medication or additional medication such as a non-potassium sparing diuretic medication with the current therapy for their disease state. In such embodiments, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the therapy management guidance.

[0216] If, after one or more of the recommendations above are provided to the host, the therapy management engine 114 determines that the host’s potassium levels have stopped trending toward the high threshold potassium level at block 514, the therapy management engine 114 can generate therapy management guidance for the host including a recommendation to maintain the current therapy for their disease state, including any adjustments to the therapy for their disease state. If, after one or more of the recommendations above is provided to the host, the therapy management engine 114 determines that the host’s potassium levels are still trending toward the high threshold potassium level, the therapy management engine 114 can generate therapy management guidance for the host including a recommendation to further decrease the dose or the dose frequency of the current therapy for their disease state, or to stop administration of the therapy altogether, either indefinitely or until the patient’s potassium level stabilize.

[0217] In certain embodiments, at block 514, the therapy management engine 114 determines a risk of potassium imbalance associated with continuing, or maintaining, the therapy for the disease state of the host, based on historical data of the host, such as historical potassium data of the host, or historical population-based data of patients with similar medication information (e.g., taking similar medications and / or doses thereof), and / or patients with similar demographic information, and / or patients with similar physiological information, and / or patients with similar disease information. For example, based on the historical data of the host or the historical population-based data, the therapy management engine 114 can identify / determine how potassium levels of the host, or potassium levels of similar patients in the population, have responded to various changes in therapies, such as a change in dose, a change in dose frequency, and / or a change in therapy type. The therapy management engine 114 can then utilize this information to determine the risk of potassium imbalance associatedwith continuing the therapy for the host’s disease state, and can generate therapy management guidance for the host at block 514 on how to mitigate the risk and stabilize the host’ s potassium levels.

[0218] In certain embodiments, at block 514, the therapy management engine 114 receives (as inputs) and utilizes blood pressure data and / or other non-analyte sensor data of the host to determine additional risks associated with continuing, or maintaining, the therapy for the disease state of the host. For example, in certain embodiments, the therapy for the host’s disease state can cause blood pressure changes in addition to potassium imbalance. Thus, if the therapy management engine 114 determines that the host is experiencing a change in blood pressure at block 514, such as hypotension, the therapy management engine 114 can generate therapy management guidance for the host including a recommendation to down-titrate the therapy for their disease state in order to stabilize the blood pressure and / or potassium levels of the host, and / or can generate other therapy management guidance based on the determined additional risks associated with continuing the therapy for the disease state.

[0219] In certain embodiments, the therapy management guidance generated by the therapy management engine 114 at block 514 includes guidance related to the host’s diet. For example, in certain embodiments, inputs 130 for the therapy management engine 114 include food consumption data, such as data from a meal tracker application or journal of the host. Based on the host’s food consumption data and corresponding potassium levels before, during, and / or after consumed meals, the therapy management engine 114 can provide therapy management guidance in the form of recommendations relating to timing of meals and / or types of foods to consume for stable potassium levels, and / or recommendations relating to timing of meals and / or types of foods that could cause potassium imbalance(s).

[0220] In certain embodiments, the time period at block 514 has a duration of 1 or more seconds, minutes, hours, days, weeks, months, or more. For example, in certain embodiments, the time period at block 514 has a duration between about 1 minute and about 24 hours, or more. For example, in certain embodiments, the time period at block 514 has a duration of about 5 minutes, about 10 minutes, about 20 minutes, about 30 minutes, about 1 hour, about 2 hours, about 3 hours, about 4 hours, about 5 hours, about 6 hours, about 7 hours, about 8 hours, about 9 hours, about 10 hours, about 11 hours, about 12 hours, about 13 hours, about 14 hours, about 15 hours, about 16 hours, about 17 hours, about 18 hours, about 19 hours, about 20 hours,about 21 hours, about 22 hours, about 23 hours, about 24 hours, or more. In certain embodiments, the time period at block 514 has a duration of about 24 hours, about 36 hours, about 48 hours, about 60 hours, about 72 hours, about 84 hours, about 96 hours, or more. In certain embodiments, the time period at block 514 has a duration of about 1 week, about 2 weeks, about 3 weeks, about 4 weeks, or more. In certain embodiments, the time period at block 514 is determined based on a type, identity, and / or dosage of the therapy begun by the host. For example, where the therapy includes a potassium supplement or insulin, the time period can have a duration of one or more hours; where the therapy includes a potassium binder, the time period can have a duration of hours to days; and where the therapy includes a RAASi medication, the time period can have a duration of one or more days.

[0221] In certain embodiments, when determining that the increasing potassium levels of the host are caused by the therapy administered by the host to treat the disease state of the host, the therapy management engine 114 first rules out other confounding factors that could have caused or contributed to the increasing potassium levels. For example, the therapy management engine 114 can first determine that the increasing potassium levels are not caused by, for example, exercise performed by the host, such as high intensity exercise, consumption of a potassium supplement, or the like. Once such confounding factors are ruled out, the therapy management engine 114 can then determine that the increasing potassium levels of the host are indeed caused by the therapy administered by the host, and can proceed to provide guidance related to therapy management.

[0222] Upon performance of block 514, the therapy management engine 114 returns to block 504 to continue monitoring at least the host’s potassium levels.

[0223] Returning to blocks 508 and 512, if the therapy management engine 114 determines that the host’s potassium levels are out of range and trending toward a low threshold potassium level, the therapy management engine 114 performs block 516.

[0224] At block 516, similar to block 514, upon determining that the host’ s potassium levels are trending toward a low threshold potassium level, the therapy management system 100 provides therapy management guidance to the host aimed at stabilizing the host’s potassium levels and continues monitoring at least the host’s potassium levels for a specified time period.

[0225] In certain embodiments, the trending of the host’s potassium levels toward the low threshold potassium level is a result of a therapy being administered by the host to treat adisease state of the host. For example, for HF patients, the decreasing potassium levels can be caused by diuretic taken by the patient to treat fluid retention associated with their HF disease state. In such embodiments, the therapy management engine 114 determines that the decreasing potassium levels are caused by the diuretic, and generates therapy management guidance aimed at stabilizing the host’s potassium levels based on this determination.

[0226] In certain embodiments, the therapy management guidance includes a recommendation to administer a potassium supplement, or other similar medication, for increasing potassium levels in the host’s body. In certain embodiments, based on a determined rate of change of the potassium levels of the host (e.g., the host’s potassium rate of change at block 506), and a proximity of the host’s potassium levels to the low threshold potassium level, the therapy management system generates guidance on an optimal timing and / or dose of the potassium supplement for effectively stabilizing the host’s potassium levels. Where the therapy management guidance includes the recommendation to administer the potassium supplement, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the potassium supplement.

[0227] In certain embodiments, if the host’s potassium levels continue trending toward the low threshold potassium level after administration of the potassium supplement or other similar medication, the therapy management engine 114 generates therapy management guidance for the host at block 516 including a recommendation to increase the dose or a dose frequency of the potassium supplement or other similar medication, decrease a dose or a dose frequency of the therapy for their disease state, or stop administration of the therapy altogether, either indefinitely or until the host’s potassium level stabilize. In such embodiments, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the therapy management guidance.

[0228] If, upon further monitoring of at least the potassium levels of the host, the therapy management engine 114 determines that the host’s potassium levels have stopped trending toward the low threshold potassium level, the therapy management engine 114 can generate therapy management guidance for the host at block 516 including a recommendation to maintain the decreased dose or dose frequency of the therapy for their disease state, and / or to stop administering the potassium supplement.

[0229] In certain embodiments, at block 516, the therapy management engine 114 determines a risk of potassium imbalance associated with continuing, or maintaining, the therapy for the disease state of the host, based on historical data of the host, such as historical potassium data of the host, or historical population-based data of patients with similar medication information (e.g., taking similar medications and / or doses thereof), and / or patients with similar demographic information, and / or patients with similar physiological information, and / or patients with similar disease information. For example, based on the historical data of the host or the historical population-based data, the therapy management engine 114 can identify / determine how potassium levels of the host, or potassium levels of similar patients in the population, have responded to various changes in therapies, such as a change in dose, a change in dose frequency, and / or a change in therapy type. The therapy management engine 114 can then utilize this information to determine the risk of potassium imbalance associated with continuing the therapy for the host’s disease state, and can generate therapy management guidance for the host at block 516 on how to mitigate the risk and stabilize the host’s potassium levels.

[0230] In certain embodiments, at block 516, the therapy management engine 114 receives and utilizes current blood pressure data and / or other non-analyte sensor data of the host to determine additional risks associated with continuing, or maintaining, the therapy for the disease state of the host. For example, as described above, such additional risks can include changes in blood pressure. In certain embodiments, the additional risks include risks associated with fluid management, particularly in embodiments where the therapy for the disease state includes a diuretic. In such embodiments, the non-analyte sensor data received by the therapy management engine 114 at block 516 can include transthoracic impedance data, and / or other data measured by a cardiovascular implantable electronic device (CIED) that indicates fluid levels of the host. Based on the additional risks, the therapy management engine 114 can generate therapy management guidance for the host including a recommendation to downtitrate the therapy for their disease state in order to mitigate the additional risks and / or stabilize potassium levels of the host, and / or can generate other therapy management guidance based on the determined additional risks associated with continuing the therapy for the disease state.

[0231] In certain embodiments, the therapy management guidance generated by the therapy management engine 114 at block 516 includes guidance related to the host’s diet. For example,based on the host’s food consumption data and corresponding potassium levels before, during, and / or after consumed meals, the therapy management engine 114 can provide therapy management guidance in the form of recommendations relating to timing of meals and / or types of foods to consume for stable potassium levels, and / or recommendations relating to timing of meals and / or types of foods that could cause potassium imbalance(s).

[0232] In certain embodiments, when determining that the decreasing potassium levels of the host are caused by the therapy administered by the host to treat the disease state of the host, the therapy management engine 114 first rules out other confounding factors that could have caused or contributed to the decreasing potassium levels. For example, the therapy management engine 114 can first determine that the decreasing potassium levels are not caused by, for example, excessive sweating by the host due to temperature or other factors, vomiting by the host, diarrhea, consumption of a laxative, or the like. Once such confounding factors are ruled out, the therapy management engine 114 can then determine that the decreasing potassium levels of the host are indeed caused by the therapy administered by the host, and can proceed to provide guidance related to therapy management.

[0233] Upon performance of block 516, the therapy management engine 114 returns to block 504 to continue monitoring at least the host’s potassium levels.

[0234] Referring now to block 518, upon determining that the host’s potassium levels are out of range, the therapy management engine 114 determines whether the host is experiencing an abnormal potassium episode or at least one of a plurality of predetermined symptoms that might indicate a severe health incident requiring medical attention. Examples of such predetermined symptoms include cardiac rhythm abnormalities such as arrhythmias, flutter, sudden cardiac death, chest pain, tingling, and muscle weakness, as well as other symptoms such as gas or bloating, nausea, decreased reflexes, paralysis, and the like. In certain embodiments, the abnormal potassium is characterized by high potassium levels with a gradual, or slow, and positive (increasing potassium levels) rate of change.

[0235] If the therapy management engine 114 determines that the host is experiencing an abnormal potassium episode or at least one of the plurality of predetermined symptoms at block 518, the therapy management engine 114 performs block 520. If the therapy management engine 114 determines that the host is not experiencing an abnormal potassium episode or atleast one of the plurality of predetermined symptoms at block 518, the therapy management engine 114 performs block 522.

[0236] In certain embodiments, at block 520, upon determining that the host is experiencing an abnormal potassium episode or at least one of the plurality of predetermined symptoms, the therapy management engine 114 generates therapy management guidance for the host. The therapy management guidance, in some examples, is a recommendation to seek immediate medical attention in an emergency healthcare setting, such as a hospital. At the emergency healthcare setting, a clinician can then carry out various medical diagnostics to determine an appropriate treatment plan for the host.

[0237] In certain embodiments, at block 520, the therapy management guidance is to administer a potassium binder medication and insulin or a diuretic for removing excess potassium from the host’s body. For example, in certain scenarios where the host experiences elevated potassium levels, potassium binders alone may take effect too slowly, as they are typically orally administered to “bind” to potassium before it is absorbed by the gastrointestinal tract. Meanwhile, insulin can cause rapid shifts of potassium into cells, such as within 10 to 20 minutes of administration. However, insulin is short-acting, while potassium binders can persist for several days. Thus, in scenarios where a more immediate but prolonged effect is needed to stabilize elevated potassium levels, the therapy management guidance can be to administer a combination of a potassium binder and insulin or a diuretic.

[0238] Where the therapy management guidance includes the administration of the potassium binder medication and insulin or diuretic, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the medication. If the host’s potassium levels continue trending upward after administration of the potassium binder medication and insulin or diuretic, the therapy management engine 114 can generate a modified treatment guidance. For example, a recommendation for the host to seek immediate medical attention in an emergency healthcare setting can follow if after administration of therapy management guidance the condition of the host does not improve or continues to advance toward an unwanted state.

[0239] In certain embodiments, at block 520, the therapy management engine 114 receives (as inputs) and utilizes current ECG data and / or other non-analyte sensor data of the host to determine if the host’s ECG demonstrates a deviation from the host’s normal historical ECGdata. If the therapy management engine 114 determines that the host’s current ECG data does deviate from the host’s normal historical ECG, the therapy management engine 114 may then generate a recommendation for the host to seek immediate medical attention in an emergency healthcare setting.

[0240] In certain embodiments, recommendations generated by the therapy management engine 114 at block 520 to proceed to an emergency healthcare setting are based on a determined distance of the host to a nearest emergency healthcare setting. For example, at block 520, the therapy management engine 114 can receive, as input, current GPS data and / or other location data of the host (in addition to potassium data) to determine the distance of the host to the nearest emergency healthcare setting. If, based on the received inputs, the therapy management engine 114 determines that the host’s potassium levels are high and nearing the high threshold potassium, but are not currently beyond the high threshold potassium, and the therapy management system further determines that the host is located one hour from the nearest emergency healthcare setting, then the therapy management engine 114 can recommend to the host to proceed to the emergency healthcare setting immediately. In certain embodiments where the host is recommended to proceed to an emergency healthcare setting, the therapy management engine 114 further recommends the host to administer a small dose of insulin, consume a food or beverage, or otherwise cause an insulin spike to decrease or stem increasing potassium levels. In such embodiments, the temporary insulin spike can help the host to avoid cardiac rhythm abnormalities, such as severe or sudden cardiac death or stroke from arrythmia, prior to reaching the emergency healthcare setting.

[0241] At block 522, upon determining that the host is not experiencing an abnormal potassium episode or at least one of the plurality of predetermined symptoms, the therapy management engine 114 generates therapy management guidance for the host aimed at stabilizing the host’s potassium levels. In certain embodiments, even though the host is determined to not be experiencing an abnormal potassium episode or at least one of the plurality of predetermined symptoms, the therapy management engine 114 determines that the host is nevertheless in a high-risk state requiring medical attention, and generates therapy management guidance including a recommendation to seek medical attention from a healthcare professional.

[0242] In certain embodiments, where the potassium levels of the host are high (above the absolute maximum potassium level of the host) at block 522, the therapy management engine114 determines that the high potassium levels are caused by a therapy administered by the host. For example, for HF patients, the high potassium levels may be caused by a RAASi medication taken by the patient to treat their HF disease state. In such embodiments, the therapy management engine 114 generates therapy management guidance aimed at reducing and stabilizing the host’s potassium levels based on this determination.

[0243] In certain embodiments, the therapy management guidance at block 522 includes a recommendation to administer a potassium binder medication, or other similar medication, for removing excess potassium from the host’s body. Where the therapy management guidance includes the recommendation to administer the potassium binder medication, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the potassium binder medication.

[0244] In certain embodiments, if the host’s potassium levels remain high after administration of the potassium binder medication or other similar medication, the therapy management engine 114 generates therapy management guidance for the host at block 522 including a recommendation to increase the dose or a dose frequency of the potassium binder medication or other similar medication, administer a different type of medication (i.e., change medications), and / or seek immediate medical attention (e.g., in an emergency healthcare setting) for potential kidney dysfunction and / or kidney injury. In such embodiments, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the therapy management guidance. In certain embodiments, if the host’s potassium levels remain high after administration of the potassium binder medication or other similar medication, the therapy management engine 114 monitors at least the host’s creatinine levels to determine whether there is a risk or presence of kidney dysfunction and / or kidney injury for the host. If kidney dysfunction and / or kidney injury is determined, the therapy management engine 114 generates therapy management guidance for the host including the recommendation to seek immediate medical attention.

[0245] In certain embodiments, if the host’s potassium levels remain high after one or more of the recommendations above is provided to the host, the therapy management engine 114 generates therapy management guidance for the host at block 522 including a recommendation to decrease a dose or a dose frequency of the current therapy for their disease state, change to a different therapy for their disease state, and / or administer a non-potassium sparing diureticmedication with the current therapy for their disease state. In such embodiments, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the therapy management guidance.

[0246] If, upon further monitoring of at least the potassium levels of the host, the therapy management engine 114 determines at block 522 that the host’s potassium levels have stabilized or decreased to within range for the host, the therapy management engine 114 can generate therapy management guidance for the host including a recommendation to maintain the decreased dose or dose frequency of the current therapy for their disease state, continue with the different therapy for their disease state, and / or continue administering the nonpotassium sparing diuretic medication with the current therapy for their disease state. If, upon further monitoring of at least the potassium levels of the host, the therapy management engine 114 determines that the host’s potassium levels are still high, the therapy management engine 114 can generate therapy management guidance for the host including a recommendation to further decrease the dose or the dose frequency of the current therapy for their disease state, or to stop administration of the therapy altogether, either indefinitely or until the host’s potassium levels stabilize.

[0247] In certain embodiments, where the potassium levels of the host are low (below the absolute minimum potassium level of the host) at block 522, the therapy management engine 114 determines that the low potassium levels are caused by a therapy administered by the host. For example, for HF patients, the low potassium levels can be caused by a diuretic taken by the patient to treat fluid retention associated with their HF disease state. In such embodiments, the therapy management engine 114 generates therapy management guidance aimed at increasing and stabilizing the host’s potassium levels based on this determination.

[0248] In certain embodiments, the therapy management guidance includes a recommendation to administer a potassium supplement, or other similar medication, for increasing potassium levels in the host’s body. Where the therapy management guidance includes the recommendation to administer the potassium supplement, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the potassium supplement.

[0249] In certain embodiments, if the host’s potassium levels remain low after administration of the potassium supplement or other similar medication, the therapymanagement engine 114 generates therapy management guidance for the host at block 522 including a recommendation to increase the dose or a dose frequency of the potassium supplement or other similar medication, decrease a dose or a dose frequency of the therapy for their disease state, or stop administration of the therapy altogether, either indefinitely or until the patient’s potassium levels stabilize. In such embodiments, the therapy management engine 114 continues monitoring at least the potassium levels of the host to determine the efficacy of the therapy management guidance.

[0250] If, upon further monitoring of at least the potassium levels of the host, the therapy management engine 114 determines that the patient’s potassium levels have stabilized or increased to within range for the host, the therapy management engine 114 can generate therapy management guidance for the host at block 522 including a recommendation to maintain the decreased dose or dose frequency of the therapy for their disease state, and / or to stop administering the potassium supplement.

[0251] In certain embodiments, at block 522, the therapy management engine 114 receives and utilizes current blood pressure data and / or other non-analyte sensor data of the host to determine additional risks associated with continuing, or maintaining, the therapy for the disease state of the host. For example, as described above, such additional risks can include changes in blood pressure. In certain embodiments, the additional risks include risks associated with fluid management, particularly in embodiments where the therapy for the disease state includes a diuretic. In such embodiments, the non-analyte sensor data received by the therapy management engine 114 at block 522 can include transthoracic impedance data, and / or other data measured by a cardiovascular implantable electronic device (CIED) that indicates fluid levels of the host. Based on the additional risks, the therapy management engine 114 can generate therapy management guidance for the host including a recommendation to downtitrate the therapy for their disease state in order to mitigate the additional risks and / or stabilize potassium levels of the host, and / or can generate other therapy management guidance based on the determined additional risks associated with continuing the therapy for the disease state.

[0252] In certain embodiments, when determining that the high or low potassium levels of the host are caused by the therapy administered by the host to treat the disease state of the host, the therapy management engine 114 first rules out other confounding factors that could have caused or contributed to the increasing potassium levels. For example, the therapymanagement engine 114 can first determine that the out-of-range potassium levels are not caused by, for example, exercise performed by the host, such as high intensity exercise, excessive sweating of the host, consumption of a potassium supplement, vomiting by the host, diarrhea, consumption of a laxative, or the like. Once such confounding factors are ruled out, the therapy management engine 114 can then determine that the out-of-range potassium levels of the host are indeed caused by the therapy administered by the host, and can proceed to provide guidance related to therapy management.

[0253] Upon performance of block 522, in certain embodiments, the therapy management engine 114 returns to block 504 to continue monitoring at least the host’s potassium levels.

[0254] Referring now to block 510, upon determining that the potassium rate of change of the host is greater than the threshold potassium rate of change at block 506, the therapy management engine 114 further monitors at least the host’s current potassium levels over a specified time period to determine whether the host’s potassium levels return to a potassium baseline of the host after the episode of significant potassium level change / fluctuation. If, at block 510, the therapy management engine 114 determines that the potassium levels of the host return to baseline by / at the end of the specified time period, the therapy management engine 114 performs block 524. If the therapy management engine 114 determines that the potassium levels of the host do not return to baseline by / at the end of the specified time period, the therapy management engine 114 performs block 524

[0255] In certain embodiments, the time period at block 510 has a duration of 1 or more seconds, minutes, hours, days, weeks, months, or more. For example, in certain embodiments, the time period at block 510 has a duration between about 30 seconds and about 15 minutes, or more. For example, in certain embodiments, the time period at block 510 has a duration of about 1 minute, about 2 minutes, about 3 minutes, about 4 minutes, about 5 minutes, about 6 minutes, about 7 minutes, about 8 minutes, about 9 minutes, about 10 minutes, or more. At block 524, upon determining that the potassium levels of the host have returned to baseline, the therapy management engine 114 continues monitoring at least the host’s current potassium levels to determine if the host experiences additional fluctuation in the host’s potassium rate of change or potassium levels. In certain embodiments, the therapy management engine 114 monitors, and / or the therapy management system 100 measures, at least the host’s potassium levels with increased frequency at block 524. In certain embodiments, the frequency ofpotassium and other analyte measurements by the therapy management system 100 at block 524 is increased to about once every 5 minutes, 10 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 24 hours, or more.

[0256] In certain embodiments, at block 524, the therapy management engine 114 determines if the previous episode of significant potassium level change / fluctuation of the host was caused or influenced by one or more external factors. Examples of external factors include medications, physical activity, meal consumption, and other events or circumstances that could cause the host’s potassium levels to increase or decrease temporarily. In certain embodiments, the therapy management engine 114 receives and utilizes, in addition to potassium data, other inputs such as analyte sensor data and non-analyte sensor data of the hose to determine one or more external factors that influenced or caused the previous episode of significant potassium level change / fluctuation of the host. For example, a host can experience a fast positive (increasing) potassium rate of change followed by a sharp decrease in potassium levels during exercise. Specifically, if a host is exercising, the host can initially experience a rise in potassium levels up to as much as 8 mEq / L as their muscles release potassium during the exercise. This level of potassium can be safe as long as potassium levels immediately fall following completion of the exercise session. Based on these potassium metrics, and / or in addition to other analyte and non-analyte sensor data, such as accelerometer data and / or heart rate data, the therapy management engine 114 can determine that the host is exercising, and that this exercise influenced or caused the increase and decrease in potassium levels of the hose.

[0257] In certain embodiments, if the therapy management engine 114 determines that the host’s potassium levels are impacted by one or more external factors, the therapy management engine 114 provides therapy management guidance including a recommendation for the host to take action to modify the external factor in order to reduce the impact on the host’ s potassium levels. For example, the therapy management engine 114 can recommend the host to adjust a medication dose, change a type of medication, halt physical activity or exercise, or perform some other action to mitigate the impact of an external factor on the host’s potassium levels.

[0258] Upon performance of block 524, the therapy management engine 114 returns to block 504 to continue monitoring at least the host’s potassium levels.

[0259] Referring now to block 526, upon determining that the potassium levels of the host have not returned to baseline by / at the end of the specified time period at block 510, the therapy management engine 114 determines whether the host’s current potassium levels are at or near a threshold potassium level. In certain embodiments, the threshold potassium level includes a predetermined upper threshold above which a measurement of potassium levels is indicative of a current or impending potassium imbalance for the host. In certain embodiments, the threshold potassium level includes a predetermined lower threshold below which a measurement of potassium levels is indicative of a current or impending potassium imbalance for the host.

[0260] In certain embodiments, to optimize the threshold potassium level for the host, the threshold potassium level is determined based on historical data of the host, such as historical potassium data of the host. In certain embodiments, in addition to or as an alternative to historical data of the host, the threshold potassium level is determined based on historical population-based data of patients with similar medication information (e.g., taking similar medications and / or doses thereof), and / or patients with similar demographic information, and / or patients with similar physiological information, and / or patients with similar disease information.

[0261] If, at block 526, the therapy management engine 114 determines that the potassium levels of the host are at or near the threshold potassium level, the therapy management engine 114 performs block 528. If the therapy management engine 114 determines that the potassium levels of the host are not at or near the threshold potassium level, the therapy management engine 114 performs block 530.

[0262] At block 528, upon determining that the potassium levels of the host are at or near the threshold potassium level at block 526, the therapy management engine 114 can generate therapy management guidance for the host including a recommendation to seek immediate medical attention (e.g., in an emergency healthcare setting). A fast negative (decreasing) potassium rate of change for the host, in combination with the host’s potassium levels approaching or going below a lower threshold potassium level, can indicate a serious health problem for the host, such as kidney or liver trauma, or rhabdomyolysis, which all require immediate medical attention. Similarly, a fast positive (increasing) potassium rate of change for the host, in combination with the host’s potassium levels approaching or surpassing anupper threshold potassium level, can also indicate a serious health problem for the host, such as ischemic heart disease, kidney or liver trauma, chromic kidney disease, heart failure, arrhythmia, inherited or acquired long QT-syndrome, or rhabdomyolysis, which all require immediate medical attention. Accordingly, at block 528, based upon the determination that the host’s potassium levels are at or approaching the threshold potassium level, the therapy management engine 114 can determine that the host is experiencing a serious health problem, and can generate a recommendation for the host to seek immediate medical attention to address the serious health problem.

[0263] At block 530, upon determining that the potassium levels of the host are not at or near the threshold potassium level at block 526, therapy management engine 114 continues monitoring at least the host’s potassium levels to determine if the host experiences additional fluctuation in the host’s potassium rate of change or potassium levels. In certain embodiments, the therapy management engine 114 monitors, and / or the therapy management system 100 measures, at least the host’s potassium levels with increased frequency at block 530. In certain embodiments, the frequency of potassium and other analyte measurements by the therapy management system 100 at block 530 is increased to about once every 5 minutes, 10 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 24 hours, or more.

[0264] Upon performance of block 530, the therapy management engine 114 returns to block 504 to continue monitoring at least the host’s potassium levels.

[0265] In certain embodiments, the therapy management guidance of workflow 500 includes recommendations encouraging the host to take a specific action. As described above, a specific action can include altering activities or making lifestyle changes, adjusting medication doses and / or medication types, or enacting countermeasures, such as alternative medications, to control the host’s potassium levels.

[0266] In certain embodiments of the workflow 500, in addition to or as an alternative to generating and providing the host with a recommendation to modify or maintain medication administration parameters, the therapy management engine 114 can wirelessly communicate with a medical device 208, such as a medication pump or dosing mechanism, to automatically adjust an administration of one or more medications (e.g., dose, medication type, timing, etc.) based on the therapy management guidance.

[0267] In certain embodiments, the therapy management guidance of workflow 500 is determined using a variety of models, such as a machine-learning model, a rules-based algorithm, and the like. In a rules-based model, various rules can be defined around a set of parameters, including the host’s potassium levels, other analyte data, non-analyte data, host input data, as well as other parameters. Host input data can include data relating to one or more meals consumed by the host, and / or information relating to activities completed by the host, such as exercise.

[0268] In one particular example of a rules-based model, a rule dictates that if a host is taking a RAASi medication or other medication affecting potassium levels, and the host is experiencing a fast potassium rate of change, then the therapy management system will recommend to the host to seek medical intervention for liver disease, kidney disease, bleeding, hemolysis, rhabdomyolysis, and / or a potential liver or kidney injury. In another example of a rules-based model, a rule dictates that if a host is taking a RAASi medication or other medication affecting potassium levels, and the host is experiencing a slow rise in potassium levels over a specified time period (e.g., when compared with the host’s historical data and / or historical population-based data), then the therapy management system will prescribe the host a potassium binder medication and continue monitoring the hosts’ potassium levels.

[0269] FIG. 6 is a flow diagram depicting a method 600 for training machine learning models to determine a risk or presence of a potassium imbalance for a host, and / or provide optimal therapy management for treating a disease state of the host while maintaining or stabilizing potassium levels. In certain embodiments, the method 600 is used to train models to optimize therapy for a host with HF.

[0270] Method 600 begins, at block 602, by a training server system, such as training server system 140 illustrated in FIG. 1, retrieving data from a historical records database, such as historical records database 112 illustrated in FIG. 1. As mentioned herein, historical records database 112 can provide a repository of up-to-date information and historical information for hosts of a continuous analyte monitoring system and connected mobile health application, such as hosts of continuous analyte monitoring system 104 and application 106 illustrated in FIG.1, as well as data for one or more patients who are not, or were not previously, hosts of continuous analyte monitoring system 104 and / or application 106.

[0271] Retrieval of data from historical records database 112 by training server system 140, at block 602, can include the retrieval of all, or any subset of, information maintained by historical records database 112. For example, where historical records database 112 stores information for 100,000 patients (e.g., non-hosts and hosts of continuous analyte monitoring system 104 and application 106), data retrieved by training server system 140 to train one or more machine learning models can include information for all 100,000 patients or only a subset of the data for those patients, e.g., data associated with only 50,000 patients or only data from the last ten years.

[0272] As an illustrative example, integrating with on premises or cloud based medical record databases through Fast Healthcare Interoperability Resources (FHIR), web application programming interfaces (APIs), Health Level 7 (HL7), and or other computer interface language can enable aggregation of healthcare historical records for baseline assessment in addition to the aggregation of de-identifiable patient data from a cloud based repository. Similarly, when integrating into the medical record databases, the integration can be accomplished by directly interfacing with the electronic medical record (EMR) system or through one or more intermediary systems (e.g., an interface engine, etc.).

[0273] As an illustrative example, at block 602, training server system 140 can retrieve information for 100,000 patients with various diseases or conditions, and / or prescribed medications and / or other therapies, stored in historical records database 112 to train a model to optimize one or more exercise sessions for the host and provide feedback to the host. Each of the 100,000 patients can have a corresponding data record (e.g., based on their corresponding host profile), stored in historical records database 112. Each host profile 118 can include information, such as information discussed with respect to FIG. 3.

[0274] The training server system 140 then uses information in each of the records to train an artificial intelligence or ML model (for simplicity referred to as “ML model” herein). Examples of types of information included in a patient’ s host profile were provided above. The information in each of these records can be featurized (e.g., manually or by training server system 140), resulting in features that can be used as input features for training the ML model. For example, a patient record can include or be used to generate features related to the patient’ s demographic information (e.g., an age of a patient, a gender of the patient, etc.), analyte information, such as potassium metrics, and / or any other data points in the patient record (e.g.,inputs 130, metrics 132, etc.). Features used to train the machine learning model(s) can vary in different embodiments.

[0275] In certain embodiments, each historical patient record retrieved from historical records database 112 is further associated with a label indicating disease state, optimal therapy parameters for facilitating potassium homeostasis while treating the disease state, etc. What the record is labeled with would depend on what the model is being trained to predict.

[0276] At block 604, method 600 continues by training server system 140 training one or more machine learning models based on the features and labels associated with the historical patient records. In some embodiments, the training server does so by providing the features as input into a model. This model can be a new model initialized with random weights and parameters, or can be partially or fully pre-trained (e.g., based on prior training rounds). Based on the input features, the model-in-training generates some output. In certain embodiments, the output can include an indication of a host’ s risk or presence of potassium imbalance, therapy parameters to optimize a host’s therapy based on the risk or presence of the potassium imbalance, and / or feedback to the host on the effects of one or more therapies, recommendations for future therapies, other lifestyle recommendations, or similar outputs. Note that the output could be in the form of a notification, a recommendation, and / or other types of output.

[0277] In certain embodiments, training server system 140 compares this generated output with the actual label associated with the corresponding historical patient record to compute a loss based on the difference between the actual result and the generated result. This loss is then used to refine one or more internal weights and parameters of the model (e.g., via backpropagation) such that the model learns to optimize a host’s therapy parameters and provide feedback to the host more accurately.

[0278] One of a variety of machine learning algorithms can be used for training the model(s) described above. For example, one of a supervised learning algorithm, a neural network algorithm, a deep neural network algorithm, a deep learning algorithm, etc. can be used.

[0279] At block 606, training server system 140 deploys the trained model(s) to make predictions associated with optimizing therapies for a host while maintaining or stabilizing potassium levels during runtime. In some embodiments, this includes transmitting some indication of the trained model(s) (e.g., a weights vector) that can be used to instantiate themodel(s) on another device. For example, training server system 140 can transmit the weights of the trained model(s) to therapy management engine 114, which could execute on display device 107, exercise machine 108, etc. The model(s) can then be used to determine, in realtime, treatment parameters to optimize therapy of a host using application 106, and / or make other types of recommendations discussed above. In certain embodiments, the training server system 140 can continue to train the model(s) in an “online” manner by using input features and labels associated with new patient records.

[0280] Further, similar methods for training illustrated in FIG. 6 using historical patient records can also be used to train models using patient-specific records to create more personalized models for making predictions associated with optimized therapy management guidance for the host. For example, a model trained using historical patient records that is deployed for a particular host, can be further re-trained after deployment. For example, the model can be re-trained after the model is deployed for a specific patient to create a more personalized model for the patient. The more personalized model can be able to more accurately make therapy optimization suggestions for the patient based on the patient’s own data (as opposed to only historical patient record data), including the patient’s own inputs 130 and metrics 132.

[0281] FIG. 7 is a block diagram depicting a computing device 700 configured to execute a therapy management engine (e.g., therapy management engine 114), according to certain embodiments disclosed herein. Although depicted as a single physical device, in embodiments, computing device 700 is implemented using virtual device(s), and / or across a number of devices, such as in a cloud environment. As illustrated, computing device 700 includes a processor 705, memory 710, storage 715, a network interface 725, and one or more VO interfaces 720. In the illustrated embodiment, processor 705 retrieves and executes programming instructions stored in memory 710, as well as stores and retrieves application data (e.g., application data 128) residing in storage 715. Processor 705 is generally representative of a single CPU and / or GPU, multiple CPUs and / or GPUs, a single CPU and / or GPU having multiple processing cores, and the like. Memory 710 is generally included to be representative of a random-access memory. Storage 715 can be any combination of disk drives, flash-based storage devices, and the like, and can include fixed and / or removable storagedevices, such as fixed disk drives, removable memory cards, caches, optical storage, network attached storage (NAS), or storage area networks (SAN).

[0282] In some embodiments, input and output (I / O) devices 735 (such as keyboards, monitors, etc.) can be connected via the I / O interface(s) 720. Further, via network interface 725, computing device 700 can be communicatively coupled with one or more other devices and components, such as host database 110. In certain embodiments, computing device 700 is communicatively coupled with other devices via a network, which can include the Internet, local network(s), and the like. The network can include wired connections, wireless connections, or a combination of wired and wireless connections. As illustrated, processor 705, memory 710, storage 715, network interface(s) 725, and VO interface(s) 720 are communicatively coupled by one or more interconnects 730. In certain embodiments, computing device 700 is representative of display device 107 associated with the host. In certain embodiments, as discussed above, the display device 107 can include the host’s laptop, computer, smartphone, and the like. In another embodiment, computing device 700 is a server executing in a cloud environment.

[0283] In the illustrated embodiment, storage 715 includes host profile 118. Memory 710 includes therapy management engine 114, which itself includes DAM 116.

[0284] As described above, continuous analyte monitoring system 104, described in relation to FIG. 1, can be a multi- analyte sensor system including a multi-analyte sensor. FIGs. SA-16 describe example multi-analyte sensors used to measure multiple analytes.

[0285] The phrases “analyte-measuring device,” “analyte-monitoring device,” “analytesensing device,” and / or “multi-analyte sensor device” as used herein are broad phrases, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to an apparatus and / or system responsible for the detection of, or transduction of a signal associated with, a particular analyte or combination of analytes. For example, these phrases may refer without limitation to an instrument responsible for detection of a particular analyte or combination of analytes. In one example, the instrument includes a sensor coupled to circuitry disposed within a housing, and configure to process signals associated with analyte concentrations into information. In one example, such apparatuses and / or systems are capable of providing specific quantitative, semi-quantitative, qualitative, and / or semi qualitativeanalytical information using a biological recognition element combined with a transducing (detecting) element.

[0286] The terms “biosensor” and / or “sensor” as used herein are broad terms and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to a part of an analyte measuring device, analyte-monitoring device, analyte sensing device, and / or multianalyte sensor device responsible for the detection of, or transduction of a signal associated with, a particular analyte or combination of analytes. In one example, the biosensor or sensor generally comprises a body, a working electrode, a reference electrode, and / or a counter electrode coupled to body and forming surfaces configured to provide signals during electrochemically reactions. One or more membranes can be affixed to the body and cover electrochemically reactive surfaces. In one example, such biosensors and / or sensors are capable of providing specific quantitative, semi-quantitative, qualitative, semi qualitative analytical signals using a biological recognition element combined with a transducing (detecting) element.

[0287] The phrases “sensing portion,” “sensing membrane,” and / or “sensing mechanism” as used herein are broad phrases, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to the part of a biosensor and / or a sensor responsible for the detection of, or transduction of a signal associated with, a particular analyte or combination of analytes. In one example, the sensing portion, sensing membrane, and / or sensing mechanism generally comprise an electrode configured to provide signals during electrochemically reactions with one or more membranes covering electrochemically reactive surface. In one example, such sensing portions, sensing membranes, and / or sensing mechanisms can provide specific quantitative, semi-quantitative, qualitative, semi qualitative analytical signals using a biological recognition element combined with a transducing (detecting) element.

[0288] The phrases “biointerface membrane” and “biointerface layer” as used interchangeably herein are broad phrases, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to a permeable membrane (which caninclude multiple domains) or layer that functions as a bioprotective interface between patient tissue and an implantable device. The terms “biointerface” and “bioprotective” are used interchangeably herein.

[0289] The term “cofactor” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to one or more substances whose presence contributes to or is required for analyte-related activity of an enzyme. Analyte-related activity can include, but is not limited to, any one of or a combination of binding, electron transfer, and chemical transformation. Cofactors are inclusive of coenzymes, non-protein chemical compounds, metal ions and / or metal organic complexes. Coenzymes are inclusive of prosthetic groups and co-substrates.

[0290] The term “continuous” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an uninterrupted or unbroken portion, domain, coating, or layer.

[0291] The phrases “continuous analyte sensing” and “continuous multi-analyte sensing” as used herein are broad phrases, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to the period in which monitoring of analyte concentration is continuously, continually, and / or intermittently (but regularly) performed, for example, from about every second or less to about one week or more. In further examples, monitoring of analyte concentration is performed from about every 2, 3, 5, 7,10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 seconds to about every 1.25, 1.50, 1.75, 2.00, 2.25, 2.50, 2.75, 3.00, 3.25, 3.50, 3.75, 4.00, 4.25, 4.50, 4.75, 5.00, 5.25, 5.50, 5.75, 6.00, 6.25, 6.50, 6.75, 7.00, 7.25, 7.50, 7.75, 8.00, 8.25, 8.50, 8.75, 9.00, 9.25, 9.50 or 9.75 minutes. In further examples, monitoring of analyte concentration is performed from about 10, 20, 30, 40 or 50 minutes to about every 1, 2, 3, 4, 5, 6, 7 or 8 hours. In further examples, monitoring of analyte concentration is performed from about every 8 hours to about every 12, 16, 20, or 24 hours. In further examples, monitoring of analyte concentration is performed from about every day to about every 1.5, 2, 3, 4, 5, 6, or 7 days. In further examples, monitoring of analyte concentration is performed from about every week to about every 1.5, 2, 3 or more weeks.

[0292] The term “coaxial” as used herein is to be construed broadly to include sensor architectures having elements aligned along a shared axis around a core that can be configured to have a circular, elliptical, triangular, polygonal, or other cross-section such elements can include electrodes, insulating layers, or other elements that can be positioned circumferentially around the core layer, such as a core electrode or core polymer wire.

[0293] The term “coupled” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to two or more system elements or components that are configured to be at least one of electrically, mechanically, thermally, operably, chemically or otherwise attached. For example, an element is “coupled” if the element is covalently, communicatively, electrostatically, thermally connected, mechanically connected, magnetically connected, or ionically associated with, or physically entrapped, adsorbed to or absorbed by another element. Similarly, the phrases “operably connected,” “operably linked,” and “operably coupled” as used herein may refer to one or more components linked to another component(s) in a manner that facilitates transmission of at least one signal between the components. In some examples, components are part of the same structure and / or integral with one another as in covalently, electrostatically, mechanically, thermally, magnetically, ionically associated with, or physically entrapped, or absorbed (i.e. “directly coupled” as in no intervening element(s)). In other examples, components are connected via remote means. For example, one or more electrodes can be used to detect an analyte in a sample and convert that information into a signal; the signal can then be transmitted to an electronic circuit. In this example, the electrode is “operably linked” to the electronic circuit. The phrase “removably coupled” as used herein may refer to two or more system elements or components that are configured to be or have been electrically, mechanically, thermally, operably, chemically, or otherwise attached and detached without damaging any of the coupled elements or components. The phrase “permanently coupled” as used herein may refer to two or more system elements or components that are configured to be or have been electrically, mechanically, thermally, operably, chemically, or otherwise attached but cannot be uncoupled without damaging at least one of the coupled elements or components covalently, electrostatically, ionically associated with, or physically entrapped, or absorbed.

[0294] The term “discontinuous” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to disconnected, interrupted, or separated portions, layers, coatings, or domains.

[0295] The term “distal” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a region spaced relatively far from a point of reference, such as an origin or a point of attachment.

[0296] The term “domain” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a region of a membrane system that can be a layer, a uniform or non-uniform gradient (for example, an anisotropic region of a membrane), or a portion of a membrane that is capable of sensing one, two, or more analytes. The domains discussed herein can be formed as a single layer, as two or more layers, as pairs of bi-layers, or as combinations thereof.

[0297] The term “electrochemically reactive surface” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to the surface of an electrode where an electrochemical reaction takes place. In one example this reaction is faradaic and results in charge transfer between the surface and its environment. In one example, hydrogen peroxide produced by an enzyme-catalyzed reaction of an analyte being oxidized on the surface results in a measurable electronic current. For example, in the detection of glucose, glucose oxidase produces hydrogen peroxide (H2O2) as a byproduct. The H2O2 reacts with the surface of the working electrode to produce two protons (2H+), two electrons (2e-) and one molecule of oxygen (02), which produces the electronic current being detected. In a counter electrode, a reducible species, for example, 02 is reduced at the electrode surface so as to balance the current generated by the working electrode.

[0298] The term "electrolysis" as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meeting), and refers without limitation to electrooxidation orelectroreduction (collectively, “redox”) of a compound, either directly or indirectly, by one or more enzymes, cofactors, or mediators.

[0299] The terms “indwelling,” “in dwelling,” “implanted,” or “implantable” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to objects including sensors that are inserted, or configured to be inserted, subcutaneously (i.e. in the layer of fat between the skin and the muscle), intracutaneously (i.e. penetrating the stratum corneum and positioning within the epidermal or dermal strata of the skin), or transcutaneously (i.e. penetrating, entering, or passing through intact skin), which may result in a sensor that has an in vivo portion and an ex vivo portion. The term “indwelling” also encompasses an object which is configured to be inserted subcutaneously, intracutaneously, or transcutaneously, whether or not it has been inserted as such.

[0300] The terms “interferants” and “interfering species” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to effects and / or species that interfere with the measurement of an analyte of interest in a sensor to produce a signal that does not accurately represent the analyte measurement. In one example of an electrochemical sensor, interfering species are compounds which produce a signal that is not analyte- specific due to a reaction on an electrochemically active surface.

[0301] The term “in vivo” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and without limitation is inclusive of the portion of a device (for example, a sensor) adapted for insertion into and / or existence within a living body of a patient.

[0302] The term “ex vivo” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and without limitation is inclusive of a portion of a device (for example, a sensor) adapted to remain and / or exist outside of a living body of a patient.

[0303] The term “ion” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an atom or molecule with a net electric charge due to the loss or gain of one or more electrons. Ions in a biological fluid may bereferred to as “electrolytes.” Nonlimiting examples of ions in biological fluids include sodium (Na+), potassium (K+), magnesium (Mg2+), calcium (Ca2+), hydrogen (H+), lithium (Li+), chloride (C1-), sulfide (S2-), sulfite (SO32-), sulfate (SO42-), phosphate (PO43-), and ammonium (NH4+). An ion is an example of an analyte.

[0304] The term and phrase “mediator” and “redox mediator” as used herein are broad terms and phrases, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to any chemical compound or collection of compounds capable of electron transfer, either directly, or indirectly, between an analyte, analyte precursor, analyte surrogate, analyte-reduced or analyte-oxidized enzyme, or cofactor, and an electrode surface held at a potential. In one example the mediator accepts electrons from, or transfer electrons to, one or more enzymes or cofactors, and / or exchanges electrons with the sensor system electrodes. In one example, mediators are transition-metal coordinated organic molecules which are capable of reversible oxidation and reduction reactions. In other examples, mediators may be organic molecules or metals which are capable of reversible oxidation and reduction reactions.

[0305] The term “membrane” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a structure configured to perform functions including, but not limited to, protection of the exposed electrode surface from the biological environment, diffusion resistance (limitation) of the analyte, service as a matrix for a catalyst (e.g., one or more enzymes) for enabling an enzymatic reaction, limitation or blocking of interfering species, provision of hydrophilicity at the electrochemically reactive surfaces of the sensor interface, service as an interface between patient tissue and the implantable device, modulation of patient tissue response via drug (or other substance) release, and combinations thereof. When used herein, the terms “membrane” and “matrix” are meant to be interchangeable.

[0306] The phrase “membrane system” as used herein is a broad phrase, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a permeable or semi-permeable membrane that can be comprised of two or more domains, layers, or layers within a domain, and is typically constructed of materials of a few microns thickness or more,which is permeable to oxygen and is optionally permeable to, e.g., glucose or another analyte. In one example, the membrane system comprises an enzyme, which enables an analyte reaction to occur whereby a concentration of the analyte can be measured.

[0307] The term “planar” as used herein is to be interpreted broadly to describe sensor architecture having a substrate including at least a first surface and an opposing second surface, and for example, comprising a plurality of elements arranged on one or more surfaces or edges of the substrate. The plurality of elements can include conductive or insulating layers or elements configured to operate as a circuit. The plurality of elements may or may not be electrically or otherwise coupled. In one example, planar includes one or more edges separating the opposed surfaces.

[0308] The term “proximal” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to the spatial relationship between various elements in comparison to a particular point of reference. For example, some examples of a device include a membrane system having a biointerface layer and an enzyme domain or layer. If the sensor is deemed to be the point of reference and the enzyme domain is positioned nearer to the sensor than the biointerface layer, then the enzyme domain is more proximal to the sensor than the biointerface layer.

[0309] The phrases “sensing portion,” “sensing membrane,” and / or “sensing mechanism” as used herein are broad phrases, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to the part of a biosensor and / or a sensor responsible for the detection of, or transduction of a signal associated with, a particular analyte or combination of analytes. In one example, the sensing portion, sensing membrane, and / or sensing mechanism generally comprise an electrode configured to provide signals during electrochemically reactions with one or more membranes covering electrochemically reactive surface. In one example, such sensing portions, sensing membranes, and / or sensing mechanisms are capable of providing specific quantitative, semi-quantitative, qualitative, semi qualitative analytical signals using a biological recognition element combined with a transducing and / or detecting element.

[0310] During general operation of the analyte measuring device, biosensor, sensor, sensing region, sensing portion, or sensing mechanism, a biological sample, for example, blood or interstitial fluid, or a component thereof contacts, either directly, or after passage through one or more membranes, an enzyme, for example, glucose oxidase, DNA, RNA, or a protein or aptamer, for example, one or more periplasmic binding protein (PBP) or mutant or fusion protein thereof having one or more analyte binding regions, each region capable of specifically or reversibly binding to and / or reacting with at least one analyte. The interaction of the biological sample or component thereof with the analyte measuring device, biosensor, sensor, sensing region, sensing portion, or sensing mechanism results in transduction of a signal that permits a qualitative, semi-qualitative, quantitative, or semi-qualitative determination of the analyte level, for example, glucose, ketone, lactate, potassium, etc., in the biological sample.

[0311] In one example, the sensing region or sensing portion can comprise at least a portion of a conductive substrate or at least a portion of a conductive surface, for example, a wire (coaxial) or conductive trace or a substantially planar substrate including substantially planar trace(s), and a membrane. In one example, the sensing region or sensing portion can comprise a non-conductive body, a working electrode, a reference electrode, and a counter electrode (optional), forming an electrochemically reactive surface at one location on the body and an electronic connection at another location on the body, and a sensing membrane affixed to the body and covering the electrochemically reactive surface. In some examples, the sensing membrane further comprises an enzyme domain, for example, an enzyme domain, and an electrolyte phase, for example, a free-flowing liquid phase comprising an electrolytecontaining fluid described further below. The terms are broad enough to include the entire device, or only the sensing portion thereof (or something in between).

[0312] In another example, the sensing region can comprise one or more periplasmic binding protein (PBP) including mutant or fusion protein thereof, or aptamers having one or more analyte binding regions, each region capable of specifically and reversibly binding to at least one analyte. Alterations of the aptamer or mutations of the PBP can contribute to or alter one or more of the binding constants, long-term stability of the protein, including thermal stability, to bind the protein to a special encapsulation matrix, membrane or polymer, or to attach a detectable reporter group or “label” to indicate a change in the binding region or transduce a signal corresponding to the one or more analytes present in the biological fluid.Specific examples of changes in the binding region include, but are not limited to, hydrophobic / hydrophilic environmental changes, three-dimensional conformational changes, changes in the orientation of amino / nucleic acid side chains in the binding region of proteins, and redox states of the binding region. Such changes to the binding region provide for transduction of a detectable signal corresponding to the one or more analytes present in the biological fluid.

[0313] In one example, the sensing region determines the selectivity among one or more analytes, so that only the analyte which has to be measured leads to (transduces) a detectable signal. The selection can be based on any chemical or physical recognition of the analyte by the sensing region, where the chemical composition of the analyte is unchanged, or in which the sensing region causes or catalyzes a reaction of the analyte that changes the chemical composition of the analyte.

[0314] The term “sensitivity” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an amount of signal (e.g., in the form of electrical current and / or voltage) produced by a predetermined amount (unit) of the measured analyte. For example, in one example, a sensor has a sensitivity (or slope) of from about 1 to about 100 picoAmps of current for every 1 mg / dL of analyte.

[0315] The phrases "signal medium" or "transmission medium" shall be taken to include any form of modulated data signal, carrier wave, and so forth. The phrase "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal.

[0316] The terms “transducing” or “transduction” and their grammatical equivalents as are used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refer without limitation to optical, electrical, electrochemical, acoustical / mechanical, or colorimetrical technologies and methods. Electrochemical properties include current and / or voltage, inductance, capacitance, impedance, transconductance, and potential. Optical properties include absorbance, fluorescence / phosphorescence, fluorescence / phosphorescence decay rate, wavelength shift, dual wave phase modulation, bio / chemiluminescence,reflectance, light scattering, and refractive index. For example, the sensing region transduces the recognition of analytes into a semi-quantitative or quantitative signal.

[0317] As used herein, the phrase “transducing element” as used herein is a broad phrase, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to analyte recognition moieties capable of facilitating, directly or indirectly, with detectable signal transduction corresponding to the presence and / or concentration of the recognized analyte. In one example, a transducing element is one or more enzymes, one or more aptamers, one or more ionophores, one or more capture antibodies, one or more proteins, one or more biological cells, one or more oligonucleotides, and / or one or more DNA or RNA moieties. Transcutaneous continuous multi-analyte sensors can be used in vivo over various lengths of time. The continuous multi-analyte sensor systems discussed herein can be transcutaneous devices, in that a portion of the device can be inserted through the patient's skin and into the underlying soft tissue while a portion of the device remains on the surface of the patient's skin. In one aspect, in order to overcome the problems associated with noise or other sensor function in the short-term, one example employs materials that promote formation of a fluid pocket around the sensor, for example architectures such as a porous biointerface membrane or matrices that create a space between the sensor and the surrounding tissue. In some examples, a sensor is provided with a spacer adapted to provide a fluid pocket between the sensor and the patient's tissue. It is believed that this spacer, for example a biointerface material, matrix, structure, and the like as described in more detail elsewhere herein, provides for oxygen and / or glucose transport to the sensor.Membrane Systems

[0318] Membrane systems disclosed herein are suitable for use with implantable devices in contact with a biological fluid. For example, the membrane systems can be utilized with implantable devices, such as devices for monitoring and determining analyte levels in a biological fluid, for example, devices for monitoring glucose levels for individuals having diabetes. In some examples, the analyte-measuring device is a continuous device. The analytemeasuring device can employ any suitable sensing element to provide the raw signal, including but not limited to those involving enzymatic, chemical, physical, electrochemical,spectrophotometric, amperometric, potentiometric, polarimetric, calorimetric, radiometric, immunochemical, or like elements.

[0319] Suitable membrane systems for the aforementioned multi-analyte systems and devices can include, for example, membrane systems disclosed in U.S. Pat. No. 6,015,572, U.S. Pat. No. 5,1264,745, and U.S. Pat. No. 6,083,523, which are incorporated herein by reference in their entireties for their teachings of membrane systems.

[0320] In general, the membrane system includes a plurality of domains, for example, an electrode domain, an interference domain, an enzyme domain, a resistance domain, and a biointerface domain. The membrane system can be deposited on the exposed electroactive surfaces using known thin film techniques (for example, vapor deposition, spraying, electrodepositing, dipping, brush coating, film coating, drop-let coating, and the like). Additional steps can be applied following the membrane material deposition, for example, drying, annealing, and curing (for example, UV curing, thermal curing, moisture curing, radiation curing, and the like) to enhance certain properties such as mechanical properties, signal stability, and selectivity. In a typical process, upon deposition of the resistance domain membrane, a biointerface / drug releasing layer having a “dry film” thickness of from about 0.05 micron (pm), or less, to about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 pm is formed. “Dry film” thickness refers to the thickness of a cured film cast from a coating formulation by standard coating techniques.

[0321] In certain examples, the biointerface / drug releasing layer is formed of a biointerface polymer, wherein the biointerface polymer comprises one or more membrane domains comprising polyurethane and / or polyurea segments and one or more zwitterionic repeating units. In some examples, the biointerface / drug releasing layer coatings are formed of a polyurethane urea having carboxyl betaine groups incorporated in the polymer and non-ionic hydrophilic polyethylene oxide segments, wherein the polyurethane urea polymer is dissolved in organic or non-organic solvent system according to a pre-determined coating formulation, and is crosslinked with an isocyanate crosslinker and cured at a moderate temperature of about 50° C. The solvent system can be a single solvent or a mixture of solvents to aid the dissolution or dispersion of the polymer. The solvents can be the ones selected as the polymerization media or added after polymerization is completed. The solvents are selected from the ones having lower boiling points to facilitate drying and to be lower in toxicity for implantapplications. Examples of these solvents include aliphatic ketone, ester, ether, alcohol, hydrocarbons, and the like. Depending on the final thickness of the biointerface / drug releasing layer and solution viscosity (as related to the percent of polymer solid), the coating can be applied in a single step or multiple repeated steps of the chosen process such as dipping to build the desired thickness. Yet in other examples, the bioprotective polymers are formed of a polyurethane urea having carboxylic acid groups and carboxyl betaine groups incorporated in the polymer and non-ionic hydrophilic polyethylene oxide segments, wherein the polyurethane urea polymer is dissolved in an organic or non-organic solvent system in a coating formulation, and is crosslinked with an a carbodiimide (e.g., l-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC)) and cured at a moderate temperature of about 50° C.

[0322] In other examples, the biointerface / drug releasing layer coatings are formed of a polyurethane urea having sulfobetaine groups incorporated in the polymer and non-ionic hydrophilic polyethylene oxide segments, wherein the polyurethane urea polymer is dissolved in an organic or non-organic solvent system according to a pre-determined coating formulation, and is crosslinked with an isocyanate crosslinker and cured at a moderate temperature of about 50° C. The solvent system can be a single solvent or a mixture of solvents to aid the dissolution or dispersion of the polymer. The solvents can be the ones selected as the polymerization media or added after polymerization is completed. The solvents are selected from the ones having lower boiling points to facilitate drying and to be lower in toxicity for implant applications. Examples of these solvents include aliphatic ketone, ester, ether, alcohol, hydrocarbons, and the like. Depending on the final thickness of the biointerface / drug releasing layer and solution viscosity (as related to the percent of polymer solid), the coating can be applied in a single step or multiple repeated steps of the chosen process such as dipping to build the desired thickness. Yet in other examples, the biointerface polymers are formed of a polyurethane urea having unsaturated hydrocarbon groups and sulfobetaine groups incorporated in the polymer and non-ionic hydrophilic polyethylene oxide segments, wherein the polyurethane urea polymer is dissolved in an organic or non-organic solvent system in a coating formulation, and is crosslinked in the presence of initiators with heat or irradiation including UV, LED light, electron beam, and the like, and cured at a moderate temperature of about 50° C. Examples of unsaturated hydrocarbon includes allyl groups, vinyl groups, acrylate, methacrylate, alkenes, alkynes, and the like.

[0323] In some examples, tethers are used. A tether is a polymer or chemical moiety which does not participate in the (electro)chemical reactions involved in sensing, but forms chemical bonds with the (electro)chemically active components of the membrane. In some examples these bonds are covalent. In one example, a tether can be formed in solution prior to one or more interlayers of a membrane being formed, where the tether bonds two (electro )chemic ally active components directly to one another or alternately, the tether(s) bond (electro)chemically active component(s) to polymeric backbone structures. In another example, (electro)chemically active components are comixed along with crosslinker(s) with tunable lengths (and optionally polymers) and the tethering reaction occurs as in situ crosslinking. Tethering can be employed to maintain a predetermined number of degrees of freedom of NAD(P)H for effective enzyme catalysis, where “effective” enzyme catalysis causes the analyte sensor to continuously monitor one or more analytes for a period of from about 5 days to about 15 days or more.Membrane Fabrication

[0324] Polymers can be processed by solution-based techniques such as spraying, dipping, casting, electrospinning, vapor deposition, spin coating, coating, and the like. Water-based polymer emulsions can be fabricated to form membranes by methods similar to those used for solvent-based materials. In both cases the evaporation of a volatile liquid (e.g., organic solvent or water) leaves behind a film of the polymer. Cross-linking of the deposited film or layer can be performed through the use of multi-functional reactive ingredients by a number of methods. The liquid system can cure by heat, moisture, high-energy radiation, ultraviolet light, or by completing the reaction, which produces the final polymer in a mold or on a substrate to be coated.

[0325] In some examples, the wetting property of the membrane (and by extension the extent of sensor drift exhibited by the sensor) can be adjusted and / or controlled by creating covalent cross-links between surface-active group-containing polymers, functional-group containing polymers, polymers with zwitterionic groups (or precursors or derivatives thereof), and combinations thereof. Cross-linking can have a substantial effect on film structure, which in turn can affect the film's surface wetting properties. Crosslinking can also affect the film's tensile strength, mechanical strength, water absorption rate and other properties.

[0326] Cross-linked polymers can have different cross-linking densities. In certain examples, cross-linkers are used to promote cross-linking between layers. In other examples, in replacement of (or in addition to) the cross-linking techniques described above, heat is used to form cross-linking. For example, in some examples, imide and amide bonds can be formed between two polymers as a result of high temperature. In some examples, photo cross-linking is performed to form covalent bonds between the polycationic layers(s) and polyanionic layer(s). One major advantage to photo-cross-linking is that it offers the possibility of patterning. In certain examples, patterning using photo-cross linking is performed to modify the film structure and thus to adjust the wetting property of the membranes and membrane systems, as discussed herein.

[0327] Polymers with domains or segments that are functionalized to permit cross-linking can be made by methods at least as discussed herein. For example, polyurethaneurea polymers with aromatic or aliphatic segments having electrophilic functional groups (e.g., carbonyl, aldehyde, anhydride, ester, amide, isocyano, epoxy, allyl, or halo groups) can be crosslinked with a crosslinking agent that has multiple nucleophilic groups (e.g., hydroxyl, amine, urea, urethane, or thiol groups). In further examples, polyurethaneurea polymers having aromatic or aliphatic segments having nucleophilic functional groups can be crosslinked with a crosslinking agent that has multiple electrophilic groups. Still further, polyurethaneurea polymers having hydrophilic segments having nucleophilic or electrophilic functional groups can be crosslinked with a crosslinking agent that has multiple electrophilic or nucleophilic groups. Unsaturated functional groups on the polyurethane urea can also be used for crosslinking by reacting with multivalent free radical agents. Non-limiting examples of suitable cross-linking agents include isocyanate, carbodiimide, glutaraldehyde, aziridine, silane, or other aldehydes, epoxy, acrylates, free-radical based agents, ethylene glycol diglycidyl ether (EGDE), polyethylene glycol) diglycidyl ether (PEGDE), or dicumyl peroxide (DCP). In one example, from about 0.1% to about 15% w / w of cross-linking agent is added relative to the total dry weights of cross-linking agent and polymers added when blending the ingredients. In another example, about 1% to about 10% w / w of cross-linking agent is added relative to the total dry weights of cross-linking agent and polymers added when blending the ingredients. In yet another example, about 5% to about 15% w / w of cross-linking agent is added relative to the total dry weights of cross-linking agent and polymers added when blending the ingredients. During the curing process, substantially all of the cross-linking agentis believed to react, leaving substantially no detectable unreacted cross-linking agent in the final film.

[0328] Polymers disclosed herein can be formulated into mixtures that can be drawn into a film or applied to a surface using methods such as spraying, self-assembling monolayers (SAMs), painting, dip coating, vapor depositing, molding, 3-D printing, lithographic techniques (e.g., photolithograph), micro- and nano-pipetting printing techniques, silk-screen printing, etc.). The mixture can then be cured under high temperature (e.g., from about 30° C to about 150° C.). Other suitable curing methods can include ultraviolet, e-beam, or gamma radiation, for example.

[0329] In some circumstances, using continuous multianalyte monitoring systems including sensor(s) configured with bioprotective and / or drug releasing membranes, it is believed that that foreign body response is the dominant event surrounding extended implantation of an implanted device and can be managed or manipulated to support rather than hinder or block analyte transport. In another aspect, in order to extend the lifetime of the sensor, one example employs materials that promote vascularized tissue ingrowth, for example within a porous biointerface membrane. For example, tissue in-growth into a porous biointerface material surrounding a sensor can promote sensor function over extended periods of time (e.g., weeks, months, or years). It has been observed that in-growth and formation of a tissue bed can take up to 3 weeks. Tissue ingrowth and tissue bed formation is believed to be part of the foreign body response. As will be discussed herein, the foreign body response can be manipulated by the use of porous bioprotective materials that surround the sensor and promote ingrowth of tissue and microvasculature over time.

[0330] Accordingly, a sensor as discussed in examples herein can include a biointerface layer. The biointerface layer, like the drug releasing layer, can include, but is not limited to, for example, porous biointerface materials including a solid portion and interconnected cavities, all of which are described in more detail elsewhere herein. The biointerface layer can be employed to improve sensor function in the long term (e.g., after tissue ingrowth).

[0331] Accordingly, a sensor as discussed in examples herein can include a drug releasing membrane at least partially functioning as or in combination with a biointerface membrane. The drug releasing membrane can include, for example, materials including a hard-soft segment polymer with hydrophilic and optionally hydrophobic domains, all of which aredescribed in more detail elsewhere herein, can be employed to improve sensor function in the long term (e.g., after tissue ingrowth). In one example, the materials including a hard-soft segment polymer with hydrophilic and optionally hydrophobic domains are configured to release a combination of a derivative form of dexamethasone or dexamethasone acetate with dexamethasone such that one or more different rates of release of the anti-inflammatory is achieved and the useful life of the sensor is extended. Other suitable drug releasing membranes of the present disclosure can be selected from silicone polymers, polytetrafluoroethylene, expanded polytetrafluoroethylene, polyethylene-co-tetrafluoroethylene, polyolefin, polyester, polycarbonate, biostable polytetrafluoroethylene, homopolymers, copolymers, terpolymers of polyurethanes, polypropylene (PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF), polyvinyl alcohol (PVA), poly vinyl acetate, ethylene vinyl acetate (EVA), polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA), polyether ether ketone (PEEK), polyamides, polyurethanes and copolymers and blends thereof, polyurethane urea polymers and copolymers and blends thereof, cellulosic polymers and copolymers and blends thereof, polyethylene oxide) and copolymers and blends thereof, polypropylene oxide) and copolymers and blends thereof, polysulfones and block copolymers thereof including, for example, di-block, tri-block, alternating, random and graft copolymers cellulose, hydrogel polymers, poly(2-hydroxyethyl methacrylate, pHEMA) and copolymers and blends thereof, hydroxyethyl methacrylate, (HEMA) and copolymers and blends thereof, polyacrylonitrilepolyvinyl chloride (PAN-PVC) and copolymers and blends thereof, acrylic copolymers and copolymers and blends thereof, nylon and copolymers and blends thereof, polyvinyl difluoride, poly anhydrides, poly(l-lysine), poly(L-lactic acid), hydroxyethylmetharcrylate and copolymers and blends thereof, and hydroxyapeptite and copolymers and blends thereof.Exemplary Multi-Analyte Sensor Membrane Configurations

[0332] Continuous multi-analyte sensors with various membrane configurations suitable for facilitating signal transduction corresponding to analyte concentrations, either simultaneously, intermittently, and / or sequentially are provided. In one example, such sensors can be configured using a signal transducer, comprising one or more transducing elements (“TL”). Such continuous multi-analyte sensor can employ various transducing means, for example, amperometry, voltametric, potentiometry, and impedimetric methods, among other techniques.

[0333] In one example, the transducing element comprises one or more membranes that can comprise one or more layers and or domains, each of the one or more layers or domains can independently comprise one or more signal transducers, e.g., enzymes, RNA, DNA, aptamers, binding proteins, etc. As used herein, transducing elements includes enzymes, ionophores, RNA, DNA, aptamers, binding proteins and are used interchangeably.

[0334] In one example, the transducing element is present in one or more membranes, layers, or domains formed over a sensing region. In one example, such sensors can be configured using one or more enzyme domains, e.g., membrane domains including enzyme domains, also referred to as EZ layers (“EZLs”), each enzyme domain can comprise one or more enzymes. Reference hereinafter to an “enzyme layer” is intended to include all or part of an enzyme domain, either of which can be all or part of a membrane system as discussed herein, for example, as a single layer, as two or more layers, as pairs of bi-layers, or as combinations thereof.

[0335] In one example, the continuous multi-analyte sensor uses one or more of the following analyte-substrate / enzyme pairs: for example, sarcosine oxidase in combination with creatinine amidohydrolase, creatine amidohydrolase being employed for the sensing of creatinine. Other examples of analytes / oxidase enzyme combinations that can be used in the sensing region include, for example, alcohol / alcohol oxidase, cholesterol / cholesterol oxidase, glactose:galactose / galactose oxidase, choline / choline oxidase, glutamate / glutamate oxidase, glycerol / glycerol-3phosphate oxidase (or glycerol oxidase), bilirubin / bilirubin oxidase, ascorbic / ascorbic acid oxidase, uric acid / uric acid oxidase, pyruvate / pyruvate oxidase, hypoxanthine:xanthine / xanthine oxidase, glucose / glucose oxidase, lactate / lactate oxidase, L-amino acid oxidase, and glycine / sarcosine oxidase. Other analyte-substrate / enzyme pairs can be used, including such analyte-substrate / enzyme pairs that comprise genetically altered enzymes, immobilized enzymes, mediator-wired enzymes, dimerized and / or fusion enzymes.NAD Based Multi-Analyte Sensor Platform

[0336] Nicotinamide adenine dinucleotide (NAD(P)+ / NAD(P)H) is a coenzyme, e.g., a dinucleotide that consists of two nucleotides joined through their phosphate groups. One nucleotide contains an adenine nucleobase and the other nicotinamide. NAD exists in two forms, e.g., an oxidized form (NAD(P)+) and reduced form (NAD(P)H) (H = hydrogen). Thereaction of NAD+ and NADH is reversible, thus, the coenzyme can continuously cycle between the NAD(P)+ / and NAD(P)H forms essentially without being consumed.

[0337] In one example, one or more enzyme domains of the sensing region of the presently disclosed continuous multi-analyte sensor device comprise an amount of NAD+ or NADH for providing transduction of a detectable signal corresponding to the presence or concentration of one or more analytes. In one example, one or more enzyme domains of the sensing region of the presently disclosed continuous multi-analyte sensor device comprise an excess amount of NAD+ or NADH for providing extended transduction of a detectable signal corresponding to the presence or concentration of one or more analytes.

[0338] In one example, NAD, NADH, NAD+, NAD(P)+, ATP, flavin adenine dinucleotide (FAD), magnesium (Mg++), pyrroloquinoline quinone (PQQ), and functionalized derivatives thereof can be used in combination with one or more enzymes in the continuous multi-analyte sensor device. In one example, NAD, NADH, NAD+, NAD(P)+, ATP, flavin adenine dinucleotide (FAD), magnesium (Mg++), pyrroloquinoline quinone (PQQ), and functionalized derivatives are incorporated in the sensing region. In one example, NAD, NADH, NAD+, NAD(P)+, ATP, flavin adenine dinucleotide (FAD), magnesium (Mg++), pyrroloquinoline quinone (PQQ), and functionalized derivatives are dispersed or distributed in one or more membranes or domains of the sensing region.

[0339] In one aspect of the present disclosure, continuous sensing of one or more or two or more analytes using NAD+ dependent enzymes is provided in one or more membranes or domains of the sensing region. In one example, the membrane or domain provides retention and stable recycling of NAD+ as well as mechanisms for transducing NADH oxidation or NAD+ reduction into measurable current with amperometry. In one example, described below, continuous, sensing of multi-analytes, either reversibly bound or at least one of which are oxidized or reduced by NAD+ dependent enzymes, for example, ketones (betahydroxybutyrate dehydrogenase), glycerol (glycerol dehydrogenase), cortisol (11 -hydroxysteroid dehydrogenase), glucose (glucose dehydrogenase), alcohol (alcohol dehydrogenase), aldehydes (aldehyde dehydrogenase), and lactate (lactate dehydrogenase) is provided. In other examples, described below, membranes are provided that enable the continuous, on-body sensing of multiple analytes which utilize FAD-dependent dehydrogenases, such as fatty acids (Acyl-CoA dehydrogenase).

[0340] Exemplary configurations of one or more membranes or portions thereof are an arrangement for providing retention and recycling of NAD+ are provided. Thus, an electrode surface of a conductive wire (coaxial) or a planar conductive surface is coated with at least one layer comprising at least one enzyme as depicted in FIG.8A. With reference to FIG. 8B, one or more optional layers can be positioned between the electrode surface and the one or more enzyme domains. For example, one or more interference domains (also referred to as “interferent blocking layer”) can be used to reduce or eliminate signal contribution from undesirable species present, or one or more electrodes (not shown) can used to assist with wetting, system equilibrium, and / or start up. As shown in FIGs. 8A-8B, one or more of the membranes provides a NAD+ reservoir domain providing a reservoir for NAD+. In one example, one or more interferent blocking membranes is used, and potentiostat is utilized to measure H2O2 production or 02 consumption of an enzyme such as or similar to NADH oxidase, the NAD+ reservoir and enzyme domain positions can be switched, to facilitate better consumption and slower unnecessary outward diffusion of excess NAD+. Exemplary sensor configurations can be found in U.S. Provisional Patent Application No. 63 / 321340, “CONTINUOUS ANALYTE MONITORING SENSOR SYSTEMS AND METHODS OF USING THE SAME,” filed March 18, 2022, and incorporated by reference in its entirety herein.

[0341] In one example, one or more mediators that are optimal for NADH oxidation are incorporated in the one or more electrode domains or enzyme domains. In one example, organic mediators, such as phenanthroline dione, or nitrosoanilines are used. In another example, metallo-organic mediators, such as ruthenium-phenanthroline-dione or osmium(bpy)2Cl, polymers containing covalently coupled organic mediators or organometallic coordinated mediators polymers for example polyvinylimidizole-Os(bpy)2Cl, or polyvinylpyridine-organometallic coordinated mediators (including rutheniumphenanthroline dione) are used. Other mediators can be used as discussed further below.

[0342] In humans, serum levels of beta-hydroxybutyrate (BHB) are usually in the low micromolar range but can rise up to about 6-8 mM. Serum levels of BHB can reach 1-2 mM after intense exercise or consistent levels above 2 mM are reached with a ketogenic diet that is almost devoid of carbohydrates. Other ketones are present in serum, such as acetoacetate and acetone, however, most of the dynamic range in ketone levels is in the form of BHB. Thus,monitoring of BHB, e.g., continuous monitoring is useful for providing health information to a patient or health care provider.

[0343] Another example of a continuous ketone analyte detection configuration employing electrode-associated mediator-coupled diaphorase / NAD+ / dehydrogenase is depicted below:

[0344] In one example, the diaphorase is electrically coupled to the electrode with organometallic coordinated mediator polymer. In another example, the diaphorase is covalently coupled to the electrode with an organometallic coordinated mediator polymer. Alternatively, multiple enzyme domains can be used in an enzyme layer, for example, separating the electrode-associated diaphorase (closest to the electrode surface) from the more distal adjacent NAD+ or the dehydrogenase enzyme, to essentially decouple NADH oxidation from analyte (ketone) oxidation. Alternatively, NAD+ can be more proximal to the electrode surface than an adjacent enzyme domain comprising the dehydrogenase enzyme. In one example, the NAD+ and / or HBDH are present in the same or different enzyme domain, and either can be immobilized, for example, using amine reactive crosslinker (e.g., glutaraldehyde, epoxides, NHS esters, imidoesters). In one example, the NAD+ is coupled to a polymer and is present in the same or different enzyme domain as HBDH. In one example, the molecular weight of NAD+ is increased to prevent or eliminate migration from the sensing region, for example the NAD+ is dimerized using its C6 terminal amine with any amine-reactive crosslinker. In one example, NAD+ can be covalently coupled to an aspect of the enzyme domain having a higher molecular weight than the NAD+ which can improve a stability profile of the NAD+, improving the ability to retain and / or immobilize the NAD+ in the enzyme domain. For example, dextran-NAD.

[0345] In one example, the sensing region comprises one or more NADH:acceptor oxidoreductases and one or more NAD-dependent dehydrogenases. In one example, sensing region comprises one or more NADH:acceptor oxidoreductases and one or more NAD(P)-dependent dehydrogenases with NAD(P)+ or NAD(P)H as cofactors present in sensing region. In one example, the sensing region comprises an amount of diaphorase.

[0346] In one example, a ketone sensing configuration suitable for combination with another analyte sensing configuration is provided. Thus, an EZL layer of about 1-20 um thick is prepared by presenting a EZL solution composition in lOmM HEPES in water having about 20uL 500mg / mL HBDH, about 20uL [500mg / mL NAD(P)H, 200mg / mL polyethylene glycol-diglycol ether (PEG-DGE) of about 400MW], about 20uL 500mg / mL diaphorase, about 40uL 250mg / mL poly vinyl imidazole- osmium bis(2,2'-bipyridine)chloride (PVI-Os(bpy)2Cl) to a substrate such as a working electrode, so as to provide, after drying, about 15-40% by weight HBDH, about 5-30% diaphorase about 5-30% NAD(P)H, about 10-50% PVI-Os(bpy)2Cl and about 1-12% PEG-DGE(400MW). The substrates discussed herein that can include working electrodes may be formed from gold, platinum, palladium, rhodium, iridium, titanium, tantalum, chromium, and / or alloys or combinations thereof, or carbon (e.g., graphite, glassy carbon, carbon nanotubes, graphene, or doped diamond, as well combinations thereof.

[0347] To the above enzyme domain was contacted a resistance domain, also referred to as a resistance layer (“RL”). In one example, the RL comprises about 55-100% PVP, and about 0.1-45% PEG-DGE. In another example, the RL comprises about 75-100% PVP, and about 0.3-25% PEG-DGE. In yet another example, the RL comprises about 85-100% PVP, and about 0.5-15% PEG-DGE. In yet another example, the RL comprises essentially 100% PVP.

[0348] The exemplary continuous ketone sensor as depicted in FIGs. 8A-8B comprising NAD(P)H reservoir domain is configured so that NAD(P)H is not rate-limiting in any of the enzyme domains of the sensing region. In one example, the loading of NAD(P)H in the NAD(P)H reservoir domain is greater than about 20%, 30%, 40% or 50% w / w. The one or more of the membranes or portions of one or more membrane domains (hereinafter also referred to as “membranes”) can also contain a polymer or protein binder, such as zwitterionic polyurethane, and / or albumin. Alternatively, in addition to NAD(P)H, the membrane can contain one or more analyte specific enzymes (e.g. HBDH, glycerol dehydrogenase, etc.), so that optionally, the NAD(P)H reservoir membrane also provides a catalytic function. In one example, the NAD(P)H is dispersed or distributed in or with a polymer(or protein), and can be crosslinked to an extent that still allows adequate enzyme / cofactor functionality and / or reduced NAD(P)H flux within the domain.

[0349] In one example, NADH oxidase enzyme alone or in combination with superoxide dismutase (SOD) is used in the one or more membranes of the sensing region. In one example, an amount of superoxide dismutase (SOD) is used that is capable of scavenging some or most of one or more free radicals generated by NADH oxidase. In one example, NADH oxidase enzyme alone or in combination with superoxide dismutase (SOD) is used in combination withNAD(P)H and / or a functionalized polymer with NAD(P)H immobilized onto the polymer from a C6 terminal amine in the one or more membranes of the sensing region.

[0350] In one example, the NAD(P)H is immobilized to an extent that maintains NAD(P)H catalytic functionality. In one example, dimerized NAD(P)H is used to entrap NAD(P)H within one or more membranes by crosslinking their respective C6 terminal amine together with appropriate amine-reactive crosslinker such as glutaraldehyde or PEG-DGE.

[0351] The aforementioned continuous ketone sensor configurations can be adapted to other analytes or used in combination with other sensor configurations. For example, analyte(s)-dehydrogenase enzyme combinations can be used in any of the membranes of the sensing region include; glycerol (glycerol dehydrogenase); cortisol (lip-hydroxy steroid dehydrogenase); glucose (glucose dehydrogenase); alcohol (alcohol dehydrogenase); aldehydes (aldehyde dehydrogenase); and lactate (lactate dehydrogenase).

[0352] In one example, a semipermeable membrane is used in the sensing region or adjacent thereto or adjacent to one or more membranes of the sensing region so as to attenuate the flux of at least one analyte or chemical species. In one example, the semipermeable membrane attenuates the flux of at least one analyte or chemical species so as to provide a linear response from a transduced signal. In another example, the semipermeable membrane prevents or eliminates the flux of NAD(P)H out of the sensing region or any membrane or domain. In one example, the semipermeable membrane can be an ion selective membrane selective for an ion analyte of interest, such as ammonium ion.

[0353] In another example, a continuous multi-analyte sensor configuration comprising one or more enzymes and / or at least one cofactor was prepared. In certain embodiments, an enzyme domain 1250 comprising an enzyme (Enzyme) with an amount of cofactor (Cofactor) is positioned proximal to at least a portion of a working electrode (“WE”) surface, where the WE comprises an electrochemically reactive surface. In one example, a second membrane 1251 comprising an amount of cofactor is positioned adjacent the first enzyme domain. The amount of cofactor in the second membrane can provide an excess for the enzyme, e.g., to extend sensor life. One or more resistance domains 1252 (“RL”) are positioned adjacent the second membrane (or can be between the membranes). The RL can be configured to block diffusion of cofactor from the second membrane. Electron transfer from the cofactor to the WE transduces a signal that corresponds directly or indirectly to an analyte concentration.

[0354] FIGs. 8C-8D depict an alternative enzyme domain configuration comprising a first membrane 1251 with an amount of cofactor that is positioned more proximal to at least a portion of a WE surface. Enzyme domain 1250 comprising an amount of enzyme is positioned adjacent the first membrane.

[0355] In the membrane configurations depicted in FIGs. 8C-8D, production of an electrochemically active species in the enzyme domain diffuses to the WE surface and transduces a signal that corresponds directly or indirectly to an analyte concentration. In some examples, the electrochemically active species comprises hydrogen peroxide. For sensor configurations that include a cofactor, the cofactor from the first layer can diffuse to the enzyme domain to extend sensor life, for example, by regenerating the cofactor. For other sensor configurations, the cofactor can be optionally included to improve performance attributes, such as stability. For example, a continuous ketone sensor can comprise NAD(P)H and a divalent metal cation, such as Mg+2. One or more resistance domains RL can be positioned adjacent the second membrane (or can be between the layers). The RL can be configured to block diffusion of cofactor from the second membrane and / or interferents from reaching the WE surface. Other configurations can be used in the aforementioned configuration, such as electrode, resistance, bio-interfacing, and drug releasing membranes, layers or domains. In other examples, continuous analyte sensors including one or more cofactors that contribute to sensor performance.

[0356] FIG. 8E depicts another continuous multi-analyte membrane configuration, where {beta} -hydroxybutyrate dehydrogenase BHBDH in a first enzyme domain 1253 is positioned proximate to a working electrode WE and second enzyme domain 1254, for example, comprising alcohol dehydrogenase (ADH) and NADH is positioned adjacent the first enzyme domain. One or more resistance domains RL 1252 can be deployed adjacent to the second enzyme domain 1254. In this configuration, the presence of the combination of alcohol and ketone in serum works collectively to provide a transduced signal corresponding to at least one of the analyte concentrations, for example, ketone. Thus, as the NADH present in the more distal second enzyme domain consumes alcohol present in the serum environment, NADH is oxidized to NAD(P)H that diffuses into the first membrane layer to provide electron transfer of the BHBDH catalysis of acetoacetate ketone and transduction of a detectable signal corresponding to the concentration of the ketone. In one example, an enzyme can be configuredfor reverse catalysis and can create a substrate used for catalysis of another enzyme present, either in the same or different layer or domain. Other configurations can be used in the aforementioned configuration, such as electrode, resistance, bio-interfacing, and drug releasing membranes, layers, or domains. Thus, a first enzyme domain that is more distal from the WE than a second enzyme domain can be configured to generate a cofactor or other element to act as a reactant (and / or a reactant substrate) for the second enzyme domain to detect the one or more target analytes.Alcohol Sensor Configurations

[0357] In one example, a continuous alcohol (e.g., ethanol) sensor device configuration is provided. In one example, one or more enzyme domains comprising alcohol oxidase (AOX) is provided and the presence and / or amount of alcohol is transduced by creation of hydrogen peroxide, alone or in combination with oxygen consumption or with another substrate-oxidase enzyme system, e.g., glucose-glucose oxidase, in which hydrogen peroxide and or oxygen and / or glucose can be detected and / or measured qualitatively or quantitatively, using amperometry.

[0358] In one example, the sensing region for the aforementioned enzyme substrate-oxidase enzyme configurations has one or more enzyme domains comprises one or more electrodes. In one example, the sensing region for the aforementioned enzyme substrate-oxidase enzyme configurations has one or more enzyme domains, with or without the one or more electrodes, further comprises one or interference blocking membranes (e.g. permselective membranes, charge exclusion membranes) to attenuate one or more interferents from diffusing through the membrane to the working electrode. In one example, the sensing region for the aforementioned substrate-oxidase enzyme configurations has one or more enzyme domains, with or without the one or more electrodes, and further comprises one or resistance domains with or without the one or more interference blocking membranes to attenuate one or more analytes or enzyme substrates. In one example, the sensing region for the aforementioned substrate-oxidase enzyme configurations has one or more enzyme domains, with or without the one or more electrodes, one or more resistance domains with or without the one or more interference blocking membranes further comprises one or biointerface membranes and / or drug releasing membranes, independently, to attenuate one or more analytes or enzyme substrates and attenuate the immune response of the patient after insertion.

[0359] In one example, the one or more interference blocking membranes are deposited adjacent the working electrode and / or the electrode surface. In one example, the one or interference blocking membranes are directly deposited adjacent the working electrode and / or the electrode surface. In one example, the one or interference blocking membranes are deposited between another layer or membrane or domain that is adjacent the working electrode or the electrode surface to attenuate one or all analytes diffusing thru the sensing region but for oxygen. Such membranes can be used to attenuate alcohol itself as well as attenuate other electrochemically actives species or other analytes that can otherwise interfere by producing a signal if they diffuse to the working electrode.

[0360] In one example, the working electrode used comprised platinum and the potential applied was about 0.5 volts.

[0361] In one example, sensing oxygen level changes electrochemically, for example in a Clark type electrode setup, or in a different configuration can be carried out, for example by coating the electrode with one or more membranes of one or more polymers, such as NAFION™. Based on changes of potential, oxygen concentration changes can be recorded, which correlate directly or indirectly with the concentrations of alcohol. When appropriately designed to obey stoichiometric behavior, the presence of a specific concentration of alcohol should cause a commensurate reduction in local oxygen in a direct (linear) relation with the concentration of alcohol. Accordingly, a multi-analyte sensor for both alcohol and oxygen can therefore be provided.

[0362] In another example, the above mentioned alcohol sensing configuration can include one or more secondary enzymes that react with a reaction product of the alcohol / alcohol oxidase catalysis, e.g., hydrogen peroxide, and provide for a oxidized form of the secondary enzyme that transduces an alcohol-dependent signal to the WE / RE at a lower potential than without the secondary enzyme. Thus, in one example, the alcohol / alcohol oxidase is used with a reduced form of a peroxidase, for example horse radish peroxidase. The alcohol / alcohol oxidase can be in same or different layer as the peroxidase, or they can be spatially separated distally from the electrode surface, for example, the alcohol / alcohol oxidase being more distal from the electrode surface and the peroxidase being more proximal to the electrode surface, or alternatively, the alcohol / alcohol oxidase being more proximal from the electrode surface and the peroxidase being more distal to the electrode surface. In one example, the alcohol / alcoholoxidase, being more distal from the electrode surface and the peroxidase, further includes any combination of electrode, interference, resistance, and biointerface membranes to optimize signal, durability, reduce drift, or extend end of use duration.

[0363] In another example, the above mentioned alcohol sensing configuration can include one or more mediators. In one example, the one or more mediators are present in, on, or about one or more electrodes or electrode surfaces and / or are deposited or otherwise associated with the surface of the working electrode (WE) or reference electrode (RE). In one example, the one or more mediators eliminate or reduce direct oxidation of interfering species that can reach the WE or RE. In one example, the one or more mediators provide a lowering of the operating potential of the WE / RE, for example, from about 0.6V to about 0.3V or less on a platinum electrode, which can reduce or eliminates oxidation of endogenous interfering species. Examples of one or mediators are provided below. Other electrodes, e.g., counter electrodes, can be employed.

[0364] In one example, other enzymes or additional components can be added to the polymer mixture(s) that constitute any part of the sensing region to increase the stability of the aforementioned sensor and / or reduce or eliminate the biproducts of the alcohol / alcohol oxidase reaction. Increasing stability includes storage or shelf life and / or operational stability (e.g., retention of enzyme activity during use). For example, byproducts of enzyme reactions can be undesirable for increased shelf life and / or operational stability, and can thus be desirable to reduce or remove. In one example, xanthine oxidase can be used to remove bi-products of one or more enzyme reactions.

[0365] In another example, a dehydrogenase enzyme is used with a oxidase for the detection of alcohol alone or in combination with oxygen. Thus, in one example, alcohol dehydrogenase is used to oxidize alcohol to aldehyde in the presence of reduced nicotinamide adenine dinucleotide (NAD(P)H) or reduced nicotinamide adenine dinucleotide phosphate (NAD(P)+). So as to provide a continuous source of NAD(P)H or NAD(P)+, NADH oxidase or NADPH oxidases is used to oxidize the NAD(P)H or NAD(P)+, with the consumption of oxygen. In another example, Diaphorase can be used instead of or in combination with NADH oxidase or NADPH oxidases. Alternatively, an excess amount of NAD(P)H can be incorporated into the one or more enzyme domains and / or the one or more electrodes in an amount so as to accommodate the intended duration of planned life of the sensor.

[0366] In the aforementioned dual enzyme configuration, a signal can be sensed either by: (1) an electrically coupled (e.g., “wired”) alcohol dehydrogenase (ADH), for example, using an electro-active hydrogel polymer comprising one or more mediators; or (2) oxygen electrochemical sensing to measure the oxygen consumption of the NADH oxidase. In an alternative example, the co-factor NAD(P)H or NAD(P)+ can be coupled to a polymer, such as dextran, the polymer immobilized in the enzyme domain along with ADH. This provides for retention of the co-factor and availability thereof for the active site of ADH. In the above example, any combination of electrode, interference, resistance, and biointerface membranes can be used to optimize signal, durability, reduce drift, or extend end of use duration. In one example, electrical coupling, for example, directly or indirectly, via a covalent or ionic bond, to at least a portion of a transducing element, such as an aptamer, an enzyme or cofactor and at least a portion of the electrode surface is provided. A chemical moiety capable of assisting with electron transfer from the enzyme or cofactor to the electrode surface can be used and includes one or more mediators as described below.

[0367] In one example, any one of the aforementioned continuous alcohol sensor configurations are combined with any one of the aforementioned continuous ketone monitoring configurations to provide a continuous multi-analyte sensor device as further described below. In one example a continuous glucose monitoring configuration combined with any one of the aforementioned continuous alcohol sensor configurations and any one of the aforementioned continuous ketone monitoring configurations to provide a continuous multi-analyte sensor device as further described below.Uric Acid Sensor Configurations

[0368] In another example, a continuous uric acid sensor device configuration is provided. Thus, in one example, uric acid oxidase (UOX) can be included in one or more enzyme domains and positioned adjacent the working electrode surface. The catalysis of the uric acid using UOX, produces hydrogen peroxide which can be detected using, among other techniques, amperometry, voltametric and impedimetric methods. In one example, to reduce or eliminate the interference from direct oxidation of uric acid on the electrode surface, one or more electrode, interference, and / or resistance domains can be deposited on at least a portion of the working electrode surface. Such membranes can be used to attenuate diffusion of uric acid as well as other analytes to the working electrode that can interfere with signal transduction.

[0369] In one alternative example, a uric acid continuous sensing device configuration comprises sensing oxygen level changes about the WE surface, e.g., for example, as in a Clark type electrode setup, or the one or more electrodes can comprise, independently, one or more different polymers such as NAFION™, polyzwitterion polymers, or polymeric mediator adjacent at least a portion of the electrode surface. In one example, the electrode surface with the one or more electrode domains provide for operation at a different or lower voltage to measure oxygen. Oxygen level and its changes in can be sensed, recorded, and correlated to the concentration of uric acid based using, for example, using conventional calibration methods.

[0370] In one example, alone or in combination with any of the aforementioned configurations, uric acid sensor configurations, so as to lower the potential at the WE for signal transduction of uric acid, one or more coatings can be deposited on the WE surface. The one or more coatings can be deposited or otherwise formed on the WE surface and / or on other coatings formed thereon using various techniques including, but not limited to, dipping, electrodepositing, vapor deposition, spray coating, etc. In one example, the coated WE surface can provide for redox reactions, e.g., of hydrogen peroxide, at lower potentials (as compared to 0.6 V on platinum electrode surface without such a coating. Example of materials that can be coated or annealed onto the WE surface includes, but are not limited to Prussian Blue, Medola Blue, methylene blue, methylene green, methyl viologen, ferrocyanide, ferrocene, cobalt ion, and cobalt phthalocyanine, and the like.

[0371] In one example, one or more secondary enzymes, cofactors and / or mediators (electrically coupled or polymeric mediators) can be added to the enzyme domain with UOX to facilitate direct or indirect electron transfer to the WE. In such configurations, for example, regeneration of the initial oxidized form of secondary enzyme is reduced by the WE for signal transduction. In one example, the secondary enzyme is horse radish peroxidase (HRP).Choline Sensor Configurations

[0372] In one example continuous choline sensor device can be provided, for example, using choline oxidase enzyme that generates hydrogen peroxide with the oxidation of choline. Thus, in one example, at least one enzyme domain comprises choline oxidase (COX) adjacent at least one WE surface, optionally with one or more electrodes and / or interference membranes positioned in between the WE surface and the at least one enzyme domain. The catalysis ofthe choline using COX results in creation of hydrogen peroxide which can be detectable using, among other techniques, amperometry, voltametric and impedimetric methods.

[0373] In one example, the aforementioned continuous choline sensor configuration is combined with any one of the aforementioned continuous alcohol sensor configurations, and continuous uric acid sensor configurations to provide a continuous multi-analyte sensor device as further described below. This continuous multi-analyte sensor device can further include continuous glucose monitoring capability. Other membranes can be used in the aforementioned continuous choline sensor configuration, such as electrode, resistance, biointerfacing, and drug releasing membranes.Cholesterol Sensor Configurations

[0374] In one example, continuous cholesterol sensor configurations can be made using cholesterol oxidase (CHOX), in a manner similar to previously described sensors. Thus, one or more enzyme domains comprising CHOX can be positioned adjacent at least one WE surface. The catalysis of free cholesterol using CHOX results in creation of hydrogen peroxide which can be detectable using, among other techniques, amperometry, voltametric and impedimetric methods.

[0375] An exemplary cholesterol sensor configuration using a platinum WE, where at least one interference membrane is positioned adjacent at least one WE surface, over which there is at least one enzyme domain comprising CHOX, over which is positioned at least one resistance domain to control diffusional characteristics was prepared.

[0376] The method described above and the cholesterol sensors described can measure free cholesterol, however, with modification, the configuration can measure more types of cholesterol as well as total cholesterol concentration. Measuring different types of cholesterol and total cholesterol is important, since due to low solubility of cholesterol in water significant amount of cholesterol is in unmodifi...

Claims

1. CLAIMS1. A therapy management system for providing therapy management guidance, the therapy management system comprising:one or more memories comprising executable instructions; andone or more processors in data communication with the one or more memories and configured to execute the executable instructions to:identify initiation of a treatment to treat a disease state of the host based, at least in part, on input data associated with the host;monitor analyte data associated with the host during a time period after initiation of the treatment, the analyte data comprising at least potassium concentration levels;process the analyte data to determine an effect of the treatment on the host, the processing comprising determining one or more potassium metrics of the host during the time period based, at least in part, on potassium concentration levels; and provide therapy management guidance to the host based, at least in part, on the determined effect of the treatment on the host.

2. The therapy management system of claim 1, wherein:the one or more potassium metrics comprise a potassium rate of change; and the one or more processors are further configured to execute the executable instructions to:determine whether the potassium rate of change surpasses a threshold potassium rate of change;when the potassium rate of change does not surpass the threshold potassium rate of change, determine whether a current potassium concentration level of the host is within a range bounded by a high threshold potassium concentration level and a low threshold potassium concentration level; andwhen the potassium rate of change surpasses the threshold potassium rate of change, monitor the potassium concentration levels for the specified time period to determine whether the potassium concentration levels return to a baseline for the host.

3. The therapy management system of claim 2, wherein the one or more processors are further configured to execute the executable instructions to:when the current potassium concentration level is within the range, determine whether the current potassium concentration level is trending toward the high threshold potassium concentration level or the low threshold potassium concentration level; and when the current potassium concentration level is out of the range, determine whether the host is experiencing an abnormal potassium episode or at least one predetermined symptom indicative of a severe health incident.

4. The therapy management system of claim 3, wherein the one or more processors are further configured to execute the executable instructions to:when the current potassium concentration level trends toward the high threshold potassium concentration level, provide therapy management guidance to the host aimed at reducing and stabilizing the potassium concentration levels of the host and continue the monitoring for a specified time period; andwhen the current potassium concentration level trends toward the low threshold potassium concentration level, provide therapy management guidance aimed at increasing and stabilizing the potassium concentration levels and continue the monitoring for the specified time period.

5. The therapy management system of claim 3, wherein the one or more processors are further configured to execute the executable instructions to:when the host is experiencing the abnormal potassium episode or the at least one predetermined symptom, provide therapy management guidance comprising at least one of:a recommendation to seek immediate medical attention in an emergency healthcare setting;a recommendation to co-administer a potassium binder with insulin or with a diuretic; ora recommendation to increase a frequency of potassium monitoring during the specified time period.

6. The therapy management system of claim 3, wherein the one or more processors are further configured to execute the executable instructions to:when the host is not experiencing the abnormal potassium episode or the at least one predetermined symptom, provide therapy management guidance comprising at least one of:a recommendation to administer or titrate a potassium binder when the current potassium concentration level is above the high threshold;a recommendation to administer or titrate a potassium supplement when the current potassium concentration level is below the low threshold;a recommendation to administer a different type of medication; ora recommendation to seek immediate medical attention for potential kidney dysfunction or injury.

7. The therapy management system of claim 2, wherein the one or more processors are further configured to execute the executable instructions to:when the potassium concentration levels return to baseline, continue to measure the potassium concentration levels with increased measurement frequency.

8. The therapy management system of claim 2, wherein the one or more processors are further configured to execute the executable instructions to:when the potassium concentration levels do not return to baseline by an end of the specified time period, determine whether the current potassium concentration level is at or near a threshold potassium concentration level.

9. The therapy management system of claim 8, wherein the one or more processors are further configured to execute the executable instructions to:when the current potassium concentration level is at or near a threshold potassium concentration level, provide therapy management guidance comprising a recommendation to seek immediate medical attention.

10. The therapy management system of claim 8, wherein the one or more processors are further configured to execute the executable instructions to:when the current potassium concentration level is not at or near a threshold potassium concentration level, continue to measure the potassium levels with increased measurement frequency.

11. The therapy management system of claim 2, wherein the threshold potassium rate of change comprises an absolute upper threshold beyond which a measured potassium rate of change is indicative of a current or impending potassium imbalance.

12. The therapy management system of claim 2, wherein:the course of therapy comprises a medication administered by the host; and the threshold potassium rate of change is selected based on at least one of a dosage, a type, or an identity of the medication.

13. The therapy management system of claim 2, wherein the threshold potassium rate of change is personalized using at least one of:historical potassium data of the host;population-based data for patients with similar medication;population-based data for patients with similar demographic information; population-based data for patients with similar physiological information; or population-based data for patients with similar disease information.

14. The therapy management system of claim 1, wherein the one or more processors are further configured to execute the executable instructions to:receive and process non-analyte data, wherein:the non-analyte data comprises at least one of heart rate data, electromyogram (EMG) or electrocardiogram (ECG) data, respiratory rate data, blood pressure data, blood oxygen data, body temperature data, galvanic skin response data, sweat data, accelerometer data, altimeter data, activity data, caloric intake data, electronic medical records (EMR) data; or geographic location data; andthe effect of the treatment is further determined based on the non-analyte data.

15. A method for providing therapy management guidance, method comprising:identifying that a host has begun a course of treatment to treat a disease state of the host based, at least in part, on input data associated with the host;monitoring analyte data associated with the host during a time period after beginning the course of treatment, the analyte data comprising at least potassium concentration levels;processing the analyte data to determine an effect of the course of treatment on the host, the processing comprising determining one or more potassium metrics of the host during the time period based, at least in part, on potassium concentration levels; andproviding therapy management guidance to the host based, at least in part, on the determined effect of the course of treatment on the host.

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