Decision support for glucose-ketone sensors

Abstract

A system includes an analyte measurement system and a software application operatively coupled to the analyte measurement system. The analyte measurement system is configured to measure a ketone level in a patient's bodily fluid. The application is configured to display at least one of (1) a current ketone level, and an indicator of a current ketone trend, (2) a ketone trend graph, and (3) a total amount of time for which the ketone level is above at least one predetermined threshold level. The application is further configured to determine whether the current ketone level is above the at least one predetermined threshold level, and output an alert in response to determining that the current ketone level is above the at least one predetermined threshold level, where the alert is periodically output when the current ketone level is above the at least one predetermined threshold level.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 696,713, filed September 19, 2024, and U.S. Provisional Application No. 63 / 539,927, filed September 22, 2023, the entire contents of which are hereby expressly combined by reference for all purposes. Technical Field

[0003] This disclosure relates to analyte monitoring apparatus, systems, and methods, such as software application apparatus, systems, and methods for detecting, notifying, and preventing adverse analyte conditions. Background Technology

[0004] The detection and / or monitoring of analyte levels is crucial for the health of individuals with diabetes. People with diabetes may experience complications including loss of consciousness, cardiovascular disease, retinopathy, neuropathy, and / or kidney disease. Diabetic ketoacidosis (DKA) is a potentially life-threatening complication of diabetes. DKA is caused by a lack of insulin, in which the body produces acidic ketone bodies in response. DKA typically occurs in people with type 1 diabetes, but can also occur in other types of diabetes under certain circumstances.

[0005] People with diabetes (PwD) typically need to monitor their glucose levels to ensure they remain within a clinically safe range. This information can also be used to determine whether and / or when insulin is needed to lower their glucose levels, or when additional glucose is needed to raise them. Many systems allow individuals to monitor their glucose levels, such as continuous glucose monitoring (CGM). Some of these systems include electrochemical biosensors, including those that use glucose sensors adapted to be located in the body, for example, fully or partially inserted subcutaneously or percutaneously, for continuous in vivo monitoring of glucose levels from bodily fluids at that site.

[0006] Increasing clinical data suggests a strong correlation between glucose monitoring frequency and glycemic control. Despite this correlation, many PwDs do not monitor their glucose levels as frequently as they should due to a combination of factors, including inconvenience, test autonomy, pain and / or cost associated with glucose testing. For patients who rely on the administration of medication (e.g., insulin, SGLT-2 inhibitors) to treat or manage diabetes, it would be desirable to have systems, devices, and / or methods that are capable of integrating glucose data with insulin dosage data and providing more useful conclusions to the patient, caregiver, health care professional (HCP), and / or primary care physician (PCP).

[0007] SGLT-2 inhibitors, also known as sodium-glucose transporters (SGLTs), are a class of drugs that modulate sodium-glucose transport (SGLT) proteins in the kidney nephron to inhibit glucose reabsorption and lower blood glucose. SGLT-2 inhibitors can increase the risk of DKA, and in particular, can cause euDKA, in which blood glucose does not rise due to the absorption of ketones. EuDKA results in high ketone levels with normal glucose levels. Patients (e.g., type 1 diabetes patients) using certain medications (e.g., SGLT-2 inhibitors) are at risk for euDKA.

[0008] Continuous monitoring of additional analytes can be utilized to detect adverse conditions in real-time, such as adverse glucose-ketone conditions (e.g., euDKA). However, despite discrete ketone test strips and CGMs being available, these systems are impractical and / or insufficient for continuous monitoring of ketones. Furthermore, it is complex and difficult for patients to correctly interpret additional analyte levels and determine appropriate actions, requiring the patient to contact a HCP for correct interpretation and guidance. SUMMARY

[0009] Accordingly, in some aspects, a system can include a software application that can detect a current or impending adverse condition (e.g., glucose-ketone condition), provide a patient with a customized notification (e.g., alert, suggestion, guidance) to take action, prompt the patient for additional information related to the detected condition, continuously monitor glucose levels and ketone levels in real-time, control insulin delivery based on ketone levels, and mitigate the risk of euDKA. Furthermore, it is desirable to provide guidance to the patient at the appropriate time and capture additional information (e.g., contextual data) about the patient’s condition at a time that the patient will remember.

[0010] In some aspects, the system can include an analyte measurement system and a software application operatively coupled to the analyte measurement system. In some aspects, the analyte measurement system can be configured to measure a first analyte and a second analyte of a patient. In some aspects, the analyte measurement system can include an analyte sensor and a display device. In some aspects, the software application can be configured to obtain sensor data of a first analyte level and a second analyte level or a plurality of analyte levels. In some aspects, the software application can be configured to detect at least one condition associated with the sensor data. In some aspects, the software application can be configured to provide a notification to the patient associated with the detected at least one condition. Advantageously, the system can detect a current or impending adverse condition (e.g., glucose-ketone condition) and provide a customized notification (e.g., alert, suggestion, guidance) to the patient to take action at an appropriate time. The detected condition can be, for example, a low, medium, or high glucose level and / or a low, medium, or high ketone level.

[0011] In some aspects, the software application can be configured to operate based on conditional logic associated with predetermined settings. In some aspects, the software application can be configured to adjust the predetermined settings. In some aspects, the predetermined settings can include a plurality of thresholds. In some aspects, the predetermined settings can include a first threshold for the first analyte and a second threshold for the second analyte, or a threshold for each of a plurality of analytes. In some aspects, the predetermined settings can include a first threshold and a second threshold for the first analyte and a third threshold and a fourth threshold for the second analyte, or a plurality of thresholds for each of a plurality of analytes. Advantageously, the software application can utilize default or editable thresholds (e.g., glucose thresholds and ketone thresholds) to provide notifications (e.g., alerts, suggestions, guidance, cues) associated with each conditional logic state defined by the thresholds. These thresholds can also be automatically configured based on a set of pre-defined rules that depend on, for example, past glucose data and ketone data, past patient responses to cues, known treatment information (e.g., elevated baseline ketone levels), known diets (e.g., ketogenic diet), other data available to the system, or a combination of these. These rules can be pre-determined by a human or using a machine learning approach or by a combination of the two. Furthermore, these thresholds can be automatically configured based on rules that are particularly suitable for the patient based on past data collected by the system for the patient. This adaptive functionality will benefit the patient by tailoring the thresholds to better suit the patient’s needs. For example, some patients can benefit from a lower high ketone detection threshold and earlier intervention for high ketone conditions, while for other patients, it can be more suitable to have a higher threshold as they can tolerate higher ketone levels and can be disrupted or interrupted by high ketone alerts that are not suitable for them. In this case, the thresholds can be adapted based on, for example, previous glucose data and ketone data, insulin delivery data, and user-entered information about symptoms.

[0012] In some aspects, the software application can be configured to adjust conditional logic based on a rate of change of the first analyte level and / or the second analyte level. In some aspects, the software application can be configured to adjust conditional logic based on a rate of change of multiple analyte levels. In some aspects, the software application can be configured to adjust conditional logic based on a rate of change of a higher order derivative (e.g., second derivative, third derivative, etc.) of the first analyte level and / or the second analyte level (e.g., glucose acceleration, ketone acceleration, etc.). In some aspects, the software application can be configured to adjust conditional logic based on a rate of change of a higher order derivative (e.g., second derivative, third derivative, etc.) of multiple analyte levels (e.g., glucose acceleration, ketone acceleration, lactate acceleration, etc.). In some aspects, the software application can be configured to adjust conditional logic based on insulin sensitivity, carbohydrate ratio, patient disease state, patient medication regimen, and / or pairing with a remote bolus system. Advantageously, the software application can adjust (e.g., optimize) conditional logic based on one or more ranges of various parameters (e.g., glucose rate of change, ketone rate of change, insulin sensitivity, carbohydrate ratio, patient disease state, patient medication regimen, remote bolus system, etc.) and / or one or more equations relating various parameters to insulin bolus values. To define predetermined settings (e.g., thresholds).

[0013] In some aspects, the first analyte and the second analyte can be measured continuously in real-time. In some aspects, the first analyte can be measured at a different frequency than the second analyte. Advantageously, the system can continuously monitor glucose levels and ketone levels in real-time, or near real-time (e.g., every minute, every 5 minutes, every 10 minutes, etc.) acquire and process sensor data to detect a current or impending adverse condition (e.g., glucose-ketone condition) at all times or at appropriate times of day (e.g., during a low period in the morning). Further advantageously, continuous ketone monitoring can account for dynamic effects of ketones, for example, on blood glucose response.

[0014] In some aspects, the analyte sensor can include a dual analyte sensor to measure a first analyte and a second analyte. In some aspects, the dual analyte sensor can include two analyte sensors (e.g., two sensor tails) or two portions or sides on the same analyte sensor in an on body unit (OBU) to measure the first analyte and the second analyte. In some aspects, the dual analyte sensor can include separate microneedles to measure the first analyte and the second analyte. In some aspects, the analyte sensor can include two separate analyte sensors (e.g., separate OBUs) to measure the first analyte and the second analyte. In some aspects, the analyte sensor can include a multi-analyte sensor to measure multiple analytes (e.g., glucose, ketones, lactate, etc.). In some aspects, the multi-analyte sensor can include a multi-analyte sensor (e.g., two sensor tails) or two portions or sides on the same analyte sensor in an OBU to measure the multiple analytes. In some aspects, the multi-analyte sensor can include separate microneedles to measure the multiple analytes. In some aspects, the first analyte can be glucose and the second analyte can be ketones. Advantageously, for continuous analyte (glucose-ketone) monitoring (e.g., real-time) or discrete analyte (glucose-ketone) monitoring (e.g., near real-time), the system can measure and acquire glucose levels and ketone levels from a single (dual) sensor for both analytes or from two separate sensors for each analyte.

[0015] In some aspects, the system can further include an insulin delivery system operatively coupled to the software application. In some aspects, the software application can be configured to control the insulin delivery system based on the detected at least one condition. In some aspects, the software application can be configured to continue insulin delivery if the ketone level is above a high ketone threshold. In some aspects, the ketone data can be used to inform and / or adjust a pump occlusion detection algorithm (e.g., the pump occlusion detection subsystem). For example, detecting elevated ketone levels can indicate a higher likelihood of a problem with the infusion set (e.g., pump occlusion). In some aspects, the ketone data (e.g., elevated ketone levels) can adjust the sensitivity and / or specificity of the pump occlusion detection algorithm. Advantageously, the software application can control insulin delivery from the insulin delivery system based on ketone levels, for example, to mitigate euDKA by continuing insulin delivery if ketone levels are high (e.g., above 3.0 mmol / L), whereas the insulin delivery system can stop or reduce insulin delivery if only glucose levels are considered (e.g., normal glucose levels, detected glucose error, detected glucose failure) in general.

[0016] In some aspects, the notification can include an alert to the patient associated with the detected at least one condition. In some aspects, for example, in addition to the alert to the patient, the notification can include an alert or alarm to another entity (e.g., caregiver, HCP, emergency services, third party, designated personnel, person near the patient, etc.) associated with the detected at least one condition. For example, the alert or alarm can be sent to the other entity via a wireless or network connection (e.g., WiFi, cellular, 5G, thread, Bluetooth, etc.). In some aspects, the notification can include a suggestion to the patient associated with the detected at least one condition to take action. In some aspects, for example, in addition to the suggestion to the patient, the notification can include a suggestion to another entity (e.g., caregiver, HCP, emergency services, third party, designated personnel, person near the patient, etc.) associated with the detected at least one condition. For example, the suggestion or message can be displayed on a display device (e.g., phone, computer, etc.) for the other entity to understand (e.g., “Help, I have diabetes and elevated ketones, please call emergency services”), for example, on the patient’s display device and / or the other entity’s display device. In some aspects, the software application can be configured to provide a second notification to the patient if the detected at least one condition remains unchanged after a predetermined period of time. Advantageously, the software application can detect a current or impending adverse condition (e.g., glucose-ketone condition) and provide a customized notification (e.g., alert, suggestion, guidance) to the patient and / or another entity (e.g., caregiver, HCP, emergency services, third party, designated personnel, person near the patient, etc.) to take action at an appropriate time.

[0017] In some aspects, the software application can be configured to provide a prompt to the patient to obtain additional information regarding the detected at least one condition. In some aspects, the additional information can include contextual data of the detected at least one condition. In some aspects, the software application can be configured to adjust the threshold of the first analyte and / or the second analyte based on the contextual data. In some aspects, the contextual data can include a frequency of occurrence of the detected at least one condition. In some aspects, the frequency can include a number of times the detected at least one condition occurred in at least one of 1 hour, 6 hours, 12 hours, one day, one week, one month, or a combination of these. In some aspects, the contextual data can include a degree of discomfort of the patient. In some aspects, the contextual data can include a causative factor of the patient, a medication taken by the patient, whether the patient has received emergency medical services, or a combination of these. Advantageously, the software application can prompt the patient for additional information related to the detected condition at an appropriate time. Further advantageously, the software application can capture additional information (e.g., contextual data) regarding the patient’s condition at a time when the patient will remember, for example, to assist a clinician or HCP in determining a root cause of the condition.

[0018] In some aspects, the software application can be configured to perform analysis of sensor data to determine a baseline first analyte level and / or a baseline second analyte level. In some aspects, the software application can be configured to perform analysis of sensor data to determine a predictive model. In some aspects, the predictive model can be based on a population model and one or more parameters that adjust the predictive model to within a known variation of the population model. In some aspects, the predictive model can be based on: regression, model-based parameter adaptation, supervised machine learning, unsupervised machine learning, semi-supervised machine learning, reinforcement learning, clustering, decision trees, anomaly detection, neural networks, classification models, or combinations of these. Advantageously, the predictive model can increase the accuracy and precision of estimates regarding future glucose and / or ketone levels of the patient. Further advantageously, the predictive model can estimate the likelihood of a high ketone condition and present the likelihood to the patient (e.g., a notification).

[0019] In some aspects, the system can further include a dose guidance system operatively coupled to the software application. In some aspects, the software application can be configured to provide a dose recommendation based on a glycemic response model. In some aspects, the glycemic response model can be based on basal insulin, insulin sensitivity, carbohydrate ratio, and a second analyte. In some aspects, the second analyte can include ketones or lactate. In some aspects, the basal insulin, insulin sensitivity, and / or carbohydrate ratio can be a function of the second analyte. Advantageously, the glycemic response model can improve a traditional insulin calculator (e.g., a bolus calculator) by utilizing one or more additional analyte measurements (e.g., ketones, lactate) and / or additional information (e.g., basal insulin, insulin sensitivity, carbohydrate ratio). Further advantageously, the glycemic response model can be modified to replace constant factors of the dose calculation (e.g., basal insulin, insulin sensitivity, carbohydrate ratio) with one or more functions of the second analyte level (e.g., ketone level, time series of ketone levels, rate of change of ketones, etc.). Further advantageously, continuous ketone monitoring can account for dynamic effects of ketones on, for example, glycemic response, thereby increasing the accuracy of the glycemic response model.

[0020] In some aspects, the software application can be configured to obtain additional data from a second sensor. In some aspects, for example, the software application can be configured to obtain additional data from a plurality of sensors in addition to the analyte sensor. In some aspects, the additional data can include activity data, heart rate, respiratory rate, body temperature, sweat data, location data, and / or lactate levels. In some aspects, the additional data can include historical data of insulin pump changes (e.g., days since last pump change), historical data of insulin delivery (e.g., pauses in insulin pump), and / or historical data of meals (e.g., ketogenic diet). Advantageously, the additional data from a second sensor or from a plurality of sensors (e.g., activity data, heart rate, respiratory rate, body temperature, sweat data, location data, lactate levels, historical data of pump changes, historical data of insulin delivery, historical data of meals, etc.) can improve the accuracy and precision of the estimation of the hyperketotic condition. Note that this additional data can also be obtained from sources other than sensors, for example, by manual input means or import from connected health, fitness, or exercise applications. Further advantageously, a predictive model (e.g., probabilistic model) can be built between the additional data and the occurrence of high ketone levels to estimate the likelihood of the hyperketotic condition and present that likelihood (e.g., notification) to the patient.

[0021] In some aspects, the software application can be configured to titrate a dose based on the first analyte level and / or the second analyte level. Advantageously, the medication dose value (e.g., SGLT-2 inhibitor) can be automatically titrated by the system for the patient (e.g., determine the amount of ingredient in the solution). Further advantageously, the titration can determine whether the medication (e.g., SGLT-2 inhibitor) dose value should be increased, decreased, or remain based on the ketone level, other analyte level (e.g., glucose), and / or other measurements (e.g., insulin delivered, carbohydrate intake, etc.).

[0022] In some aspects, the software application can be configured to determine an erroneous reading based on the first analyte level and / or the second analyte level. Advantageously, the second analyte level (e.g., ketones, lactate, lactate, alcohol) can be used to detect errors in the first analyte level (e.g., glucose, lactate, lactate, alcohol), and the first analyte level (e.g., glucose) can be used to detect errors in the second analyte level (e.g., ketones, lactate, lactate, alcohol). Further advantageously, the software application can detect an indication of euDKA or an erroneous glucose reading (e.g., low glucose when glucose level is actually high) based on a high ketone level and a low or normal glucose level and notify the patient to take appropriate action accordingly, for example, to take a blood glucose measurement (e.g., blood glucose test strip) to confirm the glucose level.

[0023] In some aspects, the software application can be part of an analyte measurement system. In some aspects, the software application can comprise a mobile application on a display device. In some aspects, the system can further comprise a remote server configured to support the software application. Advantageously, the software application can be contained entirely in a patient mobile application (app), or some or portions of the software application can be contained in a remote server (e.g., web server, cloud server) that supports the software application, e.g., with processing, communication, and / or reporting functionality. Further advantageously, the software application can comprise an application program interface (API) for two or more computer programs to communicate with each other (e.g., conditional logic system, settings system, notification system, dose control system, etc.).

[0024] In some aspects, the software application can be configured to change a default home screen of the display device based on the detected at least one condition. Advantageously, the default home screen can comprise glucose data (e.g., glucose level, rate of change of glucose, glucose trend, etc.), but when ketone levels are elevated, the default home screen can change to display ketone data (e.g., ketone level, rate of change of ketones, ketone trend, etc.), or change to display both glucose data and ketone data.

[0025] In some aspects, the software application can be configured to display a GUI comprising ketone data (e.g., ketone level, rate of change of ketones, ketone trend). A current ketone level (e.g., a recently measured ketone level) can be displayed with a trend arrow to indicate a current trend of the ketone level. The software application can also display current and / or stored ketone readings in a GUI ketone trend graph. The graph can comprise a line depicting at least one of 1.0 mmol / L, 1.5 mmol / L, 2.0 mmol / L, 2.5 mmol / L, and 3.0 mmol / L. The GUI can also comprise a ketone metric, e.g., time above a particular threshold. The threshold can be 0.5 mmol / L, 0.6 mmol / L, 1.0 mmol / L, 1.5 mmol / L, 2.0 mmol / L, or 3.0 mmol / L. In some embodiments, the current ketone level can be updated at a different rate than the ketone graph. The current ketone level can be updated at a faster rate than the ketone trend graph. For example, the current ketone level can be updated every minute, while the graph can be updated every 5 minutes, alternatively every 10 minutes, alternatively every 15 minutes, alternatively every 20 minutes, or alternatively every 30 minutes.

[0026] In some aspects, the method can include measuring a ketone level of the patient and detecting at least one condition associated with the measured ketone data. The at least one condition can be when the ketone level is above a first threshold. The at least one condition can also be when the ketone level is above a second threshold. The at least one condition can also be when the ketone level is above a third threshold. For example, any of the first, second, or third thresholds can be 0.5 mmol / L, 1.0 mmol / L, 1.5 mmol / L, 2.0 mmol / L, 2.5 mmol / L, or 3.0 mmol / L. The method can also include outputting a notification regarding the at least one condition. Outputting the notification can include outputting an audible, haptic, and / or visual notification. The visual notification can be a banner, a toast notification, or a window, or any other notification known in the art.

[0027] In some aspects, the method can include measuring a first analyte and a second analyte of the patient with an analyte measurement system, the analyte measurement system including an analyte sensor and a display device. In some aspects, the method can include measuring a plurality of analytes, such as, but not limited to, glucose, ketones, and lactate. In some aspects, an additional analyte (e.g., lactate) can be used as another measurement value input to the analyte measurement system. In some aspects, the method can include measuring a first plurality of analytes, such as, but not limited to, glucose and ketones, and measuring a second plurality of analytes, such as, but not limited to, lactate and lactate salt. In some aspects, the method can further include acquiring sensor data of the first analyte level and the second analyte level with a software application operatively coupled to the analyte measurement system. In some aspects, the method can further include detecting at least one condition associated with the sensor data. In some aspects, the method can further include providing a notification to the patient associated with the detected at least one condition. Advantageously, the method can detect a current or impending adverse condition (e.g., glucose-ketone condition) and provide a customized notification (e.g., alert, suggestion, guidance) to the patient to take action at an appropriate time.

[0028] In some aspects, the method can further include providing a prompt to the patient to acquire additional information regarding the detected at least one condition. Advantageously, the method can prompt the patient for additional information associated with the detected condition at an appropriate time. Further advantageously, the method can capture additional information (e.g., contextual data) regarding the patient condition at a time that the patient will remember, for example, to assist a clinician or HCP in determining a root cause of the condition.

[0029] In some aspects, the detecting can include utilizing conditional logic associated with predetermined settings. In some aspects, the predetermined settings can include a first threshold and a second threshold for the first analyte and a third threshold and a fourth threshold for the second analyte. Advantageously, the method can utilize default or editable thresholds (e.g., glucose thresholds and ketone thresholds) to provide notifications (e.g., alerts, suggestions, guidance, prompts) associated with each conditional logic state defined by the thresholds.

[0030] In some aspects, the measuring can include continuously measuring the first analyte and the second analyte in real-time. Advantageously, the method can continuously monitor glucose levels and ketone levels in real-time, or near real-time (e.g., every minute, every 5 minutes, every 10 minutes, etc.), acquiring and processing sensor data so as to detect current or impending adverse conditions (e.g., glucose-ketone conditions) at all times or at appropriate times of day (e.g., during a low period in the morning). Further advantageously, continuous ketone monitoring can account for dynamic effects of ketones, for example, on blood glucose response.

[0031] In some aspects, a system can include an analyte measurement system and a processor in communication with the analyte measurement system. In some aspects, the analyte measurement system can be configured to measure a first analyte and a second analyte of a patient. In some aspects, the analyte measurement system can include an analyte sensor and a display device. In some aspects, the processor can be coupled to a memory storing instructions that, when executed, cause the processor to: acquire sensor data of the first analyte level and the second analyte level, detect at least one condition associated with the sensor data, provide a notification to the patient associated with the detected at least one condition, prompt the patient to input contextual data, save a record of the detected at least one condition with the contextual data, and generate a report including the detected at least one condition and the associated contextual data. In some aspects, the contextual data is associated with the detected at least one condition.

[0032] In some aspects, a system can include an analyte measurement system, a processor in communication with the analyte measurement system, and an insulin delivery system in communication with the processor. In some aspects, the analyte measurement system can be configured to measure a first analyte and a second analyte of a patient. In some aspects, the analyte measurement system can include an analyte sensor. In some aspects, the processor can be coupled to a memory storing instructions that, when executed, cause the processor to: acquire sensor data of the first analyte level and the second analyte level, and detect at least one condition associated with the sensor data. In some aspects, the processor can be configured to cause the insulin delivery system to deliver insulin based on the first analyte level and the second analyte level.

[0033] In some aspects, the first analyte can include glucose and the second analyte can include ketones. In some aspects, an insulin dose can be calculated based on the glucose level. In some aspects, the calculated insulin dose can be further adjusted based on the ketone level. In some aspects, insulin sensitivity can be adjusted based on the glucose level and the ketone level. In some aspects, a carbohydrate ratio can be adjusted based on the glucose level and the ketone level.

[0034] In some aspects, the system can further include a display configured to display one or more first trend arrows for the first analyte (e.g., glucose) and one or more second trend arrows for the second analyte (e.g., ketones, lactate, lactic acid, alcohol). In some aspects, the one or more first trend arrows and the one or more second trend arrows have the same number of trend arrows (e.g., one first trend arrow and one second trend arrow, two first trend arrows and two second trend arrows, etc.). In some aspects, the one or more first trend arrows and the one or more second trend arrows have a different number of trend arrows (e.g., one first trend arrow and two second trend arrows, two first trend arrows and one second trend arrow, etc.). In some aspects, the one or more first trend arrows and the one or more second trend arrows display the same rate of change units for the first analyte and the second analyte (e.g., mmol / L / min, mg / dL / min, mmol / L / hr, mg / dL / hr, etc.). In some aspects, the rate of change units are mmol / L / min. In some aspects, the one or more first trend arrows and the one or more second trend arrows display different rate of change units for the first analyte and the second analyte (e.g., mmol / L / min for the one or more first trend arrows and mmol / L / hr for the one or more second trend arrows, etc.). In some aspects, the one or more first trend arrows include a flat arrow to indicate a different rate of change unit for the one or more first trend arrows relative to the one or more second trend arrows. In some aspects, the one or more second trend arrows include a flat arrow to indicate a different rate of change unit for the one or more second trend arrows relative to the one or more first trend arrows.

[0035] Implementations of any of the techniques described above can include systems, methods, processes, devices, and / or apparatus. The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims.

[0036] Further features and exemplary aspects of these aspects, as well as structures and operations of various aspects are described in detail below with reference to the attached drawing figures. It is noted that these aspects are not limited to the specific aspects described herein. The aspects given herein are only examples of the aspects. Additional aspects will be apparent to those of ordinary skill in the art based on the teachings of this disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0037] The accompanying drawings incorporated herein and forming a part of the specification, illustrate these aspects and together with the description, further serve to explain the principles of these aspects and to enable a person skilled in the art to make and use the aspects.

[0038] FIG. 1 is a schematic diagram of an analyte monitoring system having a software application in accordance with an exemplary aspect.

[0039] FIG. 2A is shown in accordance with an exemplary aspect. FIG. 1 is an analyte monitoring system flow diagram of the analyte monitoring system shown.

[0040] FIG. 2B is shown in accordance with an exemplary aspect. FIG. 1 is a software application flow diagram of the software application shown.

[0041] FIG. 3 is a schematic diagram of a software application of the analyte monitoring system shown in accordance with an exemplary aspect. FIG. 1

[0042] FIG. 4A and FIG. 4B is a state diagram of a conditional logic state of the software application shown in accordance with an exemplary aspect and a schematic diagram of a corresponding display notification. FIG. 3

[0043] FIG. 5A and FIG. 5B is a state diagram of a conditional logic state of the software application shown in accordance with an exemplary aspect and a schematic diagram of a corresponding display notification. FIG. 3

[0044] FIG. 6A and FIG. 6B is a state diagram of a conditional logic state of the software application shown in accordance with an exemplary aspect and a schematic diagram of a corresponding display notification. FIG. 3

[0045] FIG. 7A and FIG. 7B is a state diagram of a conditional logic state of the software application shown in accordance with an exemplary aspect and a schematic diagram of a corresponding display notification. FIG. 3

[0046] FIG. 8A and​​​​​FIG. 8B is according to an example aspect FIG. 3 a state diagram of conditional logic states of the software application and a corresponding schematic of displayed notifications.

[0047] FIG. 9A and FIG. 9B is according to an example aspect FIG. 3 a state diagram of conditional logic states of the software application and a corresponding schematic of displayed notifications.

[0048] FIG. 10A and FIG. 10B is according to an example aspect FIG. 3 a state diagram of conditional logic states of the software application and a corresponding schematic of displayed notifications.

[0049] FIG. 11 is according to an example aspect FIG. 3 a schematic of a dose guidance system of the software application.

[0050] FIG. 12 is according to an example aspect FIG. 3 a schematic of a glycemic response model of the software application.

[0051] FIG. 13 is an example display of the ketone metric.

[0052] The features and example aspects will become more apparent in light of the following detailed description, in conjunction with the accompanying drawings, in which like reference numerals identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and / or structurally similar elements. Additionally, generally, the left-most digit of a reference number identifies the drawing in which the reference number first appears. The drawings provided throughout the disclosure should not be interpreted as being to scale. DETAILED DESCRIPTION

[0053] The specification discloses one or more aspects that incorporate features of the application. The aspects disclosed are only examples of the application. The scope of the application is not limited to the aspects disclosed. The application is defined by the claims appended hereto.

[0054] Aspects described in the specification and by the terms “one aspect”, “an aspect”, “an example aspect”, “an example aspect”, “aspect”, and the like refer to aspects that can include the particular feature, structure, or characteristic, but every aspect need not necessarily include the particular feature, structure, or characteristic. Moreover, these phrases are not necessarily referring to the same aspect. Further, when a particular feature, structure, or characteristic is described in connection with an aspect, it is submitted that it is within the knowledge of those skilled in the art to effect such feature, structure, or characteristic in connection with other aspects whether or not explicitly described.

[0055] For ease of description, spatially relative terms, such as "below", "lower", "down", "above", "upper", "up", "bottom" and "top" can be used herein for the purpose of describing the orientation of one element or feature relative to another element or feature depicted in the figures. The spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. The device can be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.

[0056] The term "about" or "approximately" as used herein means a value of a given quantity that can vary based on a particular technology. Based on a particular technology, the term "about" or "approximately" can mean a value of a given quantity that varies within, for example, 0.1-10% of the value (e.g., ±0.1%, ±1%, ±2%, ±5%, or ±10% of the value).

[0057] Numerical values, including endpoints of ranges, can be expressed herein as approximations, the approximation being preceded by the word "about". Other aspects include the specific numerical values. Whether the numerical values are expressed as approximations or not, the disclosure includes both aspects: one expressed as an approximation and the other not expressed as an approximation. It will further be understood that the endpoints of each of the ranges are significant, both in relation to the other endpoint and independently of the other endpoint.

[0058] Aspects of the disclosure can be implemented in hardware, firmware, software, or any combination thereof. Aspects of the disclosure can also be implemented as instructions stored on a machine-readable medium, which can be read and executed by one or more processors. A machine-readable medium can include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium can include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), and others. Further, firmware, software, routines, and / or instructions can be described herein as performing certain actions. However, it should be appreciated that such descriptions are merely for convenience and that such actions are not performed by firmware, software, routines, instructions, etc., unless an apparatus, a processor, a controller, or other device executes the firmware, software, routines, instructions, etc.

[0059] The term "glucose-ketone condition" or "glucose-ketone conditions" as used herein indicates one or more adverse patient conditions dependent on glucose levels and / or ketone levels, for example, hypoglycemia, hyperglycemia, DKA, euDKA, ketosis, ketonuria, seizures, headaches, fatigue, insomnia, nausea, or any other adverse condition.

[0060] The term "context data" as used herein indicates data received from a user based on a context associated with a detected condition, for example, but not limited to, a distress data (e.g., a degree of physical distress, a degree of mental distress, a headache, fatigue, emergency services, etc.), a disease data (e.g., cancer, nausea, virus, bacteria, etc.), a diet data (e.g., fasting, ketogenic diet, no carbohydrates, etc.), an activity data (e.g., strenuous activity, running, etc.), a causative factor data (e.g., genetics, obesity, etc.), a medication data (e.g., SGLT-2 inhibitor, statin, etc.), an insulin delivery data (e.g., pump malfunction, insulin pause, etc.), a frequency data (e.g., a number of occurrences of a condition within a time period), or any other context-based data.

[0061] The term "recommendation" as used herein indicates a recommendation to a user based on a detected condition, for example, but not limited to, an adjustment of a medication delivery (e.g., insulin, SGLT-2 inhibitor, etc.), a behavior change, a food intake (e.g., a carbohydrate amount, etc.), hydration, seeking emergency services, monitoring one or more analytes (e.g., glucose, ketones, etc.). Or any other recommendation based on one or more analyte levels within a certain range.

[0062] Exemplary analyte monitoring system

[0063] As mentioned above, the detection and / or monitoring of analyte levels (e.g., glucose, ketones, lactate, oxygen, hemoglobin A1C, etc.) is crucial for the health of a patient with diabetes (PwD). Patients suffering from diabetes can experience complications, including loss of consciousness, cardiovascular disease, retinopathy, neuropathy, and / or nephropathy. DKA is a potentially life-threatening complication of diabetes. DKA is an adverse condition associated with patients with diabetes that can lead to hospitalization and even death. DKA is caused by a lack of insulin, in reaction, the body produces acidic ketone bodies. DKA is associated with high ketone levels caused by insufficient glucose uptake in insulin-dependent cells, as can be evident from prolonged high glucose levels. The insufficient glucose uptake leading to DKA can be caused by insufficient insulin levels or high levels of insulin resistance in the patient, for example, caused by illness. In this case, the glucose levels can be within or below the target range. Typically, DKA occurs in patients with type 1 diabetes, but can also occur in other types of diabetes under certain circumstances.

[0064] PwDs often need to monitor their glucose levels to ensure that glucose levels remain within a clinically safe range, and can also use this information to determine whether and / or when insulin is needed to lower their glucose levels, or when additional glucose is needed to raise their glucose levels. Many systems allow individuals to monitor their glucose, e.g., CGMs. Some of these systems include electrochemical biosensors, including those that use glucose sensors adapted to be positioned in the body, e.g., fully or partially inserted into a subcutaneous or transcutaneous site in the body, for continuous in vivo monitoring of glucose levels of a bodily fluid (e.g., blood, interstitial fluid) at that site.

[0065] Increasing clinical data suggests a strong correlation between glucose monitoring frequency and glycemic control. Despite this correlation, many PwDs do not monitor their glucose levels as frequently as they should due to a combination of factors, including inconvenience, test autonomy, pain and / or cost associated with glucose testing. For patients who rely on the administration of a medication (e.g., insulin, SGLT-2 inhibitors, etc.) to treat or manage diabetes, it would be desirable to have systems, devices, and / or methods that are capable of integrating glucose data with insulin dosage data and providing more useful conclusions to the patient, caregiver, HCP, and / or PCP.

[0066] SGLT-2 inhibitors, also known as sodium-glucose co-transporter-2 inhibitors, are a class of drugs that modulate SGLT proteins in the kidney nephron, thereby inhibiting the reabsorption of glucose and lowering blood glucose. This effectively lowers the threshold for renal glucose clearance, making it more difficult for high glucose concentrations to be reached in the blood. As a result, SGLT-2 inhibitors can increase the risk of DKA, and in particular, can cause euDKA, in which the production of ketone bodies is increased to compensate for lower glucose utilization. euDKA results in high ketone levels with normal glucose levels. Patients using certain medications (e.g., SGLT-2 inhibitors), such as patients with type 1 diabetes, are at risk for euDKA.

[0067] SGLT-2 inhibitors are diabetes medications that can help reduce glucose fluctuations around meal times and are indicated for use in patients with type 2 diabetes. SGLT-2 inhibitors can also help type 1 diabetes patients manage their glucose levels. However, there are concerns with using SGLT-2 inhibitors in type 1 diabetes patients because it can lead to high ketone levels and DKA while glucose levels are normal, referred to herein as euDKA. For people with type 1 diabetes, sotagliflozin (an SGLT-1 and SGLT-2 inhibitor) is the only SGLT-2 inhibitor-containing medication currently approved by the European Medicines Agency (EU). Currently, there are no SGLT-2 inhibitor medications approved by the FDA for use in type 1 diabetes patients. Therefore, continuous ketone monitoring can be an important component of managing type 2 diabetes with SGLT-2 inhibitors and can reduce the risk of euDKA in type 1 diabetes patients taking SGLT-2 inhibitor medications. For the purposes of this discussion, it should be understood that any medication that can lower the threshold for renal glucose clearance can produce this euDKA risk or ketosis in general, in addition to SGLT-2 inhibitors.

[0068] Continuous monitoring of additional analytes (e.g., ketones, lactate, lactate, alcohol, etc.) can be used to detect adverse conditions in real-time, such as adverse glucose-ketone conditions (e.g., euDKA). However, despite discrete ketone test strips and CGMs being available, these systems are impractical and / or insufficient for continuous monitoring of ketones. Furthermore, it is complex and difficult for patients to correctly interpret additional analyte levels (e.g., ketones, lactate, lactate, alcohol) and determine appropriate actions, requiring the patient to contact a HCP for correct interpretation and guidance.

[0069] Treatment for euDKA typically includes administering insulin and offsetting any unwanted lowering of glucose (e.g., due to insulin) by ingesting carbohydrates. However, it can be confusing for patients as to when they should perform such treatment and when they should seek emergency medical intervention. For example, when a patient’s ketone levels are elevated, but not high enough to represent DKA, and their glucose levels are in the normal range or lower, the patient can not know what to do. Furthermore, a patient’s HCP would benefit from situational information around the elevated ketones or pre-ketosis episode in order to better understand the cause of the patient’s condition and help alleviate the condition when needed.

[0070] The analyte monitoring devices, systems, and methods described below can provide software applications that can detect current or impending adverse conditions (e.g., glucose-ketosis), provide patients with customized notifications (e.g., alerts, suggestions, guidance, etc.) to take action, prompt patients with additional information associated with the detected condition, continuously monitor glucose and ketone levels in real time, control insulin delivery based on ketone levels, and mitigate the risk of euDKA. Furthermore, the aspects of the analyte monitoring devices, systems, and methods discussed below can provide guidance to patients at appropriate times and capture additional information about the patient's condition (e.g., contextual data) at moments the patient will remember.

[0071] FIG. 1 An analyte monitoring system 100 with software application 300 according to an exemplary aspect is illustrated. The analyte monitoring system 100 can be configured to measure a patient's first and second analytes (e.g., glucose and ketones), detect current or impending adverse conditions (e.g., glucose-ketone status), provide the patient with customized notifications (e.g., alerts, suggestions, guidance) to take action, and / or prompt the patient with additional information related to the detected condition. The analyte monitoring system 100 can also be configured to continuously monitor the levels of the first and second analytes (e.g., glucose and ketone levels) in real time, control insulin delivery based on ketone levels, and / or mitigate the risk of euDKA. While the analyte monitoring system 100 in… FIG. 1 While these are shown as independent devices and / or systems, aspects of this disclosure may be used in conjunction with other devices, systems, and / or methods, such as analyte monitoring system flowchart 200A, software application flowchart 200B, software application 300, and / or status diagrams 400A to 1000A and corresponding display notifications 400B to 1000B.

[0072] like FIG. 1 As shown, the analyte monitoring system 100 may include an analyte measurement system 110, a remote server 180, an insulin delivery system 190, and / or a software application 300. The analyte measurement system 110 may be configured to measure (e.g., via supra-body unit 120) a patient's first analyte (e.g., glucose) and second analyte (e.g., ketones, lactate, lactate, alcohol). The analyte measurement system 110 may also be configured to display and / or notify the patient (e.g., via display device 130) the measurement levels of the first analyte (e.g., glucose) and second analyte (e.g., ketones, lactate, lactate, alcohol). FIG. 1 As shown, the analyte measurement system 110 may include an on-body unit (OBU) 120, an insertion device 128, and a display device 130.

[0073] The OBU 120 can be configured to measure and communicate data of a first analyte (e.g., glucose) and a second analyte (e.g., ketones, lactate, lactic acid, alcohol) of a patient. The OBU 120 can also be configured to communicate data (e.g., sensor data 312) from the analyte sensor 122 to one or more components of the analyte monitoring system 100 (e.g., the display device 130, the remote server 180, the insulin delivery system 190, the software application 300, etc.). As FIG. 1 shown, the OBU 120 can include the analyte sensor 122, on the on-body electronics 124, on the on-body housing 125, and / or on the adhesive layer 126.

[0074] The analyte sensor 122 can be configured to measure a first analyte (e.g., glucose) and a second analyte (e.g., ketones, lactate, lactic acid, alcohol) of a patient. The analyte sensor 122 can also be configured to continuously and automatically measure (e.g., in vivo) a concentration of one or more analytes (e.g., the first analyte 123a, the second analyte 123b, etc.) of a patient. As FIG. 1 shown, the analyte sensor 122 can detect a first analyte 123a (e.g., glucose) and a second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol). In some aspects, a portion (e.g., a distal portion) of the analyte sensor 122 can be positioned in vivo through a skin surface (e.g., transdermally) of a patient and in fluid contact with a bodily fluid (e.g., blood, interstitial fluid, etc.) of the patient. In some aspects, the analyte sensor 122 can be insertable into a patient (e.g., a vein, an artery, skin, etc.) containing an analyte. In some aspects, the analyte sensor 122 can include a CGM to continuously and automatically track glucose levels (e.g., the first analyte level 314a). In some aspects, the analyte sensor 122 can include continuous ketone monitoring to continuously and automatically track ketone levels (e.g., the second analyte level 314b).

[0075] In some aspects, for example, as FIG. 1As shown, the analyte sensor 122 can include a single (dual) analyte sensor to measure a first analyte 123a and a second analyte 123b (e.g., simultaneously). In some aspects, the analyte sensor 122 can include two separate analyte sensors to measure a first analyte 123a and a second analyte 123b (e.g., a CGM sensor and a separate continuous ketone monitoring sensor). In some aspects, the first analyte 123a can be glucose and the second analyte 123b can be ketones. In some aspects, the analyte sensor 122 can measure and obtain glucose levels and ketone levels in real-time (e.g., about 1 second to 60 seconds) for continuous analyte (e.g., glucose-ketone) monitoring. In some aspects, the analyte sensor 122 can measure and obtain glucose levels and ketone levels near real-time (e.g., about 1 minute to 15 minutes) for discrete analyte (e.g., glucose-ketone) monitoring. Exemplary analyte monitoring systems are described in US 2018 / 0256103, US 2024 / 0033427, US 2022 / 0056500, US 2020 / 0237275, US 2022 / 0386910, US 2021 / 0190719, US 2021 / 0219885, US 2022 / 0186278, US 2022 / 0233116, US 2022 / 0186277, US 2022 / 0202327, US 2022 / 0386910, the entireties of which are hereby incorporated by reference for all purposes.

[0076] In some aspects, the analyte sensor 122 can automatically and / or continuously monitor one or more analyte levels (e.g., the first analyte 123a, the second analyte 123b, etc.), e.g., glucose and ketones of a patient, in vivo for a predetermined time interval (e.g., sensor lifetime) or a given sensing duration (e.g., 1 day, 3 days, 7 days, 14 days, 30 days, at least 1 day, at least 3 days, at least 1 day to 3 days, at least 7 days, at least 10 days, optionally 1 day to 10 days, at least 14 days, at least 3 days to 14 days, at least 30 days, etc.). In some aspects, the analyte sensor 122 can be coupled (e.g., electronically) to the on-body electronics 124 to process information (e.g., sensor data 312) obtained from the analyte sensor 122. In some aspects, the analyte sensor 122 can be in communication (e.g., wired, wireless) with the on-body electronics 124.

[0077] In some aspects, the analyte sensor 122 can measure one or more analytes. For example, the analyte sensor 122 can measure one or more metabolic analytes (e.g., glucose, ketones, lactate, lactic acid, oxygen, hemoglobin A1C, lactones, lactose, galactose, vitamin C, glucuronate, glycogen, mannose, phosphate, diphosphate, fructose, glyceraldehyde, glycerol, triglycerides, sorbitol, phosphogluconate, phosphogluconate, xylulose, ribose, bile, cysteine, serine, homoserine, pyruvate, phenylpyruvate, glutamate, glycine, taurine, threonine, methionine, ethanol, acetone, acetate, oxaloacetate, alanine, phenylalanine, aspartate, asparagine, alcohol, cholesterol, vitamin D, progesterone, testosterone, estrogen, squalene, insulin, oxybutyrate, leucine, isoleucine, malonyl, malonate, glucagon, epinephrine, norepinephrine, palmitate, lysine, eicosanoids, melanin, dopamine, tyrosine, tryptophan, niacin, melatonin, serotonin, citrate, isocitrate, valine, porphyrin, histidine, urocanate, histamine, glutamine, proline, creatine, putrescine, spermidine, spermine, arginine, ornithine, citrulline, fumarate, succinate, arginyl succinate, succinyl, ketoglutarate, aconitate, glyoxylic acid, caffeine, sugars, carbohydrates, etc.). In some aspects, the analyte sensor 122 can measure one or more analytes simultaneously with one or more corresponding electrochemical biosensors for each different analyte being measured.

[0078] The on-body electronics 124 can be configured to process signals from the analyte sensor 122. The on-body electronics 124 can also be configured to communicate data (e.g., sensor data 312) from the analyte sensor 122 to one or more external devices (e.g., the software application 300, the display device 130, the remote server 180, etc.). The on-body electronics 124 can also be configured to wirelessly communicate (e.g., WiFi, Bluetooth, the Internet, etc.) analyte-related data (e.g., the first analyte 123a and / or the second analyte 123b). As shown, the on-body electronics 124 can be operatively (e.g., electrically) coupled to the analyte sensor 122, and wirelessly coupled to the display device 130 and the software application 300. FIG. 1

[0079] ​In some aspects, on-body electronics 124 can include a printed circuit board (PCB) for connecting to various components (e.g., analyte sensor 122, processor, ASIC, wireless transceiver, wireless transmitter, controller, memory, etc.). In some aspects, on-body electronics 124 can store (e.g., via memory) historical analyte-related data (e.g., first analyte 123a and / or second analyte 123b). In some aspects, on-body electronics 124 can be configured to store some or all analyte-related data (e.g., sensor data 312) from analyte sensor 122 in memory, for example, during a sensing duration (e.g., 1 day, 3 days, 7 days, 14 days, 30 days, etc.). In some aspects, on-body electronics 124 can include one or more processors and / or control logic configured to determine (e.g., via software programs and / or algorithms) future and / or expected analyte levels based on analyte-related data (e.g., sensor data 312) from analyte sensor 122. In some aspects, on-body electronics 124 can include one or more processors and / or control logic configured to determine (e.g., via software programs and / or algorithms) current analyte levels (e.g., first analyte level 314a, second analyte level 314b, etc.), rates of change of analyte levels (e.g., first analyte ROC 316a, second analyte ROC 316b, etc.), rates of acceleration of analyte levels (e.g., rates of first analyte ROC 316a and second analyte ROC 316b), and / or analyte trend information (e.g., trend display 144), and / or analyte fluctuation levels (e.g., standard deviations, etc.).

[0080] In some aspects, on-body electronics 124 can be configured to periodically send (broadcast) analyte-related data (e.g., sensor data 312) to one or more external devices (e.g., software application 300, display device 130, remote server 180, etc.) without receiving a command or request from the external devices. In some aspects, on-body electronics 124 can be configured to send (broadcast) real-time data associated with monitored analyte levels (e.g., first analyte 123a and / or second analyte 123b) from analyte sensor 122 to one or more external devices (e.g., software application 300, display device 130, remote server 180, etc.), for example, when the external devices are within a communication range of the broadcasted data from OBU 120.

[0081] In some aspects, the on-body electronics 124 can be configured to wirelessly transmit stored analyte-related data (e.g., first analyte 123a and / or second analyte 123b) to one or more external devices (e.g., software application 300, display device 130, remote server 180, etc.) during a monitoring time period. In some aspects, analyte-related data (e.g., sensor data 312) transmitted from the on-body electronics 124 can be stored (e.g., permanently, temporarily) in one or more storage units, such as a storage unit on one or more external devices (e.g., software application 300, display device 130, remote server 180, etc.). In some aspects, the display device 130 can be configured as a data conduit to pass data (e.g., sensor data 312) received from the on-body electronics 124 to one or more external devices (e.g., software application 300, remote server 180, etc.).

[0082] The on-body housing 125 can be configured to provide an internal compartment for a portion (e.g., a proximal portion) of the analyte sensor 122 and the on-body electronics 124. As FIG. 1 shown, the on-body housing 125 can include the analyte sensor 122 and the on-body electronics 124, and can be coupled to the adhesive layer 126. In some aspects, the on-body housing 125 can include a sealed housing (e.g., a hermetically sealed biocompatible housing). The adhesive layer 126 can be configured to attach the OBU 120 to a skin surface of a patient. As FIG. 1 shown, the adhesive layer 126 can be coupled to the on-body housing 125 to securely position a portion (e.g., a distal portion) of the analyte sensor 122 to the skin surface. In some aspects, the adhesive layer 126 can provide a tip seal for the insertion device 128.

[0083] The insertion device 128 can be configured to position a portion (e.g., a distal portion) of the analyte sensor 122 through a skin surface of a patient (e.g., in vivo) and in fluidic contact with a bodily fluid (e.g., blood, interstitial fluid) of the patient. The insertion device 128 can also be configured to attach the OBU 120 to the skin surface of the patient. As FIG. 1 shown, the insertion device 128 can be configured to hold the OBU 120 and, when operated, position a portion of the analyte sensor 122 through a skin surface of a patient in vivo and in fluidic contact with a bodily fluid (e.g., blood, interstitial fluid), and secure the OBU 120 to the skin surface. In some aspects, the OBU 120 can be sealed within the insertion device 128 prior to use.

[0084] Display device 130 can be configured to output (e.g., display) information to a patient. Display device 130 can also be configured to provide customized notifications (e.g., alerts, recommendations, guidance, etc.) to a patient (e.g., via software application 300). Display device 130 can also be configured to provide customized prompts to a patient to obtain additional information (e.g., via software application 300). As FIG. 1 shown, display device 130 can be operatively (e.g., wirelessly) coupled to OBU 120, remote server 180, and / or software application 300. In some aspects, display device 130 can include a handheld computer (e.g., a smart phone, a cellular phone, a mobile phone, a PDA, a smart watch, etc.), a personal computer, a laptop computer, or any other portable communication device.

[0085] In some aspects, a default home screen of display device 130 (e.g., glucose display to ketone display, etc.) can be changed based on a detected condition, e.g., a condition detected via software application 300. In some aspects, the default home screen of display device 130 can include glucose data (e.g., glucose level, rate of change of glucose, glucose trend, etc.), but when ketone levels are elevated, the default home screen of display device 130 can change to display ketone data (e.g., ketone level, rate of change of ketones, ketone trend, etc.), or change to display both glucose data and ketone data. As FIG. 1 shown, display device 130 can include a housing 132, an input component 134, a data communication port 136, and / or a display 140.

[0086] Input component 134 can be configured to control operation of display device 130. Input component 134 can also be configured to input data and / or commands to display device 130. As FIG. 1 shown, input component 134 can interact with display device 130 to control operation of display device 130 (e.g., in response to notifications and / or prompts). In some aspects, input component 134 can include buttons, actuators, switches, dials, touchscreens, microphones, cameras, combinations thereof, or similar input elements. For example, input component 134 can be a touchscreen or touch-sensitive element of display 140. In some aspects, input component 134 can include audio commands recognized, e.g., via a microphone of display device 130. In some aspects, input component 134 can include predetermined motion and / or gesture commands recognized, e.g., via a camera of display device 130.

[0087] Data communication port 136 can be configured to communicate data with one or more external devices (e.g., OBU 120, software application 300, remote server 180, a blood glucose reader, a blood ketone reader, etc.). As FIG. 1As shown, the data communication port 136 can be operatively coupled to the housing 132 of the display device 130. In some aspects, the data communication port 136 can include wireless data communication (e.g., WiFi, Bluetooth, cloud computing, the Internet, etc.). In some aspects, the data communication port 136 can include a wireless transceiver, a wireless transmitter, and / or a wireless receiver. In some aspects, the data communication port 136 can include wired data communication (e.g., a USB port, a mini-USB port, a serial port, an Ethernet port, an Internet port, etc.).

[0088] In some aspects, the data communication port 136 can be configured to receive data from an in-vitro test strip (e.g., having a fluid sample thereon) based on an in-vitro measurement (e.g., a blood glucose measurement, a blood ketone measurement, etc.), for example, from a blood glucose reader and / or a blood ketone reader. In some aspects, the data communication port 136 can be configured to receive data from an in-vitro glucose test strip via a blood glucose reader based on an in-vitro blood glucose measurement. In some aspects, the data communication port 136 can be configured to receive data from an in-vitro ketone test strip via a blood ketone reader based on an in-vitro blood ketone measurement. In some aspects, the data communication port 136 can be configured to receive data from an in-vitro test strip via a fluid analyte reader based on an in-vitro fluid measurement of a plurality of analytes (e.g., glucose, ketones, lactate, lactic acid, oxygen, hemoglobin A1C, alcohol, etc.).

[0089] The display 140 can be configured to display various information— some or all of which can be displayed simultaneously or at different times. The display 140 can also be configured to output an alert, a notification (e.g., an alarm, a suggestion, a guidance, etc.), a prompt, a first analyte 123a (e.g., glucose) level, a second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol) level, or a combination thereof, which can be visual, audible, tactile, or a combination thereof. As FIG. 13 As shown, the display 140 can include, but is not limited to, a graphical display 142, a trend display 144, a numerical display 146, a menu input 148, a time display 150, a graphical input 152, a connectivity display 154, an alert display 156, a date display 158, a sensor calibration display 160, and / or a battery display 162.

[0090] In some aspects, the default home screen of the display 140 can display glucose data, e.g., a glucose level (e.g., first analyte level 314a, numerical display 146), a rate of change of the glucose level (e.g., first analyte ROC 316a, trend display 144), and a glucose graph (e.g., graphical display 142). In some aspects, the default home screen of the display 140 can display ketone data, e.g., a ketone level (e.g., second analyte level 314b, numerical display 146), a rate of change of the ketone level (e.g., second analyte ROC 316b, trend display 144), and a ketone graph (e.g., graphical display 142).

[0091] In some aspects, the default home screen of the display 140 can display glucose data and can include an indication of ketone range (e.g., second analyte level 314b, etc.). In some aspects, the default home screen of the display 140 can display glucose data and omit any indication of ketones, e.g., if the ketone level is within a normal range (e.g., about 0.5 mmol / L to 1.5 mmol / L) or close to a baseline value (e.g., about 0.5 mmol / L).

[0092] In some aspects, the default home screen of the display 140 can display glucose data and can include an indication of ketones, e.g., if the ketone level is elevated within a moderate range (e.g., about 1.0 mmol / L to 3.0 mmol / L) or a high range (e.g., above 3.0 mmol / L). For example, the display 140 can display a ketone level (e.g., second analyte level 314b, numerical display 146) and / or (optionally) a rate of change of the ketone level (e.g., second analyte ROC 316b, trend display 144), which can be calculated based on a predetermined sensing duration (e.g., 15 minutes, 30 minutes, 60 minutes, etc.).

[0093] In some aspects, the default home screen of the display 140 can change to be ketone-centric, rather than glucose-centric, when the ketone level is elevated (e.g., above 1.0 mmol / L). For example, the display 140 can display ketone data, e.g., a ketone level (e.g., second analyte level 314b, numerical display 146), a rate of change of the ketone level (e.g., second analyte ROC 316b, trend display 144), and / or a ketone graph (e.g., graphical display 142), and the display 140 can indicate whether there is a low glucose level (e.g., below 70 mg / dL).

[0094] In some respects, when ketone levels rise (e.g., above 1.0 mmol / L), the default glucose time-series plot on display 140 (e.g., graph display 142) can be automatically replaced by a ketone time-series plot (e.g., graph display 142). In some respects, the ketone time-series plot (e.g., graph display 142) can utilize a different time range (X-axis) than the default glucose time-series plot (e.g., graph display 142), for example, a 24-hour ketone plot and an 8-hour glucose plot. In some respects, the glucose time-series plot (e.g., graph display 142) and the ketone time-series plot (e.g., graph display 142) can be juxtaposed (e.g., side-by-side) on display 140. For example, the glucose axis can be displayed on the left Y-axis, and the ketone axis can be displayed on the right Y-axis, and the time scale (X-axis) can be switched accordingly (e.g., switched to 24 hours). In some aspects, the display 140 may include user interface devices (e.g., input component 134, user interface subsystem 344 of software application 300) to switch or convert between glucose focusing information and ketone focusing information.

[0095] In some implementations, the system may monitor only a single analyte, such as ketone bodies, and the default main screen 1800 of the display device 130 may display only the ketone level, such as... FIG. 1 As shown. Alternatively, multiple analytes can be monitored, and a single analyte, such as ketones, can be displayed. The ketone indicator can include the current (or most recently measured) ketone level 146, a ketone trend arrow 144, a ketone graph 242, and time above a threshold level 250. The current ketone level 146 and trend arrow 144 can be displayed in a banner on the GUI 18800. The current ketone level 146 can be displayed differently depending on its value. For specific ranges, the numerical value of the ketone level will not be displayed. When the current ketone level 146 is less than a low threshold, a descriptive expression can be displayed instead of a numerical value. For example, if the low threshold is 0.5 mmol / L, the current ketone level can be displayed as "<0.5" when the ketone level is less than 0.5 mmol / L. Similarly, when the current ketone level 146 is higher than a high threshold, a different descriptive expression can be displayed instead of a numerical value. For example, if the high threshold is 3.0 mmol / L, the current ketone level can be displayed as "<0.5" when the ketone level is greater than 3.0 mmol / L. 3.0". If the current ketone level 146 is at or above the low threshold and at or below the high threshold, the current ketone level can be displayed as a numerical value. For example, a ketone level of 1.7 mmol / L can be displayed as 1.7 mmol / L.

[0096] The ketones graph 242 can display current and historical ketones levels, or can display only historical ketones levels. If a user drags their finger along the ketones trace 244, the ketones numerical levels can be displayed along the graph. For example, the numerical ketones levels can be displayed in a marker 248 above the ketones trace 244 along with the time corresponding to the measured ketones level. The indication of the ketones level in the marker can be different depending on the value of the ketones level, and whether the ketones level is below the low threshold, at or above the low threshold and at or below the high threshold, or above the high threshold, as described above with respect to the current ketones level 146. The ketones graph 242 can include lines marking various levels, for example, a line 242 at 1.0 mmol / L and a different line 244 at 1.5 mmol / L to highlight to the user when their measured levels are above these various thresholds so that it is immediately apparent. The ketones graph 242 can display ketones levels within a range, and ketones levels at or above or at or below the upper and lower limits of the range can be displayed as a break in the line or trace. For example, the range can be from 0.5 mmol / L to 3 mmol / L. Ketones levels below 0.5 mmol / L can be displayed as a line at 0.5 mmol / L, and ketones values above mmol / L can be displayed as a break in the line or trace at 3.0 mmol / L. In some embodiments, the time difference between the current or real-time ketones level 146 and the ketones level on the graph is displayed on the graph. For example, the current or real-time ketones level 146 can be updated every minute, while the graph 242 can be updated every 5 minutes, alternatively every 10 minutes, alternatively every 15 minutes, alternatively every 20 minutes, and alternatively every 30 minutes. The time above threshold level 250 can be the number of hours that the user has measured ketones levels above the threshold level in the day 220 (e.g., the current day) displayed. The threshold level can be about 0.5 mmol / L, alternatively about 0.6 mmol / L, alternatively about 1.0 mmol / L, alternatively about 1.5 mmol / L. The GUI 1800 can also include a link 222 to a live screen and a link 224 to settings.

[0097] GUI 1800 can also include an indicator regarding sensor status. The indicator can include an icon 230 indicating sensor status and information 232 regarding the biosensor status. In some embodiments, the icon 230 can also include a progress indicator indicating the remaining time until the biosensor is ready. The icon 230 can include a graphic highlighting the time remaining until the biosensor activity. The graphic can include a radiating circle of dots, which can be animated. The icon 230 can include a progress indicator, which can be a bar with a colored portion, or can be a colored portion along the circumference of a circle, where the colored portion is proportional to the amount of time remaining until the sensor activity, for example, the total circumference of the circle can equal 60 minutes, and the colored portion of the circumference can be proportional to the amount of time remaining until the sensor activity is less than one hour. The information 232 can include text indicating that the sensor is “ready in XX,” where XX can be displayed in minutes and seconds. For example, the banner 1002 can display “ready in 55:10,” where the sensor will be active in 55 minutes and 10 seconds. Or the information 232 can include indicating that the sensor is currently active and display “LIVE.”

[0098] The graphical display 142 can be configured to provide a graphical plot (e.g., time series plot) of the first analyte 123a (e.g., glucose) and / or the second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol) from the analyte sensor 122. As shown, the graphical display 142 can include a plot of the first analyte 123a (e.g., glucose) over time. In some aspects, the graphical display 142 can include a plot of the second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol) over time. In some aspects, the graphical display 142 can include one or more plots corresponding to one or more analytes. For example, the graphical display 142 can include a glucose time series plot (e.g., based on the first analyte 123a) and a ketones time series plot (e.g., based on the second analyte 123b) juxtaposed on the display 140. In some aspects, the graphical display 142 can include important markers, e.g., meals, exercise, sleep, heart rate, blood pressure, etc. FIG. 1

[0099] The trend display 144 can be configured to indicate a rate of change (ROC) of the first analyte 123a (e.g., glucose) and / or the second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol). As shown, the trend display 144 can include a plot of the first analyte 123a (e.g., glucose) over time. In some aspects, the trend display 144 can include a plot of the second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol) over time. In some aspects, the trend display 144 can include one or more plots corresponding to one or more analytes. For example, the trend display 144 can include a glucose time series plot (e.g., based on the first analyte 123a) and a ketones time series plot (e.g., based on the second analyte 123b) juxtaposed on the display 140. In some aspects, the trend display 144 can include important markers, e.g., meals, exercise, sleep, heart rate, blood pressure, etc. FIG. 1 ​As shown, the trend display 144 can include an arrow (trend) indicative of the ROC of the first analyte 123a (e.g., glucose). In some aspects, the trend display 144 can indicate the magnitude and direction of any ongoing trend, e.g., the ROC of the first analyte 123a (e.g., glucose). In some aspects, the trend display 144 can include the ROC of the first analyte 123a (e.g., glucose). In some aspects, the trend display 144 can include the ROC of the second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol). In some aspects, the trend display 144 can include one or more arrows (trends) corresponding to one or more analytes. For example, the trend display 144 can include a glucose arrow (e.g., first analyte ROC 316a) and a ketones arrow (e.g., second analyte ROC 316b) juxtaposed (e.g., side-by-side) on the display 140. In some aspects, the trend display 144 can indicate the ROC rate of the first analyte 123a (e.g., glucose) and / or the second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol).

[0100] The numerical display 146 can be configured to provide a monitored level of the first analyte 123a (e.g., glucose) and / or the second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol). As shown, FIG. 1 As shown, the numerical display 146 can indicate a current value of an analyte, e.g., the first analyte 123a (e.g., glucose). In some aspects, the numerical display 146 can include a numerical level of the first analyte 123a (e.g., glucose), e.g., in mg / dL. In some aspects, the numerical display 146 can include a numerical level of the second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol), e.g., in mmol / L. In some aspects, the numerical display 146 can include one or more current values (numerical levels) corresponding to one or more analytes. For example, the numerical display 146 can include a glucose level (e.g., first analyte level 314a) and a ketones level (e.g., second analyte level 314b) juxtaposed (e.g., side-by-side) on the display 140.

[0101] The menu input 148 can be configured to control the operation of the display device 130. The menu input 148 can also be configured to provide access to additional menus (e.g., change display settings, etc.) of the display 140. As shown, FIG. 1As shown, menu input 148 can be positioned on display 140. In some aspects, menu input 148 can be a touch screen button or touch sensitive element of display 140. In some aspects, menu input 148 can include a touch screen, gesture recognition, command recognition (e.g., vocal, visual), or other suitable input element. In some aspects, menu input 148 can be configured to change the display configuration of display 140, e.g., change the default home screen configuration.

[0102] Time display 150 can be configured to provide time of day information. Connection display 154 can be configured to indicate wireless communication connections with other devices (e.g., OBU 120, remote server 180, insulin delivery system 190, software application 300, etc.). Date display 158 can be configured to provide date information. Battery display 162 can be configured to indicate (e.g., graphically) the status of a battery (e.g., rechargeable, disposable) of display device 130. As FIG. 1 As shown, time display 150, connection display 154, date display 158, and battery display 162 can be positioned along a perimeter panel of display 140.

[0103] Graphical input 152 can be configured to control the operation of graphical display 142. In some aspects, graphical input 152 can also be configured to provide access to additional menus (e.g., change graphical display settings, etc.) of graphical display 142. As FIG. 1 As shown, graphical input 152 can be a touch screen button or touch sensitive element of display 140. In some aspects, graphical input 152 can include a touch screen, gesture recognition, command recognition (e.g., vocal, visual), or other suitable input element. In some aspects, graphical input 152 can be configured to change the display configuration of graphical display 142, e.g., change the time scale (X-axis) of graphical display 142.

[0104] Alert display 156 can be configured to indicate the status of an alert condition. As FIG. 8AAs shown, the alarm display 156 may be located above the graphic display 142 and indicate when a specific alarm has been triggered (e.g., via software application 300). In some aspects, the alarm display 156 may indicate one or more specific alarms, such as a low glucose threshold alarm (e.g., about 70 mg / dL), a moderate glucose threshold alarm (e.g., about 110 mg / dL), a high glucose threshold alarm (e.g., about 180 mg / dL), a low ketone threshold alarm (e.g., about 0.5 mmol / L), a moderate ketone threshold alarm (e.g., about 1.0 mmol / L), a high ketone threshold alarm (e.g., about 3.0 mmol / L), etc. In some aspects, the alarm display 156 may indicate one or more specific alarms based on the levels of a first analyte 123a (e.g., glucose) and a second analyte 123b (e.g., ketones, lactate, lactate, alcohol), for example, as... FIG. 9A , FIG. 10A and FIG. 1 As shown separately, an alarm can be triggered if the ketone level is below the high ketone threshold (e.g., about 3.0 mmol / L) and above the low ketone threshold (e.g., about 0.5 mmol / L), and the glucose level is above the high glucose threshold (e.g., about 180 mg / dL); or if the ketone level is below the high ketone threshold (e.g., about 3.0 mmol / L) and above the low ketone threshold (e.g., about 0.5 mmol / L), and the glucose level is below the high glucose threshold (e.g., about 180 mg / dL) and above the low glucose threshold (e.g., about 70 mg / dL); or if the ketone level is below the high ketone threshold (e.g., about 3.0 mmol / L) and above the low ketone threshold (e.g., about 0.5 mmol / L), and the glucose level is below the low glucose threshold (e.g., about 70 mg / dL). In some aspects, the graphical display 142 may also be combined with the alarm display 156 to display an alarm icon. In some respects, for example, based on changes to predetermined settings 332 (e.g., threshold 334) of software application 300, a user or HCP can change (e.g., customize) specific alarms and / or alarm icons.

[0105] The alert notifications described in the various embodiments herein may include toast alerts, banner alerts, lock screen alerts, swipe-up notifications, or any other alerts known in mobile application design. The notification may include haptic components, such as vibration. The notification may include auditory components, such as a beeping sound.

[0106] The sensor calibration display 160 can be configured to indicate when the analyte sensor 122 needs calibration. For example... FIG. 1As shown, the sensor calibration display 160 can be located on the upper panel of the display 140. In some aspects, the sensor calibration display 160 can provide periodic, routine, and / or predetermined calibration events based on the condition of the analyte sensor 122. In some aspects, the sensor calibration display 160 can notify the user when the analyte sensor 122 needs to be calibrated or replaced, for example, the display 140 (e.g., the graphical display 142, the alarm display 156) can also display a calibration alert icon in conjunction with the sensor calibration display 160. In some aspects, sensor calibration can be omitted (e.g., no calibration is needed).

[0107] The remote server 180 can be configured to provide data management, data analysis, and / or data communication with one or more components of the analyte monitoring system 100 (e.g., the OBU 120, the display device 130, the insulin delivery system 190, the software application 300, etc.). The remote server 180 can be configured to support the display device 130 and / or the software application 300. As FIG. 1 As shown, the remote server 180 can be operatively (e.g., wirelessly) coupled to the display device 130 and the software application 300. In some aspects, the remote server 180 can include a personal computer (e.g., a smartphone), a laptop computer, an external server, a server terminal, a cloud server, a web server, or other suitable server that provides functionality for other programs and / or devices.

[0108] In some aspects, the remote server 180 can be connected to a wireless network (e.g., the Internet), a local area network (LAN), a wide area network (WAN), or any other data network for one-way or two-way data communication between one or more components of the analyte monitoring system 100 (e.g., the OBU 120, the display device 130, the insulin delivery system 190, the software application 300, etc.). In some aspects, the remote server 180 can provide new software and / or software updates (e.g., versions, patches, fixes, updates, upgrades, etc.) to one or more components of the analyte monitoring system 100 (e.g., the OBU 120, the display device 130, the insulin delivery system 190, the software application 300, etc.). In some aspects, all of the software application 300 can be contained in the remote server 180. In some aspects, some or portions of the software application 300 can be contained in the remote server 180, for example, to support processing, communication, and / or reporting functions of the software application 300.

[0109] The insulin delivery system (IDS) 190 can be configured to provide insulin to the patient. The IDS 190 can also be configured to adjust the rate of insulin to the patient (e.g., in response to CGM values and trends). As FIG. 1As shown, the IDS 190 can be operatively coupled to the software application 300. In some aspects, the IDS 190 can be an automated insulin delivery (AID) system. In some aspects, the IDS 190 can include an insulin pump, an infusion set, a CGM (e.g., the analyte sensor 122), a controller (e.g., an algorithm that calculates and dynamically adjusts insulin delivery based on CGM values and trends), or a combination thereof. In some aspects, the IDS 190 can include an insulin pen (e.g., a smart insulin pen).

[0110] In some aspects, the IDS 190 can be operatively coupled to the OBU 120, the display device 130, the remote server 180, and / or the software application 300, for example, to form a closed loop system for automatically delivering insulin to the patient at the right time and in the right amount. In some aspects, the software application 300 can control the amount of insulin delivered by the IDS 190 based on the detected condition. In some aspects, the software application 300 can control the delivery of insulin from the IDS 190 based on the ketone level (e.g., the second analyte level 314b). For example, the software application 300 can be configured to continue delivering insulin from the IDS 190 if the ketone level is above a high ketone threshold (e.g., above about 3.0 mmol / L).

[0111] In some aspects, the software application 300 can override the default or scheduled insulin delivery from the IDS 190 when the ketone level is elevated (e.g., above 3.0 mmol / L). For example, the IDS 190 can normally stop or reduce insulin delivery if only glucose levels are considered (e.g., normal glucose levels, detected glucose error, detected glucose failure, etc.), but if the ketone level is high (e.g., above 3.0 mmol / L), the software application 300 can control the IDS 190 to continue insulin delivery, thereby mitigating euDKA.

[0112] The software application 300 can be configured to obtain sensor data (e.g., the sensor data 312) of the first analyte level and the second analyte level (e.g., the first analyte level 314a and the second analyte level 314b). The software application 300 can also be configured to detect a condition (e.g., the condition detection 320) based on the sensor data (e.g., the sensor data 312) from the analyte sensor 122. The software application 300 can also be configured to provide a notification (e.g., the notification 352) to the patient based on the condition (e.g., the conditional logic states 10, 20, 30, 40, 50, 60, 70). As Alternative alert implementationsAs shown, the software application 300 can be operatively coupled to the OBU 120, the display device 130, the remote server 180, and the IDS 190. In some aspects, the software application 300 can include one or more processors (e.g., processors, controllers, microprocessors, microcontrollers, ASICs, etc.). In some aspects, the software application 300 can include and / or be coupled to a memory that stores instructions, e.g., that, when executed, cause one or more processors of the software application 300 to include, without limitation, acquire sensor data of a first analyte level and a second analyte level, detect a condition based on the sensor data, provide a notification to the patient based on the condition, prompt the patient to input contextual data (e.g., based on the condition), save a record of the detected condition with the contextual data, and / or generate a report including the detected condition and associated contextual data.

[0113] In some aspects, the software application 300 can be part of the analyte measurement system 110. For example, the software application 300 can be part of the display device 130. In some aspects, the software application 300 can include a mobile application (app). For example, the software application 300 can be part of the display device 130 (e.g., in a mobile application). In some aspects, the remote server 180 can be configured to support all or part of the software application 300. In some aspects, the software application 300 can be included entirely in a patient mobile application (app). In some aspects, some or part of the software application 300 can be included in the remote server 180 (e.g., web server, cloud server, etc.) that supports the software application 300. For example, the remote server 180 can support processing, communication, and / or reporting functions of the software application 300. In some aspects, the software application 300 can include an application program interface (API) for two or more computer programs to communicate with each other (e.g., the conditional logic system 310, the settings system 330, the notification system 350, the dose control system 370, etc.).

[0114] In some aspects, software application 300 can include a mobile application-based system that detects conditions for which action should be taken, provides guidance to the patient, and provides a means to record important contextual data that coincided with the detected condition, e.g., contextual data that will help the HCP later understand when advising the patient how to avoid the detected condition in the future. In some aspects, all of the processing and functionality required by software application 300 can be contained in the mobile application. In some aspects, some or portions of software application 300 (e.g., the mobile application) can be contained in a remote server 180 that supports software application 300, e.g., remote server 180 can support a mobile application with processing, communication hub, and reporting functionality. In some aspects, the functionality described herein for software application 300 includes functionality on the mobile application, remote server 180, or both, noting that all or some of the functionality can be in either or both. In some aspects, a reference to software application 300 described herein implies both the mobile application and the web server (e.g., remote server 180) that supports the mobile application.

[0115] In some aspects, software application 300 (e.g., the mobile application) can acquire continuous glucose sensor data (e.g., first analyte level 314a) and continuous ketone sensor data (e.g., second analyte level 314b) in real-time (e.g., continuously monitor 318). In some aspects, these data can be from two separate analyte sensors, or a dual-analyte sensor (e.g., analyte sensor 122), where a single sensor provides data for both analytes. In some aspects, software application 300 can acquire intermittent discrete glucose measurements and / or discrete ketone measurements. In some aspects, software application 300 can acquire various combinations of discrete and continuous analyte measurements. In some aspects, software application 300 can include a mechanism (e.g., an algorithm) to acquire discrete analyte measurements in the event that continuous analyte measurements are unavailable for a sensing duration.

[0116] In some aspects, software application 300 (e.g., the mobile application) can acquire and process sensor data from OBU 120 in near real-time (e.g., about 1 to 15 minutes), e.g., every minute, every 5 minutes, every 10 minutes, every 15 minutes, etc. In some aspects, software application 300 (e.g., the mobile application) can acquire and process sensor data from OBU 120 in real-time (e.g., about 1 to 60 seconds), e.g., every second, every 5 seconds, every 10 seconds, every 15 seconds, every 30 seconds, every 60 seconds, etc.

[0117] In some aspects, the software application 300 (e.g., mobile application) can acquire and process (e.g., in real-time) the first analyte 123a (e.g., glucose) and / or second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol) data at different frequencies (e.g., every minute for glucose, and every 15 minutes for ketones). In some aspects, the software application 300 (e.g., mobile application) can acquire and process (e.g., in real-time) the first analyte 123a (e.g., glucose) and second analyte 123b (e.g., ketones, lactate, lactic acid, alcohol) data at different time periods at different times of day. For example, depending on the likelihood of alcohol consumption (e.g., higher likelihood in the evening than in the morning), the real-time processing of the software application 300 can be suspended or limited to a low period (e.g., hourly measurements) during the morning when alcohol consumption is less likely, for example, to minimize unnecessary power consumption and / or communication bandwidth of the software application 300.

[0118] In some aspects, the software application 300 (e.g., mobile application) can be used for situations where high ketones can require attention. For example, the software application 300 can be used for type 1 diabetic patients taking SGLT-2 inhibitors, for example, to mitigate the risk of euDKA. In some aspects, the software application 300 (e.g., mobile application) can be operatively coupled (e.g., wirelessly) to the patient’s insulin delivery or insulin guidance system (e.g., IDS 190) to automatically determine whether the patient has some form of insulin resistance (e.g., caused by disease, overweight, metabolic syndrome, stroke, high triglycerides, etc.). For example, the software application 300 can determine whether insulin correction doses do not appear to lower glucose levels.

[0119] Exemplary flowchart

[0120] In some implementations, the analyte monitoring system can provide an alarm display when specific conditions are met. For example, an alarm threshold can be set to alert users that their ketone levels are rising in an effort to prevent potential DKA or euglycemic DKA. The alarm display can be configured to show or output an alarm if the ketone level is above a first threshold (e.g., about 1.0 mmol / L). If the ketone level is above a second threshold (e.g., about 1.5 mmol / L), an additional alarm can be shown or output. If the ketone level is above a third threshold (e.g., about 2.0 mmol / L), an additional alarm can be shown or output. If the ketone level is above a fourth threshold (e.g., about 3.0 mmol / L), an additional alarm can be shown or output. If an alarm is triggered, the notification associated with the alarm can include the numerical value of the ketone level that triggered the alarm, for example, ketone level: 2.4 mmol / L. In some implementations, alarms can be output periodically when the ketone level is above at least one threshold. For example, an alarm may be issued every 5 minutes, alternatively every 10 minutes, alternatively every 15 minutes, or alternatively every 30 minutes. In some implementations, the periodicity of the alarm may depend on the ketone level or the ketone threshold that is triggered. For example, the interval between alarms triggered by ketone levels above a first threshold may be greater than the interval between alarms triggered by ketone levels above a second, higher threshold. For example, if the thresholds are 1.0 mmol / L and 1.5 mmol / L, then the alarm interval for a ketone level of 1.4 mmol / L may be every 15 minutes, while the alarm interval for a ketone level of 2.6 mmol / L may be every 5 minutes.

[0121] The alarm notifications described in the various embodiments herein may include message alarms, banner alarms, lock screen alarms, swipe-up notifications, or any other alarms known in mobile application design. The notification may include haptic components, such as vibration. The notification may include auditory components, such as a beeping sound.

[0122] FIG. 2A

[0123] FIG. 1 It is shown according to the exemplary aspects FIG. 2A The analyte monitoring system flowchart 200A of the illustrated analyte monitoring system 100 is shown. The analyte monitoring system flowchart 200A can be configured to measure a patient's first and second analytes (e.g., glucose and ketones), detect current or impending adverse conditions (e.g., glucose-ketone status), provide the patient with customized notifications (e.g., alerts, suggestions, guidance) to take action, and / or prompt the patient with additional information related to the detected condition. It should be understood that additional information is not required. FIG. 2Aall steps in the methods described herein. Moreover, some steps can be performed simultaneously, sequentially, and / or in an order different than FIG. 1 FIG. 3 , FIG. 4A to FIG. 10B and FIG. 2A will be described with reference to the analyte monitoring system flowchart 200A. However, the analyte monitoring system flowchart 200A is not limited to those example aspects. While the analyte monitoring system flowchart 200A is shown as a standalone method in FIG. 1 , aspects of the present disclosure can be used with other devices, systems, and / or methods, such as the analyte monitoring system 100, the software application flowchart 200B, and / or the software application 300. In some aspects, the analyte monitoring system flowchart 200A can be implemented by the analyte monitoring system 100 and / or the software application 300 as shown. FIG. 1

[0124] In step 202A, as shown in the examples of FIG. 3 and FIG. 1 , a first analyte 123a and a second analyte 123b of a patient can be measured with the analyte measurement system 110. In some aspects, the analyte measurement system 110 can include an analyte sensor 122 and a display device 130. In some aspects, the measurement can include continuous measurement of the first analyte 123a and the second analyte 123b in real-time (e.g., about 1 second to 60 seconds). In some aspects, the measurement can include measurement of the first analyte 123a and the second analyte 123b in near real-time (e.g., about 1 minute to 15 minutes). In some aspects, the measurement can include continuous ketone monitoring to account for dynamic effects of ketones, for example, on blood glucose response.

[0125] In step 204A, as shown in the examples of FIG. 3 and FIG. 1 , sensor data 312 of the first analyte level 314a and the second analyte level 314b can be acquired with the software application 300 from the OBU 120. In some aspects, the software application 300 (e.g., a mobile application) can be operatively coupled (e.g., wirelessly) to the analyte measurement system 110. In some aspects, the software application 300 can acquire and process the sensor data 312 in real-time (e.g., every second, every 5 seconds, every 10 seconds, every 15 seconds, every 30 seconds, every 60 seconds, etc.). In some aspects, the software application 300 can acquire and process the sensor data 312 in near real-time (e.g., every minute, every 5 minutes, every 10 minutes, every 15 minutes, etc.).

[0126] In step 206A, as shown in the examples of FIG. 3 , FIG. 4A to FIG. 10B , FIG. 1 ​​As illustrated in the example, a condition can be detected based on sensor data 312 (e.g., condition detection 320). In some aspects, software application 300 can detect current or impending adverse conditions (e.g., glucose-ketone conditions) at all times or at appropriate times of day (e.g., low periodicity during the morning). In some aspects, detection may include the use of conditional logic (e.g., conditional logic system 310) based on predetermined settings (e.g., predetermined settings 332). In some aspects, predetermined settings may include a first threshold and a second threshold (e.g., first analyte threshold 336) for a first analyte 123a and a third and a fourth threshold (e.g., second analyte threshold 338) for a second analyte 123b. In some aspects, detection may utilize default or editable thresholds (e.g., glucose threshold and ketone threshold) to provide notifications (e.g., alarms, suggestions, guidance, prompts) associated with each conditional logic state defined by the threshold.

[0127] In step 208A, as FIG. 3 , FIG. 4A to FIG. 10B , FIG. 1 As illustrated in the example, notifications can be provided to patients based on their condition (e.g., notification 352). In some aspects, providing notifications may include providing alerts (e.g., alert 354), suggestions (e.g., 356), or a combination thereof. In some aspects, providing notifications may include providing patients with customized notifications (e.g., alerts, suggestions, guidance) to take action based on detected current or impending adverse conditions (e.g., glucose-ketosis).

[0128] In step 210A, optionally, as FIG. 3 , FIG. 4A to FIG. 10B , FIG. 5B As illustrated in the example, a prompt (e.g., prompt 358) may be provided to the patient to request additional information about the condition (e.g., contextual data 362). In some aspects, the prompt may be provided at an appropriate time in relation to the detected condition (e.g., immediately after the condition is detected). In some aspects, providing the prompt may include capturing additional information about the patient's condition (e.g., contextual data 362) at a time when the patient will remember it, for example, to help clinicians or HCPs determine the root cause of the condition. In some aspects, contextual data (e.g., contextual data 362) may be associated with a specific detected condition; for example, hyperglycemia may be associated with first contextual data, while hypoglycemia may be associated with second contextual data. In some aspects, contextual data (e.g., contextual data 362) may be entered as free-form text via input from display device 130 (e.g., input component 134), or as free-form audio via input from display device 130 (e.g., input component 134), or from, for example, a dropdown list (e.g., ...). FIG. 2BSelect from the prompt shown in 508b).

[0129] FIG. 1 It is shown according to the exemplary aspects FIG. 3 and FIG. 2B The software application flowchart 200B of software application 300 is shown. Software application flowchart 200B can be configured to detect current or impending adverse conditions (e.g., glucose-ketosis) and provide the patient with customized notifications (e.g., alerts, suggestions, guidance) to take action at an appropriate time. It should be understood that it is not necessary to... FIG. 2B All steps in the document are used to perform the disclosure provided herein. Furthermore, some steps may be performed simultaneously, sequentially, and / or in conjunction with... FIG. 1 The different execution orders shown are illustrated. (Refer to...) FIG. 3 , FIG. 4A to FIG. 10B and FIG. 2B The software application flowchart 200B is described. However, the software application flowchart 200B is not limited to those example aspects. Although the software application flowchart 200B is in FIG. 1 While shown as a standalone method, aspects of this disclosure can be used with other apparatus, systems, and / or methods, such as analyte monitoring system 100, analyte monitoring system flowchart 200A, and / or software application 300. In some aspects, software application flowchart 200B can be derived from... FIG. 3 and FIG. 1 The software application shown is implemented using 300.

[0130] In step 202B, as FIG. 3 and FIG. 1 As shown in the example, sensor data 312 for the patient's first analyte level 314a and second analyte level 314b can be acquired. In some aspects, the software application 300 can acquire and process the sensor data 312 in real time (e.g., every second, every 5 seconds, every 10 seconds, every 15 seconds, every 30 seconds, every 60 seconds, etc.). In some aspects, the software application 300 can acquire and process the sensor data 312 in near real time (e.g., every minute, every 5 minutes, every 10 minutes, every 15 minutes, etc.).

[0131] In step 204B, as FIG. 3 and FIG. 1As shown by the example of FIG. 3, it can be determined whether the first analyte level 314a and the second analyte level 314b are below or above predetermined settings (e.g., predetermined settings 332, thresholds 334). In some aspects, the predetermined settings (e.g., predetermined settings 332) can include one or more default thresholds (e.g., thresholds 334). In some aspects, the thresholds 334 can include a first analyte threshold 336 (e.g., one or more glucose thresholds) and a second analyte threshold 338 (e.g., one or more ketone thresholds). In some aspects, the first analyte threshold 336 can include, but is not limited to, a low glucose threshold (e.g., about 70 mg / dL), a medium glucose threshold (e.g., about 110 mg / dL), and / or a high glucose threshold (e.g., about 180 mg / dL). In some aspects, the second analyte threshold 338 can include, but is not limited to, a low ketone threshold (e.g., about 0.5 mmol / L), a medium ketone threshold (e.g., about 1.0 mmol / L), and / or a high ketone threshold (e.g., about 3.0 mmol / L). In some aspects, the predetermined settings (e.g., predetermined settings 332, thresholds 334, adjustment settings 340, etc.) can extend to any number of predetermined settings and / or thresholds, e.g., where different text (e.g., notifications 352, alerts 354, suggestions 356, prompts 358) are associated with each conditional logic state defined by the predetermined settings and / or thresholds.

[0132] In step 206B, as FIG. 3 、 FIG. 4A to FIG. 10B 、 FIG. 1As shown in the example of FIG. 3, a condition can be detected (e.g., condition detection 320) based on whether the first analyte level 314a and the second analyte level 314b are below or above predetermined settings. In some aspects, the detection can include utilizing conditional logic (e.g., conditional logic system 310) based on predetermined settings (e.g., predetermined settings 332). In some aspects, the predetermined settings can include first and second thresholds for the first analyte 123a (e.g., first analyte thresholds 336) and third and fourth thresholds for the second analyte 123b (e.g., second analyte thresholds 338). In some aspects, the first analyte thresholds 336 can include, but are not limited to, a low glucose threshold (e.g., about 70 mg / dL), a medium glucose threshold (e.g., about 110 mg / dL), and / or a high glucose threshold (e.g., about 180 mg / dL), for example, the first threshold for the first analyte 123a can be a low glucose threshold (e.g., about 70 mg / dL, at least 70 mg / dL, about 60 mg / dL to about 80 mg / dL, etc.) and the second threshold for the first analyte 123a can be a high glucose threshold (e.g., about 180 mg / dL, at least 180 mg / dL, about 140 mg / dL to about 220 mg / dL, etc.). In some aspects, the second analyte thresholds 338 can include, but are not limited to, a low ketone threshold (e.g., about 0.5 mmol / L), a medium ketone threshold (e.g., about 1.0 mmol / L), and / or a high ketone threshold (e.g., about 3.0 mmol / L), for example, the third threshold for the second analyte 123b can be a medium ketone threshold (e.g., about 1.0 mmol / L, at least 1.0 mmol / L, about 0.8 mmol / L to about 1.0 mmol / L) and the fourth threshold for the second analyte 123b can be a high ketone threshold (e.g., about 3.0 mmol / L, at least 3.0 mmol / L, about 2.5 mmol / L to about 3.5 mmol / L, etc.). In some aspects, the detection can utilize default or editable thresholds (e.g., glucose thresholds and ketone thresholds) to provide notifications (e.g., alerts, suggestions, guidance, tips) associated with each conditional logic state defined by the thresholds.

[0133] In step 208B, as FIG. 3 、 FIG. 4A to FIG. 10B 、 FIG. 1 As shown in the example of FIG. 3, a notification can be provided to the patient based on the condition (e.g., notification 352). In some aspects, providing the notification can include providing an alert (e.g., alert 354), a suggestion (e.g., 356), or a combination thereof. In some aspects, providing the notification can include providing a customized notification (e.g., alert, suggestion, guidance) to the patient to take action based on the detected current or impending adverse condition (e.g., glucose-ketone condition).

[0134] In step 210B, optionally, as FIG. 3 , FIG. 4A to FIG. 10B , Exemplary software application As illustrated in the example, a prompt (e.g., prompt 358) may be provided to the patient to obtain additional information about the condition (e.g., contextual data 362). In some aspects, the additional information (e.g., contextual data 362) may include contextual information related to the detected condition to help clinicians or HCPs determine the root cause of the condition. For example, contextual information may include patient discomfort (e.g., the degree of discomfort in the patient's digits, the patient's selection from a list of degrees of discomfort / descriptions, etc.), causative factors, medications taken, frequency of the condition (e.g., the number of times the condition occurred in at least one of 1 hour, 6 hours, 12 hours, 1 day, 1 week, 1 month, etc.), whether the patient received emergency medical services, etc. In some aspects, the prompt may be provided at an appropriate time related to the detected condition (e.g., immediately after the condition is detected). In some aspects, providing the prompt may include capturing additional information about the patient's condition (e.g., contextual data 362) at a time when the patient will remember it, for example, to help clinicians or HCPs determine the root cause of the condition.

[0135] FIG. 3

[0136] FIG. 3 A software application 300 according to an exemplary aspect is illustrated. The software application 300 can be configured to measure sensor data 312 of analyte sensor 122, including a patient's first analyte level 314a and a second analyte level 314b (e.g., glucose and ketones). The software application 300 can also be configured to detect current or impending adverse conditions (e.g., glucose-ketosis) and provide the patient with customized notifications (e.g., alerts, suggestions, guidance) to take action. The software application 300 can also be configured to prompt the patient with additional information related to the detected condition. The software application 300 can also be configured to continuously monitor the first analyte level 314a and the second analyte level 314b (e.g., glucose and ketone levels) in real time. The software application 300 can also be configured to control insulin delivery (e.g., via IDS 190) based on ketone levels and mitigate the risk of euDKA. Although the software application 300 in… FIG. 3 While each aspect of this disclosure is shown as a separate device and / or system, it may be used in conjunction with other devices, systems, and / or methods, such as analyte monitoring system 100, analyte measurement system 110, analyte monitoring system flowchart 200A, software application flowchart 200B, and / or status diagrams 400A to 1000A and corresponding display notifications 400B to 1000B.

[0137] like FIG. 3As shown, the software application 300 can include a conditional logic system 310, a settings system 330, a notification system 350, and / or a dose control system 370. The conditional logic system 310 can be configured to obtain sensor data 312 (e.g., first analyte 123a and second analyte 123b) from an analyte sensor 122 of the analyte measurement system 110. The conditional logic system 310 can also be configured to determine whether the first analyte level 314a and the second analyte level 314b of the sensor data 312 are below or above a predetermined setting 332. The conditional logic system 310 can also be configured to detect a condition (e.g., condition detection 320) based on whether the first analyte level 314a and the second analyte level 314b of the sensor data 312 are below or above the predetermined setting 332.

[0138] The conditional logic system 310 can be operatively coupled to the settings system 330, the notification system 350, and / or the dose control system 370. As FIG. 3 shown, the conditional logic system 310 can include sensor data 312, continuous monitoring 318, condition detection 320, error detection 322, and / or predictive model 324.

[0139] The sensor data 312 can be configured to measure one or more analytes (e.g., glucose and ketones) of a patient. As FIG. 3 shown, the sensor data 312 can include a first analyte level 314a (e.g., glucose level), a second analyte level 314b (e.g., ketone level), a first analyte rate of change (ROC) 316a (e.g., glucose ROC), and / or a second analyte ROC 316b (e.g., ketone ROC). In some aspects, the first analyte level 314a (e.g., glucose level) and / or the second analyte level 314b (e.g., ketone level) can be estimated in a variety of ways. For example, the first analyte level 314a and the second analyte level 314b can each be estimated by a mean (average), a median, a mode, a weighted mean, a geometric mean, a moving average, a weighted median, a weighted mode, a mid-range, or a combination thereof. In some aspects, the predetermined setting 332 can depend on the first analyte level 314a (e.g., glucose level) and / or the second analyte level 314b (e.g., ketone level).

[0140] In some aspects, the first analyte ROC 316a (e.g., glucose ROC) and / or the second analyte ROC 316b (e.g., ketone ROC) can be estimated in a variety of ways. For example, the second analyte ROC 316b (e.g., ketone ROC) can be estimated by determining the slope of a recent 15 minute window of the second analyte levels 314b (e.g., ketone levels). In some aspects, the predetermined settings 332 can additionally depend on the first analyte ROC 316a (e.g., glucose ROC) and / or the second analyte ROC 316b (e.g., ketone ROC). For example, the moderate ketone range can be defined as a second analyte level 314b (e.g., ketone level) greater than about 1.0 mmol / L, or alternatively, a second analyte level 314b (e.g., ketone level) greater than about 0.5 mmol / L and a second analyte ROC 316b (e.g., ketone ROC) greater than about 0.3 mmol / L / hr calculated based on recent data (e.g., past 15 minutes, half hour, 2 hours, or other predetermined duration).

[0141] Continuous monitoring 318 can be configured to continuously monitor one or more analytes (e.g., glucose and ketones) of a patient. As shown, the continuous monitoring 318 can continuously monitor sensor data 312 from an analyte sensor 122, e.g., CGM and continuous ketone monitoring. In some aspects, the continuous monitoring 318 can monitor the sensor data 312 in real-time (e.g., every second, every 5 seconds, every 10 seconds, every 15 seconds, every 30 seconds, every 60 seconds, etc.). In some aspects, the continuous monitoring 318 can include continuous ketone monitoring (e.g., second analyte levels 314b) to account for dynamic effects of ketones, e.g., on glycemic response. In some aspects, the continuous monitoring 318 can monitor the sensor data 312 near real-time (e.g., every minute, every 5 minutes, every 10 minutes, every 15 minutes, etc.). FIG. 3

[0142] Condition detection 320 can be configured to detect a condition (e.g., conditional logic state) based on whether the sensor data 312 (e.g., first analyte levels 314a and second analyte levels 314b) is below or above the predetermined settings 332. As shown, the condition detection 320 can receive the sensor data 312 and compare the sensor data 312 to the predetermined settings 332 (e.g., thresholds 334). In some aspects, the condition detection 320 can detect a conditional logic state based on the predetermined settings (e.g., predetermined settings 332, thresholds 334). For example, as shown, the condition detection 320 can detect a conditional logic state based on the predetermined settings 332 (e.g., thresholds 334) and the sensor data 312 (e.g., first analyte levels 314a and second analyte levels 314b). FIG. 4A FIG. 5A FIG. 6A FIG. 7A FIG. 8A FIG. 9A FIG. 10A FIG. 4A ​​​​​​​​As shown in the state diagrams 400A-1000A, the condition detection 320 can detect the conditional logic states 10, 20, 30, 40, 50, 60, 70 based on a comparison of the sensor data 312 to predetermined settings 332 (e.g., thresholds 334), respectively.

[0143] In some aspects, the condition detection 320 can detect a high ketones condition (e.g., ketones level elevated), a medium ketones condition (e.g., ketones level decreased), a low ketones condition (e.g., ketones level rapidly recovered), a low ketones condition (e.g., ketones level recovered), a medium ketones and high glucose condition (e.g., medium ketones-high glucose condition), a medium ketones and medium glucose condition (e.g., medium ketones-medium glucose condition), and / or a medium ketones and low glucose condition (e.g., medium ketones-low glucose condition).

[0144] In some aspects, the condition detection 320 can detect a high ketones condition (ketones level elevated). For example, as shown in the state diagram 400A, the condition detection 320 can detect a first conditional logic state 10, e.g., a high ketones condition when the ketones level transitions from below a high ketones threshold (e.g., about 3.0 mmol / L) to above the high ketones threshold. FIG. 5A In some aspects, the condition detection 320 can detect a medium ketones condition (ketones level decreased). For example, as shown in the state diagram 500A, the condition detection 320 can detect a second conditional logic state 20, e.g., a medium ketones condition when the ketones level transitions from above a high ketones threshold (e.g., about 3.0 mmol / L) to below the high ketones threshold. FIG. 6A In some aspects, the condition detection 320 can detect a medium ketones condition (ketones level decreased). For example, as shown in the state diagram 500A, the condition detection 320 can detect a second conditional logic state 20, e.g., a medium ketones condition when the ketones level transitions from above a high ketones threshold (e.g., about 3.0 mmol / L) to below the high ketones threshold.

[0145] In some aspects, the condition detection 320 can detect a low ketones condition (ketones level rapidly recovered). For example, as shown in the state diagram 600A, the condition detection 320 can detect a third conditional logic state 30, e.g., a low ketones condition when the ketones level transitions from above a high ketones threshold (e.g., about 3.0 mmol / L) to below a low ketones threshold (e.g., about 0.5 mmol / L). FIG. 7A In some aspects, the condition detection 320 can detect a low ketones condition (ketones level rapidly recovered). For example, as shown in the state diagram 600A, the condition detection 320 can detect a third conditional logic state 30, e.g., a low ketones condition when the ketones level transitions from above a high ketones threshold (e.g., about 3.0 mmol / L) to below a low ketones threshold (e.g., about 0.5 mmol / L). FIG. 8A In some aspects, the condition detection 320 can detect a low ketones condition (ketones level rapidly recovered). For example, as shown in the state diagram 600A, the condition detection 320 can detect a third conditional logic state 30, e.g., a low ketones condition when the ketones level transitions from above a high ketones threshold (e.g., about 3.0 mmol / L) to below a low ketones threshold (e.g., about 0.5 mmol / L).

[0146] In some aspects, the condition detection 320 can detect a medium ketones and high glucose condition (medium ketones-high glucose condition). For example, as shown in the state diagram 700A, the condition detection 320 can detect a fourth conditional logic state 40, e.g., a medium ketones and high glucose condition when the ketones level transitions from above a medium ketones threshold (e.g., about 1.0 mmol / L) to below a low ketones threshold (e.g., about 0.5 mmol / L). FIG. 9AAs shown, when ketone levels are below the high ketone threshold (e.g., about 3.0 mmol / L) and above the low ketone threshold (e.g., about 0.5 mmol / L), and glucose levels are above the high glucose threshold (e.g., about 180 mg / dL), condition detection 320 can detect a fifth conditional logic state 50, such as a moderate ketone-hyperglucose state.

[0147] In some aspects, the Condition Detector 320 can detect moderate ketosis and moderate glucose status (moderate ketosis-moderate glucose status). For example, as... FIG. 10A As shown, when ketone levels are below the high ketone threshold (e.g., about 3.0 mmol / L) and above the low ketone threshold (e.g., about 0.5 mmol / L), and glucose levels are below the high glucose threshold (e.g., about 180 mg / dL) and above the low glucose threshold (e.g., about 70 mg / dL), condition detection 320 can detect a sixth conditional logic state 60, such as a moderate ketone-moderate glucose state.

[0148] In some aspects, the Condition Detector 320 can detect moderate ketosis and hypoglycemia (moderate ketosis-hypoglycemia). For example, as... FIG. 3 As shown, when ketone levels are below the high ketone threshold (e.g., about 3.0 mmol / L) and above the low ketone threshold (e.g., about 0.5 mmol / L), and glucose levels are below the low glucose threshold (e.g., about 70 mg / dL), condition detection 320 can detect a seventh conditional logic state 70, such as a moderate ketone-hypoglycemic state.

[0149] In some aspects, condition detection 320 (e.g., conditional logic) may include hysteresis (e.g., hysteresis of changes in the effects leading to the measured value), for example, to reduce unstable variations in the detected condition due to noisy ROC estimates. For example, if a detected condition is determined to be at a moderate ketone level (e.g., about 1.0 mmol / L) based on a second analyte ROC 316b (e.g., a ketone ROC), for example, greater than about 0.3 mmol / L / hr, then to return to a low ketone level (e.g., about 0.5 mmol / L), the conditional logic may be limited to a second analyte level 314b (e.g., a ketone level) less than about 1.0 mmol / L and a second analyte ROC 316b (e.g., a ketone ROC) less than about 0 mmol / L / hr.

[0150] Error detection 322 can be configured to detect errors in sensor data 312. For example... FIG. 3As shown, error detection 322 can receive sensor data 312 and perform error detection. In some aspects, error detection 322 can periodically perform quality checks of sensor data 312, e.g., error detection, potential error detection, error verification, and / or error correction. In some aspects, error detection 322 can perform error correction on errors detected in sensor data 312. In some aspects, error detection 322 can be configured to improve the signal-to-noise ratio (SNR) of sensor data 312, e.g., by utilizing one or more error detection techniques (e.g., parity check, cyclic redundancy check, forward error correction, automatic repeat request, error-correcting code, etc.).

[0151] In some aspects, error detection 322 can be configured to determine an erroneous reading based on first analyte level 314a (e.g., glucose level) and / or second analyte level 314b (e.g., ketone level). For example, error detection 322 can detect an indication of euDKA or an erroneous glucose reading (e.g., low glucose when glucose level is actually high) based on a high ketone level and a low or normal glucose level. In some aspects, error detection 322 can utilize second analyte level 314b (e.g., ketone level) to detect an error in first analyte level 314a (e.g., glucose level). In some aspects, error detection 322 can utilize first analyte level 314a (e.g., glucose level) to detect an error in second analyte level 314b (e.g., ketone level).

[0152] In some aspects, software application 300 can detect an indication of euDKA or an erroneous glucose reading (e.g., error detection 322) based on a high ketone level and a low or normal glucose level, and accordingly notify the patient to take appropriate action, e.g., perform a blood glucose measurement (e.g., blood glucose test strip) to confirm glucose level. In some aspects, software application 300 can detect an indication of euDKA or an erroneous ketone reading (e.g., error detection 322) based on a high ketone level and a low or normal glucose level, and accordingly notify the patient to take appropriate action, e.g., perform a blood ketone measurement (e.g., blood ketone test strip) to confirm ketone level.

[0153] In some aspects, the analyte sensors 122 can include a ketone sensor and a glucose sensor, and the ketone sensor can be used to detect faults in the glucose sensor, and the glucose sensor can be used to detect faults in the ketone sensor. In some aspects, the error detection 322 can detect a glucose sensor fault based on a high glucose level and a low ketone level, e.g., if the software application 300 detects that insulin was recently delivered (e.g., via the IDS 190). In some aspects, the error detection 322 can detect a ketone sensor fault based on a high glucose level and a low ketone level, e.g., if the software application 300 detects that insulin was not recently delivered (e.g., via the IDS 190).

[0154] In some aspects, the software application 300 can be operatively coupled (e.g., wirelessly) to an insulin delivery system (e.g., the IDS 190) or an insulin pen (e.g., a smart insulin pen), and insulin delivery data can be analyzed by the error detection 322, e.g., to confirm that a glucose sensor (e.g., the analyte sensor 122) is functioning properly. In some aspects, the software application 300 can include a predictive model (e.g., the predictive model 324) that can include insulin delivery data and / or insulin dose guidance information, e.g., whether the patient is seeking guidance for a prandial insulin dose or a high glucose correction dose. In some aspects, the predictive model (e.g., the predictive model 324) can take into account the time of day, e.g., overnight (e.g., between 11:00 PM and 7:00 AM), as ketones tend to rise overnight due to the patient’s fasting. In some aspects, the error detection 322 can detect a glucose sensor fault, e.g., if a measured glucose level (e.g., the first analyte level 314a) is significantly different from a predicted glucose level (e.g., via the predictive model 324). In some aspects, the error detection 322 can detect a ketone sensor fault, e.g., if a measured ketone level (e.g., the second analyte level 314b) is significantly different from a predicted ketone level (e.g., via the predictive model 324).

[0155] In some aspects, error detection 322 can utilize the analyte sensors 122 (e.g., dual glucose-ketone sensor, separate glucose sensor and ketone sensor) to detect a problem with pump delivery of insulin (e.g., infusion set occlusion) or a problem with pen delivery of insulin. For example, error detection 322 can detect a failure of insulin delivery (e.g., some or all of the insulin is not delivered to the patient) based on high ketone levels and high glucose levels and a record of insulin delivery (e.g., via IDS 190). In some aspects, if a failure of insulin delivery is detected (e.g., error detection 322), software application 300 can provide guidance and instructions (e.g., notification 352) to the patient to check the functionality of the insulin delivery system (e.g., IDS 190).

[0156] In some aspects, error detection 322 can detect a failure of pump occlusion based on ketone levels rising faster than glucose levels. For example, error detection 322 can detect that the second analyte ROC 316b (e.g., ketone ROC) has crossed a predetermined ketone ROC threshold (e.g., about 0.3 mmol / L / hr) followed by the first analyte ROC 316a (e.g., glucose ROC) crossing a predetermined glucose ROC threshold (e.g., about 35 mg / dL / hr). In some aspects, error detection 322 can detect abnormal sensor decay (e.g., reduced sensitivity) based on high ketone levels occurring simultaneously with low glucose levels. For example, abnormal sensor decay can occur at the beginning of sensor life, later in sensor life, or during sensor life (e.g., when the patient applies pressure to the sensor).

[0157] In some aspects, in an AID system (or similarly, with a manual injection of insulin system), if the glucose sensor is reading low (e.g., analyte sensor 122) incorrectly, insulin delivery can be low or paused (e.g., IDS 190). For example, glucose levels can be rising undetected, but ketone levels can be rising and detected. In some aspects, based on high ketone levels (e.g., error detection 322), software application 300 can notify the patient (e.g., notification 352) to confirm their glucose levels with a blood glucose test strip.

[0158] In some aspects, error detection 322 can detect abnormal events and / or sensor errors retrospectively. For example, when elevated ketone levels are detected to occur simultaneously with low insulin delivery data, a report generation process (e.g., report 364) can be caused to exclude glucose data from the report calculations prior to (e.g., about 4 hours) and up to the point in time that no longer indicates the detected condition.

[0159] In some aspects, error detection 322 and / or IDS 190 can include a pump occlusion detection subsystem. For example, the pump occlusion detection subsystem can include a method for measuring tubing pressure during insulin delivery (e.g., a pressure sensor). In some aspects, error detection 322 can notify the patient of a possible pump occlusion, e.g., if the tubing pressure exceeds a tubing pressure threshold during insulin delivery. In some aspects, error detection 322 can detect a possible pump occlusion based on the first analyte level 314a (e.g., glucose) and / or the second analyte level 314b (e.g., ketones, lactate, lactic acid, alcohol), and adjust one or more parameters of the pump occlusion detection subsystem, e.g., lower the tubing pressure threshold for detecting an occlusion. In some aspects, the parameters and outputs of a glucose-ketone based occlusion detector (e.g., error detection 322) and a pressure based occlusion detector (e.g., IDS 190) can be integrated together in a number of ways to provide a more reliable occlusion detection method, e.g., in a predictive model (e.g., predictive model 324). In some aspects, if both error detection 322 and IDS 190 are deemed to generate high certainty detections that contradict each other, an alternative response can be triggered. For example, if error detection 322 infers that the ketone level is unreasonably increasing for a given glucose history, the increase in ketone level can be associated with a pump occlusion, but if IDS 190 does not indicate abnormal pressure, a different kind of condition, such as but not limited to fat hypertrophy (e.g., scar tissue formation due to repeated injections), can be decreasing the effectiveness of the infusion site. In this case, a prompt to reapply the catheter to a new infusion site can be triggered.

[0160] Predictive model 324 can be configured to analyze sensor data 312 and other information (e.g., insulin delivery data) to model a patient’s future condition. As shown, predictive model 324 can include a population model 326 and / or a predictive algorithm 328. In some aspects, predictive model 324 can be based on one or more parameters, including but not limited to sensor data 312, first analyte level 314a (e.g., glucose), second analyte level 314b (e.g., ketones, lactate, lactic acid, alcohol), first analyte ROC 316a (e.g., glucose ROC), second analyte ROC 316b (e.g., ketone ROC), insulin delivery data (e.g., IDS 190), continuous monitoring 318, condition detection 320, error detection 322, predetermined settings 332, adjusted settings 340, contextual data 362, etc. FIG. 3

[0161] ​In some aspects, the software application 300 can be configured to perform analysis of the sensor data 312 (e.g., periodically) to determine a baseline level of the first analyte level 314a (e.g., glucose) and / or a baseline level of the second analyte level 314b (e.g., ketones, lactate, lactic acid, alcohol). For example, the baseline of the second analyte level 314b (e.g., ketones, lactate, lactic acid, alcohol) can be determined by calculating the median of the ketone levels, assuming that the patient is at a baseline level for most of the time sampled for the calculation. In some aspects, the software application 300 can be configured to perform periodic analysis of the sensor data 312. For example, the software application 300 can determine a baseline level of the first analyte level 314a (e.g., glucose) and / or a baseline level of the second analyte level 314b (e.g., ketones, lactate, lactic acid, alcohol) by calculating a mean (e.g., a sum of sensor data levels divided by a total number of entries), a median (e.g., a middle of an upper half and a lower half of sensor data samples), a linear regression (e.g., a trend line), a non-linear regression, or any other suitable calculation. The period of data used in the calculation can be defined as a period in which the data does not exceed a predetermined level of variability. For example, the period can simply be defined as a period in which the ketone values do not exceed a threshold. After the baseline level is constructed, the system can subtract the baseline level from the ketone values such that the display shows the level as zero. Alternatively, the illustration of the ketone values can indicate zero ketones when the measured ketones are at that level.

[0162] In some aspects, the software application 300 can be configured to perform analysis of the sensor data 312 to determine a predictive model 324. For example, the predictive model 324 can determine a trend line or a future statistical value of the sensor data 312 (e.g., the first analyte level 314a, the second analyte level 314b, etc.). In some aspects, the predictive model 324 can be based on a population model. For example, the predictive model 324 can be based on a population model 326 and one or more parameters that adjust the predictive model 324 to within a known range of variability of the population model 326. In some aspects, the predictive model 324 can be based on a predictive algorithm. For example, the predictive model can be based on a predictive algorithm 328, which can include, but is not limited to, regression, model-based parameter adaptation, supervised machine learning, unsupervised machine learning, semi-supervised machine learning, reinforcement learning, clustering, decision trees, anomaly detection, neural networks, classification models, or combinations thereof. In some aspects, the predictive model 324 can increase the accuracy and precision of the estimate of future analyte levels (e.g., glucose and / or ketone levels) for the patient. In some aspects, the predictive model 324 can estimate the likelihood of a future condition (e.g., a high ketone condition) and present the likelihood to the patient (e.g., a notification).

[0163] In some aspects, the prediction model 324 can be employed using the data inputs and outcome outputs described herein (e.g., sensor data 312), and estimate the likelihood of a hyperketotic event, and present the estimate to the patient. For example, when a condition is detected (e.g., 50% probability of becoming a hyperketotic event), the software application 300 can present the prediction model’s estimate on demand (e.g., as part of the ketones measurement screen) or as a notification (e.g., notification 352).

[0164] In some aspects, the prediction model 324 can be developed using data from a population of patients (e.g., population model 326) based on standard modeling techniques, where the data inputs and outcome outputs described herein (e.g., sensor data 312) are obtained. For example, the prediction model 324 can be adapted (e.g., customized) for a particular patient when sufficient input and / or output data is obtained from the particular patient. In some aspects, the prediction model 324 can be based on a population of patients (e.g., population model 326) used at the beginning of the patient sensor wear. For example, over time, if there are specific parameters that adjust the prediction model 324 to within known ranges of variation of the population model 326, these parameters can be estimated and updated over time. In some aspects, the prediction model 324 can utilize the population model 326 and one or more updated parameters to better estimate the patient’s analyte levels. For example, with updated parameters, the prediction model 324 can better estimate the patient’s ketone levels in the near future. In some aspects, the prediction model 324 can use standard regression techniques, model-based parameter adaptation, supervised machine learning, unsupervised machine learning, semi-supervised machine learning, reinforcement learning, clustering, decision trees, or anomaly detection, e.g., with prediction algorithm 328. In some aspects, the prediction model 324 can use a classification-based approach, e.g., with prediction algorithm 328. For example, the prediction model 324 can use one or more classification models to present a small set of quantified value ranges.

[0165] The settings system 330 can be configured to provide one or more predetermined settings 332 of the software application 300 to the conditional logic system 310, for example. The settings system 330 can also be configured to adjust one or more predetermined settings 332 of the software application 300. The settings system 330 can be operatively coupled to the conditional logic system 310, the notification system 350, and / or the dose control system 370. As FIG. 3As shown, the settings system 330 can include predetermined settings 332, threshold values 334, adjustment settings 340, an input / output (I / O) subsystem 342, and / or a user interface (UI) subsystem 344. In some aspects, the settings system 330 can include an interface (e.g., the UI subsystem 344) to enable an interface with a remote bolus calculator or a dose guidance system (e.g., the dose guidance 374 of the dose control system 370) including an AID system (e.g., the IDS 190).

[0166] The predetermined settings 332 can be configured to provide a comparison value (e.g., a threshold value) to the sensor data 312 for the software application 300 to detect a conditional logic state (e.g., the conditional logic states 10, 20, 30, 40, 50, 60, 70) of the patient. As shown, the predetermined settings 332 can be coupled to the conditional logic system 310 to determine whether the sensor data 312 (e.g., the first analyte level 314a and the second analyte level 314b) is below or above the predetermined settings 332. In some aspects, as shown, the predetermined settings 332 can include one or more threshold values, e.g., the threshold values 334 (e.g., the first analyte threshold 336, the second analyte threshold 338). FIG. 3 FIG. 3 As shown, the predetermined settings 332 can include one or more threshold values, e.g., the threshold values 334 (e.g., the first analyte threshold 336, the second analyte threshold 338).

[0167] In some aspects, the software application 300 can be configured to operate the conditional logic (e.g., the conditional logic system 310) based on the predetermined settings 332. In some aspects, the software application 300 can be configured to adjust the predetermined settings 332. For example, based on one or more parameters of the patient (e.g., a known disease, an SGLT-2 inhibitor medication, an insulin delivery rate, etc.), the software application 300 can adjust (e.g., increase or decrease) one or more threshold values 334. In some aspects, the predetermined settings 332 can include multiple threshold values. For example, the predetermined settings 332 can include the threshold values 334.

[0168] ​In some aspects, the predetermined settings 332 can include a first threshold for a first analyte (e.g., first analyte threshold 336) and a second threshold for a second analyte (e.g., second analyte threshold 338). For example, the first analyte threshold 336 (e.g., glucose) can be a high glucose threshold (e.g., about 180 mg / dL), and the second analyte threshold 338 (e.g., ketones, lactate, lactic acid, alcohol) can be a high ketones threshold (e.g., about 3.0 mmol / L). In some aspects, the predetermined settings 332 can include a first threshold and a second threshold for a first analyte (e.g., first analyte threshold 336) and a third threshold and a fourth threshold for a second analyte (e.g., second analyte threshold 338). For example, the first analyte threshold 336 (e.g., glucose) can be a low glucose threshold (e.g., about 70 mg / dL) and a high glucose threshold (e.g., about 180 mg / dL), and the second analyte threshold 338 (e.g., ketones, lactate, lactic acid, alcohol) can be a moderate ketones threshold (e.g., about 1.0 mmol / L) and a high ketones threshold (e.g., about 3.0 mmol / L).

[0169] In some aspects, the software application 300 can provide default predetermined settings 332 for condition thresholds (e.g., thresholds 334, such as glucose thresholds and ketones thresholds), alert text (e.g., alerts 354), suggested action or treatment text (e.g., suggestions 356), and / or prompt text (e.g., prompts 358). In some aspects, the software application 300 can edit these settings or provide an opportunity to edit these settings (e.g., adjustment settings 340). For example, the software application 300 can update the predetermined settings 332 and require a patient or HCP to confirm the edited settings, or the software application 300 can make these settings available for adjustment (e.g., adjustment settings 340) via a settings menu.

[0170] In some aspects, the predetermined settings 332 can include default thresholds (e.g., thresholds 334). For example, the default thresholds (e.g., first analyte threshold 336 and second analyte threshold 338) can include a low glucose threshold (e.g., about 70 mg / dL), a moderate glucose threshold (e.g., about 110 mg / dL), a high glucose threshold (e.g., about 180 mg / dL), a low ketones threshold (e.g., about 0.5 mmol / L), a moderate ketones threshold (e.g., about 1.0 mmol / L), and / or a high ketones threshold (e.g., about 3.0 mmol / L). In some aspects, the software application 300 (e.g., via the predetermined settings 332) can extend to any number of thresholds (e.g., thresholds 334), with different text associated with each conditional logic state defined by the thresholds.

[0171] Thresholds 334 can be configured to define values or ranges of sensor data 312 to detect conditional logic states (e.g., conditional logic states 10, 20, 30, 40, 50, 60, 70) of a patient. Thresholds 334 can also be configured to provide comparison values or ranges to determine whether sensor data 312 is below, above, or within a threshold 334. As shown in FIG. 3, thresholds 334 can include first analyte thresholds 336 (e.g., one or more glucose thresholds) and second analyte thresholds 338 (e.g., one or more ketone thresholds). In some aspects, thresholds 334 can be default thresholds defining default corresponding conditional logic states. In some aspects, software application 300 can utilize default or editable thresholds 334 (e.g., glucose thresholds and ketone thresholds) to provide notifications (e.g., alerts, recommendations, guidance, prompts) associated with each conditional logic state defined by the thresholds. FIG. 3

[0172] In some aspects, first analyte thresholds 336 can include one or more first analyte thresholds (e.g., one or more glucose thresholds). For example, first analyte thresholds 336 can include a low glucose threshold (e.g., about 70 mg / dL), a medium glucose threshold (e.g., about 110 mg / dL), and / or a high glucose threshold (e.g., about 180 mg / dL). In some aspects, second analyte thresholds 338 can include one or more second analyte thresholds (e.g., one or more ketone thresholds). For example, second analyte thresholds 338 can include a low ketone threshold (e.g., about 0.5 mmol / L), a medium ketone threshold (e.g., about 1.0 mmol / L), and / or a high ketone threshold (e.g., about 3.0 mmol / L).

[0173] Adjustment settings 340 can be configured to adjust one or more parameters of predetermined settings 332. Adjustment settings 340 can also be configured to provide additional information or data to software application 300 to detect conditional logic states of a patient. As shown in FIG. 3, adjustment settings 340 can be coupled to predetermined settings 332 and conditional logic system 310 to adjust one or more thresholds to adjust detection of conditional logic states. FIG. 12

[0174] ​​In some aspects, software application 300 can be configured to adjust conditional logic based on the ROC of a first analyte level (e.g., glucose ROC) and / or the ROC of a second analyte level (e.g., ketone ROC). For example, adjustment setting 340 may include one or more ROC thresholds (e.g., one or more glucose ROC thresholds, one or more ketone ROC thresholds) that can be compared with the first analyte ROC 316a and / or the second analyte ROC 316b of sensor data 312 to detect conditional logic status. In some aspects, software application 300 can be configured to adjust conditional logic based on insulin sensitivity, carbohydrate ratio, patient disease status, patient medication regimen, and / or pairing with a remote dosing system. For example, adjustment setting 340 may detect pairing with a remote dosing system (e.g., IDS 190) and adjust conditional logic based on insulin dose values ​​(e.g., predetermined setting 332). In some aspects, software application 300 can adjust (e.g., optimize) conditional logic based on one or more ranges of various parameters (e.g., glucose ROC, ketone ROC, insulin sensitivity, carbohydrate ratio, patient disease state, patient medication regimen, remote dosing system, etc.). For example, adjustment setting 340 can limit or adjust predetermined setting 332 (e.g., threshold 334) based on one or more equations that correlate various parameters with, for example, insulin dose values. In some aspects, adjustment setting 340 can be based on one or more equations that depend on analyte measurements (e.g., ketone levels). For example, as... FIG. 12 As shown, the equations may include basal insulin (basal) = f(ketones) [units per day], insulin sensitivity (IS) = f(ketones) [mg / dL per unit], and / or carbohydrate ratio (CR) = f(ketones) [grams per unit]. In some aspects, the adjustment setting 340 may be based on one or more conditional equations, such as if-then conditional equations. For example, as FIG. 11 As shown, the condition equations may include: If ketones are <1.0 mmol / L, Then IS = 4 mg / dL / unit (or expressed in ordinary symbol, 1:4). If the ketone concentration is ≥1.0 and <3.0 mmol / L, Then IS = 4 + {(ketone - l) / 2] 4 mmol / L Otherwise, ketone = 8 mmol / L.

[0175] In some respects, adjusting setting 340 may include insulin dose values ​​(e.g., via IDS 190). For example, as FIG. 12As shown, insulin dose values can be associated with different glucose and ketone ranges, as in the example dose guidance system 1100, and dose settings can be defined by insulin type, insulin units, total daily dose (TDD), and / or percent TDD. In some aspects, the adjustment settings 340 can define insulin dose parameters as equations of analyte measurements (e.g., ketone levels). For example, as shown in the example blood glucose response model 1200, insulin dose parameters, including but not limited to basal insulin (Basal), insulin sensitivity (IS), and carbohydrate ratio (CR), can each be defined as a function f(Ketone) of ketone values, such as ketone levels, ketone ROC, ketone-based indicators, etc. FIG. 11 As shown, insulin dose values can be associated with different glucose and ketone ranges, as in the example dose guidance system 1100, and dose settings can be defined by insulin type, insulin units, total daily dose (TDD), and / or percent TDD. In some aspects, the adjustment settings 340 can define insulin dose parameters as equations of analyte measurements (e.g., ketone levels). For example, as shown in the example blood glucose response model 1200, insulin dose parameters, including but not limited to basal insulin (Basal), insulin sensitivity (IS), and carbohydrate ratio (CR), can each be defined as a function f(Ketone) of ketone values, such as ketone levels, ketone ROC, ketone-based indicators, etc.

[0176] In some aspects, the adjustment settings 340 can define insulin dose parameters as equations that have ketone values (e.g., ketone levels, ketone ROC, ketone-based indicators, etc.) as inputs and have insulin doses or percent TDD as outputs, and the equation parameters can be part of the adjustment settings 340. For example, the percent TDD can be a function of ketone values, or the calculated insulin amount can be subtracted by a function of ketone values. In some aspects, instead of a function of ketone values, the adjustment settings 340 can use different functions based on ketone value ranges, e.g., using two or more ketone value ranges as inputs to a function that modifies the insulin dose. In some aspects, the adjustment settings 340 can include insulin sensitivity (IS) to determine a suggested insulin dose (e.g., dose recommendation 378 of the dose control system 370), e.g., to reduce high glucose levels.

[0177] In some aspects, the adjustment settings 340 can include a carbohydrate amount or carbohydrate ratio (CR) to define an insulin amount that covers a certain amount of carbohydrates, or conversely, to define a carbohydrate amount that covers a certain amount of insulin. For example, as shown in the example blood glucose response model 1200, the adjustment settings 340 can define a carbohydrate ratio (CR) as 1:5 (U:g) for a low glucose level and a high ketone level, as in the example dose guidance system 1100, and recommend taking 10 U of insulin and 5 / CR + 15 g (15 g to cover the low glucose) of carbohydrate intake. FIG. 3

[0178] ​In some aspects, the adjustment settings 340 can include insulin sensitivity (IS) and / or carbohydrate ratio (CR) defined as a range of parameters to determine a suggested insulin dose, e.g., where each element of the range of parameters is associated with a glucose range and / or a ketone range. For example, the software application 300 can accommodate a situation where the patient’s insulin sensitivity (IS) was low the previous day, or where the patient’s insulin sensitivity (IS) indirectly led to a high ketone reading. In some aspects, the adjustment settings 340 can include insulin sensitivity (IS) and / or carbohydrate ratio (CR) defined as a function, where glucose and / or ketones are inputs and insulin sensitivity (IS) and / or carbohydrate ratio (CR) are outputs. For example, the adjustment settings 340 can include adjustable equation parameters that define a function.

[0179] In some aspects, the software application 300 can include settings that indicate a patient’s disease state (e.g., known disease) and / or medication regimen. For example, if the patient is a type 1 diabetic using an SGLT-2 inhibitor, the adjustment settings 340 can enable features and thereby adjust the detection of conditional logic states based on that patient information. In some aspects, the software application 300 can include settings that indicate whether the patient is using an insulin pump, insulin pen, and / or AID system (e.g., IDS 190) or whether the patient is on a low-carb diet. For example, the adjustment settings 340 can enable features to change text (e.g., notifications 352) associated with conditional logic and / or change the conditional logic itself (e.g., condition detection 320) based on that patient information.

[0180] In some aspects, for patients on a low-carb diet, the adjustment settings 340 can automatically or manually (e.g., via a clinician) adjust one or more threshold settings (e.g., threshold 334) to accommodate the low-carb diet. For example, if a low-carb diet option is selected, the software application 300 can automatically increase the moderate ketones threshold (e.g., 1.0 mmol / L to 1.5 mmol / L), or a clinician can manually increase the moderate ketones threshold in the software application 300. In some aspects, the software application 300 can perform periodic data analysis to assess whether the patient can maintain moderate to high ketones (e.g., between 1.0 mmol / L and 3.0 mmol / L) without causing high ketones (e.g., above 3.0 mmol / L). For example, the adjustment settings 340 can periodically monitor the patient’s ketone levels and automatically set the moderate ketones threshold (e.g., about 1.0 mmol / L), or suggest to a clinician or HCP in a report (e.g., report 364) how to adjust the moderate ketones threshold.

[0181] In some respects, software application 300 can provide patients with instructions to discontinue a medication regimen (e.g., an SGLT-2 inhibitor) until otherwise instructed. For example, for patients using SGLT-2 inhibitors (e.g., patients with type 1 diabetes), notifications (e.g., recommendations) regarding moderate or high ketone levels can include instructions to discontinue the SGLT-2 inhibitor until the patient's HCP indicates that medication should be restarted.

[0182] I / O subsystem 342 can be configured to receive and transmit data between conditional logic system 310, setting system 330, notification system 350, and / or dose control system 370. For example... FIG. 3 As shown, the I / O subsystem 342 can be coupled to the predetermined settings 332, the adjustment settings 340, and the UI subsystem 344. In some aspects, the I / O subsystem 342 can send and receive data between the software application 300 and one or more devices of the analyte monitoring system 100 (e.g., analyte sensor 122, OBU 120, display device 130, remote server 180, etc.). In some aspects, the I / O subsystem 342 can send and receive data between all components and subcomponents of the software application 300 (e.g., conditional logic system 310, setting system 330, notification system 350, dose control system 370, etc.). In some aspects, the patient can transmit data or adjust settings in the software application 300 via the I / O subsystem 342, for example, using the UI subsystem 344.

[0183] In some aspects, the I / O subsystem 342 can send a predetermined setting 332 to the conditional logic system 310, which can compare sensor data 312 with the predetermined setting 332 to detect a conditional logic state (e.g., via condition detection 320). In some aspects, the I / O subsystem 342 can receive detected states (e.g., conditional logic state 10) from the conditional logic system 310, obtain one or more notifications 352 from the notification system 350 based on the detected states, and send corresponding notifications 352 to external devices (e.g., display device 130, remote server 180, etc.). In some aspects, the I / O subsystem 342 can receive additional information (e.g., contextual data 362) from the patient based on prompts (e.g., prompt 358) from the notification system 350, and send the additional information to the conditional logic system 310 to adjust the conditional logic and / or report to the HCP (e.g., report 364). In some respects, the I / O subsystem 342 may receive dose guidance (e.g., dose guidance 374) or IDS control instructions (e.g., IDS control 372) from the dose control system 370 and send the information to an external insulin delivery system (e.g., IDS 190, smart insulin pen, etc.).

[0184] The UI subsystem 344 can be configured to provide one or more user interfaces that allow a user to interact with the software application 300. The UI subsystem 344 can also be configured to send data to the conditional logic system 310, the settings system 330, the notification system 350, and / or the dose control system 370. As FIG. 3 shown, the UI subsystem 344 can be coupled to the predetermined settings 332 to confirm or modify the predetermined settings 332 (e.g., the threshold values 334). In some aspects, the UI subsystem 344 can provide a user interface for inputting, confirming, or modifying the predetermined settings 332 (e.g., the threshold values 334), for example, based on different treatment plans or medication regimens. In some aspects, the UI subsystem 344 can receive user input via one or more input devices, such as a keyboard, a mouse, a touchscreen, a gesture, or any other suitable input device (e.g., the display device 130). In some aspects, the UI subsystem 344 can display and manipulate a model of one or more analyte measurements (e.g., the display 140 with the menu input 148 and the graphical input 152). In some aspects, the UI subsystem 344 can input or modify notifications of the notification system 350 (e.g., the alerts 354, the suggestions 356, the prompts 358).

[0185] The notification system 350 can be configured to provide customized notifications (e.g., alerts, suggestions, guidance, prompts, etc.) based on detected conditions. The notification system 350 can also be configured to provide customized notifications to a patient at appropriate times. The notification system 350 can be operatively coupled to the conditional logic system 310, the settings system 330, and / or the dose control system 370. As FIG. 3 shown, the notification system 350 can include notifications 352, second notifications 360, context data 362, and / or reports 364.

[0186] The notifications 352 can be configured to provide one or more notifications (e.g., text, alerts, guidance, prompts, etc.) based on detected conditional logic states (e.g., conditional logic states 10, 20, 30, 40, 50, 60, 70). As FIG. 4B shown, the notifications 352 can include alerts 354, suggestions 356, prompts 358, or combinations thereof. In some aspects, the notifications 352 can include one or more notifications (e.g., alerts 354, suggestions 356, prompts 358) to one or more external devices (e.g., the display device 130, the remote server 180, etc.) based on detected conditions (e.g., condition detection 320). For example, as FIG. 5B , FIG. 6B , FIG. 7B , FIG. 8B , FIG. 9B , FIG. 10B and FIG. 4A to FIG. 10BAs shown, a corresponding display notification 400B-1000B can be provided for each of the different conditional logic states 10, 20, 30, 40, 50, 60, 70 of the software application 300, e.g., on the display 140.

[0187] In some aspects, the software application 300 can detect (e.g., in real-time) the occurrence of a conditional logic state (e.g., glucose-ketone condition) and the conditional logic state is determined by a predetermined threshold (e.g., predetermined settings 332). For example, as shown in FIG. 4A, the software application 300 can detect a low glucose condition 10 and a medium ketone condition 20. As shown, the software application 300 can provide a notification (e.g., notification 352) to the patient, e.g., to the display 140 of the display device 130, when the condition is first detected. FIG. 3 As shown, the software application 300 can utilize the state diagrams 400A-1000A and corresponding display notifications 400B-1000B for the different conditional logic states 10, 20, 30, 40, 50, 60, 70.

[0188] In some aspects, when a condition is first detected, the software application 300 will send a notification (e.g., notification 352) to the patient, e.g., to the display 140 of the display device 130. In some aspects, the software application 300 can include an alarm function. For example, the alarm function can include a button or other input element for the patient to acknowledge and silence the alarm, e.g., alarm display 156 of the display 140. In some aspects, if the notification 352 (e.g., alarm, alert) is ignored, the notification 352 will sound for a short period of time (e.g., about 15 seconds) and then reappear periodically (e.g., every 15 minutes) until acknowledged, e.g., via a second notification 360. In some aspects, the notification 352 (e.g., alarm, alert) can be provided to others (e.g., clinician, HCP, patient family member, etc.), e.g., using a caregiver software application. For example, the software application 300 can be downloaded and used by one or more caregivers, separate from the patient’s software application 300, and receive one or more notifications 352 (e.g., ketone alerts).

[0189] In some aspects, the software application 300 can be designed to modify the behavior of the presented alarms (e.g., notifications 352) as the analyte condition states (e.g., glucose and ketone condition states) can transition at different times. For example, the software application 300 can present a low glucose alarm at one point in time and a medium ketone and low glucose alarm at a later time (e.g., a few minutes), such that the low glucose alarm behavior after the initial notification can be changed or suppressed to support the post-notification behavior of the medium ketone and low glucose alarms.

[0190] The alerts 354 can be configured to provide customized alerts (e.g., text) based on the detected conditions. As shown in FIG. 4B, the software application 300 can provide a low glucose alert 354A and a medium ketone alert 354B for the low glucose and medium ketone conditions 10, 20, respectively. FIG. 4BAs shown, the alert 354 can be based on the detected condition (e.g., condition detection 320) and provided by the software application 300. In some aspects, the alert 354 can include one or more alerts to one or more external devices (e.g., display device 130, remote server 180, etc.) based on the detected condition (e.g., condition detection 320). For example, as shown in FIG. 3B, the alert 354 can include an alert to the display device 130 based on the detected condition (e.g., condition detection 320). In some aspects, the alert 354 can include an alert to the remote server 180 based on the detected condition (e.g., condition detection 320). In some aspects, the alert 354 can include an alert to the display device 130 and the remote server 180 based on the detected condition (e.g., condition detection 320). FIG. 5B , FIG. 6B , FIG. 7B , FIG. 8B , FIG. 9B , FIG. 10B and FIG. 3 As shown, the alert 354 can be based on the detected condition (e.g., condition detection 320) and provided by the software application 300. In some aspects, the alert 354 can include one or more alerts to one or more external devices (e.g., display device 130, remote server 180, etc.) based on the detected condition (e.g., condition detection 320). For example, as shown in FIG. 3B, the alert 354 can include an alert to the display device 130 based on the detected condition (e.g., condition detection 320). In some aspects, the alert 354 can include an alert to the remote server 180 based on the detected condition (e.g., condition detection 320). In some aspects, the alert 354 can include an alert to the display device 130 and the remote server 180 based on the detected condition (e.g., condition detection 320).

[0191] In some aspects, the alert 354 can continue to be displayed each time the patient accesses the current analyte reading and / or analyte display in the software application 300, such as the current ketones reading on the display 140. In some aspects, when the software application 300 detects a condition (e.g., high ketones level) and provides the alert 354 to the patient, the software application 300 can include a button or other input element to display a suggestion (e.g., suggestion 356) and / or other text associated with the detected condition (e.g., prompt 358).

[0192] In some aspects, when the software application 300 detects a condition (e.g., high ketones level) and provides the alert 354 to the patient, the software application 300 can automatically display a suggestion (e.g., suggestion 356) and / or other text associated with the detected condition (e.g., prompt 358) on the display 140 showing the current ketones reading (e.g., numerical display 146) and / or ketones display (e.g., graphical display 142). In some aspects, the alert 354 can provide an alert or text to the patient based on the detected condition and can include a suggestion (e.g., suggestion 356). For example, the alert 354 can include a suggested amount of insulin and / or carbohydrates (e.g., take in 15 grams of carbohydrates).

[0193] The suggestion 356 can be configured to provide a customized suggestion (e.g., text) to the patient to take action based on the detected condition. As shown, FIG. 4B The suggestion 356 can be based on the detected condition (e.g., condition detection 320) and provided by the software application 300. In some aspects, the suggestion 356 can include one or more suggestions to one or more external devices (e.g., display device 130, remote server 180, etc.) based on the detected condition (e.g., condition detection 320). For example, as shown in FIG. 3B, the suggestion 356 can include a suggestion to the display device 130 based on the detected condition (e.g., condition detection 320). In some aspects, the suggestion 356 can include a suggestion to the remote server 180 based on the detected condition (e.g., condition detection 320). In some aspects, the suggestion 356 can include a suggestion to the display device 130 and the remote server 180 based on the detected condition (e.g., condition detection 320). FIG. 8B ,FIG. 9B , FIG. 10B and FIG. 9B As shown, for example, on display 140, corresponding suggestions 408b, 810b, 910b, 1010b for different conditional logic states 10, 50, 60, 70, respectively, of software application 300 can be provided.

[0194] In some aspects, suggestions 356 can include suggested insulin and / or carbohydrate amounts. For example, as shown, for a detected moderate ketone level and moderate glucose level (e.g., conditional logic state 60), suggestion 910b can include a suggested insulin and carbohydrate amount (e.g., “if you haven’t injected insulin in the last 3 hours, consume 15 grams of carbohydrates and inject enough insulin to cover those carbohydrates”), for example, on display 140 following alert 908b. FIG. 3

[0195] Prompts 358 can be configured to provide customized prompts (e.g., text) based on detected conditions. Prompts 358 can also be configured to obtain additional information (e.g., context data 362) about detected conditions. As shown, for example, prompts 358 can be based on detected conditions (e.g., condition detection 320) and provided by software application 300. In some aspects, prompts 358 can include one or more prompts to one or more external devices (e.g., display device 130, remote server 180, etc.) based on detected conditions (e.g., condition detection 320). For example, as shown, for a detected moderate ketone level and moderate glucose level (e.g., conditional logic state 60), prompt 912b can include a prompt to display device 130 to display a message (e.g., “if you haven’t injected insulin in the last 3 hours, consume 15 grams of carbohydrates and inject enough insulin to cover those carbohydrates”) on display 140, for example, on display 140 following alert 908b. FIG. 5B FIG. 6B , FIG. 7B , FIG. 8B , FIG. 9B , FIG. 10B and FIG. 7B As shown, for example, on display 140, corresponding prompts 508b, 608b, 610b, 708b, 812b, 912b, 1012b for different conditional logic states 20, 30, 40, 50, 60, 70, respectively, of software application 300 can be provided.

[0196] ​​In some aspects, the software application 300 can be configured to provide a prompt 358 to the patient to obtain additional information about the condition. For example, the software application 300 can prompt the patient for additional information (e.g., contextual data 362) related to the detected condition at an appropriate time, e.g., immediately after the condition is detected. In some aspects, the software application 300 can capture additional information (e.g., contextual data 362) about the patient’s condition at a time that the patient will remember, e.g., to assist the clinician or HCP in determining a root cause of the condition. In some aspects, the additional information can include contextual data (e.g., contextual data 362) of the condition. In some aspects, the software application 300 can be configured to adjust the threshold (e.g., threshold 334) for the first analyte (e.g., glucose) and / or the second analyte (e.g., ketones, lactate, lactic acid, alcohol) based on the contextual data 362.

[0197] In some aspects, the contextual data 362 can include a frequency of the condition occurring, e.g., a number of times the condition occurred in an hour, six hours, twelve hours, a day, a week, a month, etc. In some aspects, the contextual data 362 can include a degree of discomfort of the patient, e.g., based on a numerical scale (e.g., increasing degree of discomfort represented by 0 to 3). For example, as shown in FIG. 7B, the prompt 708b can include a list of degrees of discomfort for the patient to select (e.g., “0 - no discomfort; 1 - mild discomfort; 2 - emergency medical services accepted, symptoms mild; 3 - emergency medical services accepted, symptoms severe”). FIG. 3

[0198] In some aspects, if the patient does not answer the prompt 358 (e.g., text, list), the prompt 358 will be vocalized for a short period of time (e.g., about 15 seconds) and then reappear periodically (e.g., every hour) until the prompt 358 is acknowledged and answered, e.g., via the second notification 360. In some aspects, the software application 300 can record or store the patient’s answer or selection to the prompt 358 (e.g., contextual data 362). For example, once the information is entered and recorded (e.g., contextual data 362), the prompt 358 can be discontinued until the next time a condition is detected.

[0199] In some aspects, the prompt 358 can be used to determine whether the patient is ill and details about the illness. For example, the prompt 358 can include a series of questions for the patient to answer (e.g., similar to a clinician or HCP) to isolate and determine the illness (e.g., hyperketoacidosis episode, DKA, euDKA, etc.). In some aspects, the software application 300 can record or store the patient’s answer or selection to one or more prompts 358. For example, once the information is entered and recorded, the information can be provided to a clinician or HCP (e.g., report 364) to assist in determining one or more root causes of the illness, e.g., hyperketoacidosis episode. ​

[0200] The second notification 360 can be configured to provide a follow-up notification (e.g., alert, alarm, warning, suggestion, prompt) if the notification 352 is ignored or not acknowledged by the patient within a specified time period. As shown, the second notification 360 can be based on the time period or the patient’s response to the notification 352 and provided by the software application 300. In some aspects, if the detected condition remains unchanged after a predetermined time period (e.g., about 15 minutes), the software application 300 can provide the second notification 360 to the patient (e.g., via the display 140). In some aspects, if the notification 352 (e.g., alert, alarm, warning, suggestion, prompt) is ignored, the notification 352 will sound for a short period of time (e.g., about 15 seconds) and then the second notification 360 will reappear periodically (e.g., every 15 minutes) until the notification 352 is acknowledged. FIG. 3

[0201] In some aspects, the second notification 360 can be utilized when the software application 300 is paired with an insulin delivery pump or insulin pen (e.g., IDS 190). For example, if the software application 300 provides a suggested dose (e.g., insulin dose) but after a specified time period (e.g., about 15 minutes) the suggested dose is not delivered within a specified delivery period (e.g., within the last 3 hours and 15 minutes) and the detected condition remains unchanged, the software application 300 can generate the second notification 360 to resend the notification 352 and indicate that this is a repeated notification and continue to send the second notification 360 periodically as long as the detected condition remains unchanged.

[0202] The contextual data 362 can be configured to provide contextual information (e.g., patient discomfort, causative factors, medications taken, etc.) related to the detected condition to assist the clinician or HCP in determining the root cause of the condition. As shown, the contextual data 362 can be received from the patient in response to one or more prompts 358. In some aspects, the software application 300 can prompt the patient for the contextual data 362 related to the detected condition at an appropriate time, for example, immediately after the detected condition when the patient will remember. FIG. 7B

[0203] In some aspects, the contextual data 362 can include the frequency of the condition, for example, the number of times the condition occurs in an hour, six hours, twelve hours, a day, a week, a month, etc. In some aspects, the contextual data 362 can include the degree of the patient’s discomfort, for example, based on a numerical scale (e.g., degree of discomfort represented by 0 to 3). For example, as shown, the contextual data 362 can include the degree of the patient’s discomfort, for example, based on a numerical scale (e.g., degree of discomfort represented by 0 to 3). FIG. 3 ​​As shown, the context data 362 can include a patient selection from a list of illness levels (e.g., "2 - seek emergency medical services, symptoms mild"). In some aspects, the software application 300 can record or store the context data 362. For example, once the context data 362 is entered and recorded, the prompt 358 can be discontinued until the next episode. In some aspects, the context data 362 can be reported to a clinician or HCP, e.g., via the report 364.

[0204] The report 364 can be configured to report information to one or more external devices (e.g., the display device 130, the remote server 180, etc.). The report 364 can also be configured to generate a report (e.g., a graph, a trend, a time trace, etc.) of patient information, including analyte measurements, detected episodes, and / or inputs from the patient (e.g., the context data 362). As FIG. 3 As shown, the report 364 can be sent to the patient (e.g., the display device 130) or to a clinician or HCP (e.g., the remote server 180) via the software application 300.

[0205] In some aspects, the report 364 can include a daily time trace of one or more analytes (e.g., glucose, ketones, lactate, lactate acid, alcohol), e.g., similar to a current glucose daily report. In some aspects, the report 364 can juxtapose two or more analytes (e.g., any combination of glucose, ketones, lactate, lactate acid, alcohol) on the same report. In some aspects, the report 364 can include a glucose time trace graph annotated with ketone-centric information. For example, color bands (e.g., green = low ketone range, yellow = moderate ketone range, red = high ketone range) or other indicia of ketone levels or ketone ranges can be annotated on the glucose time trace graph.

[0206] In some aspects, information collected from the prompt 358 of the software application 300 (e.g., the context data 362) can be used to annotate a daily time trace of one or more analytes (e.g., ketones, lactate, lactate acid, alcohol). For example, a symbol or indicia representative of a cause of a high ketone episode (or text of the cause itself) can be vertically coincident with the time the cause was recorded or the time the ketone levels began to increase. In some aspects, an icon or symbol associated with a cause of a detected episode (e.g., insulin pump malfunction) can be placed on the report at the beginning of the associated increase in ketone levels. In some aspects, information from the patient (e.g., the context data 362) can be annotated on the trace or report, e.g., illness level information from the patient can be included on the trace at the beginning, middle, and / or end of an associated event (e.g., a high ketone episode).

[0207] In some aspects, the report 364 can include other indicators configured to provide a clinician or HCP with a conclusion about the patient’s condition. For example, the report 364 can include: hyperketo events, medium keto events, frequency of hyperketo events occurring at normal target range glucose levels (e.g., number of occurrences per year); all possible glucose and keto ranges; hyperketo causes; keto baseline levels, or combinations thereof. In some aspects, the report 364 can include a frequency of analyte conditions (e.g., hyperketo episodes) and information from the patient (e.g., contextual data 362). For example, a report can be made of the cause frequency and discomfort distribution of hyperketo events over the past year. In some aspects, the report 364 can identify and display an indication that a patient’s medication regimen (e.g., use of SGLT-2 inhibitors) or a carbohydrate-restricted diet is not recommended based on the reported indicators, e.g., if the patient has had two or more hyperketo events over the past year.

[0208] In some aspects, the report 364 can include a relationship (e.g., correlation) between medium keto levels and hyperketo levels. For example, a report can be made of the frequency of hyperketo events occurring after medium keto events. In some aspects, the report 364 can include a day pattern plot (e.g., 24 hr plot) of one or more analytes (e.g., keto, lactate, lactate, alcohol), e.g., to identify a prevalent time of day for hyperketo. In some aspects, the report 364 can include a week pattern plot (e.g., 7 day plot) of one or more analytes (e.g., keto, lactate, lactate, alcohol), e.g., to identify days of the week that the patient can be at a higher risk (e.g., hyperketo events) and help determine one or more factors that lead to hyperketo events.

[0209] In some aspects, the report 364 can include a day pattern plot (e.g., 24 hr plot) of continuous keto levels. For example, the day pattern plot can include individual keto tracks or keto percentile tracks (e.g., 5th percentile, 25th percentile, 50th percentile, 75th percentile, 95th percentile, etc.) computed for each hour of the day. In some aspects, the report 364 can include overlapping plots (e.g., different plots superimposed on each other) that temporally align with key events of an analyte track (e.g., keto track). For example, the overlapping plots can temporally align with the onset of increasing keto levels or keto levels exceeding a particular threshold (e.g., hyperketo threshold).

[0210] In some aspects, the software application 300 can associate the degree of discomfort (e.g., contextual data 362) of the patient to determine the effectiveness of the treatment plan. For example, metrics (e.g., dosage of SGLT-2 inhibitor, daily exercise, weight loss, etc.) can be calculated and presented via the report 364 to indicate that the degree of discomfort is reduced or that keto episodes are mitigated if the recommended treatment plan is followed. In some aspects, the report 364 can present a corresponding distribution of the frequency of keto episodes and the degree of discomfort. For example, the report 364 can present a report showing the frequency of keto episodes when the treatment is followed versus when the treatment is not followed or delayed and the corresponding distribution of the degree of discomfort when a keto episode occurs when the treatment is followed versus when the treatment is not followed or delayed.

[0211] In some aspects, the software application 300 can utilize information from the report 364 (e.g., effectiveness of the treatment plan) to determine one or more thresholds 334 (e.g., low-keto threshold). For example, analysis from the report 364 can determine that, for a particular patient, keto levels up to 1.4 mmol / L do not transition to above a hyperketo threshold (e.g., 3.0 mmol / L), while keto levels above 1.4 mmol / L do transition to hyperketo levels (e.g., above 3.0 mmol / L), indicating that the low-keto threshold can be set to about 1.4 mmol / L. In some aspects, the software application 300 can periodically perform analysis from the report 364 and automatically adjust one or more thresholds (e.g., low-keto threshold) based on data from the report 364. In some aspects, the software application 300 can periodically perform analysis from the report 364 and report the data to a clinician or HCP for manual adjustment of one or more thresholds 334 (e.g., low-keto threshold).

[0212] In some aspects, the report 364 can include a time trajectory of individual analyte events (e.g., moderate and hyperketo events) annotated or aligned with other information to account for the effectiveness of the treatment plan. For example, the time trajectory of individual moderate and hyperketo events can be annotated with a meal log, insulin delivery log or records, glucose trajectory, and / or other possible data to show the effectiveness of the treatment plan to mitigate hyperketo events.

[0213] The dosage control system 370 can be configured to control and / or operate an insulin delivery system (e.g., an insulin pump, an insulin pen, an AID system, the IDS 190, etc.). The dosage control system 370 can also be configured to provide dosage guidance to the patient. The dosage control system 370 can also be configured to utilize a blood glucose model (e.g., the blood glucose response model 376) to provide a suggested dosage based on one or more blood glucose parameters. The dosage control system 370 can be operatively coupled to the conditional logic system 310, the setting system 330, and / or the notification system 350. AsFIG. 3 As shown, the dose control system 370 can include an IDS control 372, a dose guidance 374, a glycemic response model 376, additional data 382 (e.g., additional sensors), and / or a dose titration 384.

[0214] In some aspects, the dose control system 370 can control insulin delivery based on the first analyte level 314a (e.g., glucose) and the second analyte level 314b (e.g., ketones, lactate, lactic acid, alcohol). For example, the dose control system 370 can control insulin delivery based on glucose levels and ketone levels. In some aspects, the dose control system 370 can calculate an insulin dose based on glucose levels and modify the insulin dose based on ketone levels. In some aspects, the dose control system 370 can adjust insulin delivery parameters, e.g., insulin sensitivity factor (ISF), carbohydrate ratio (CR), etc., based on glucose levels and ketone levels.

[0215] The IDS control 372 can be configured to control and / or operate an insulin delivery system (e.g., an insulin pump, an insulin pen, an AID system, the IDS 190, etc.). The IDS control 372 can also be configured to send data to and receive data from an insulin delivery system (e.g., the IDS 190). As FIG. 3 As shown, the IDS control 372 can be coupled to the glycemic response model 376 and provide control instructions to an insulin delivery system (e.g., the IDS 190). In some aspects, if the software application 300 has been configured for an insulin pump patient (e.g., paired with the IDS 190), the recommendations 356 can include instructions related to insulin delivery, e.g., check the pump for proper operation (e.g., tubing pressure) to ensure that insulin is being delivered and the correct amount of insulin.

[0216] In some aspects, for an AID system application (e.g., the IDS 190), the AID system (e.g., the IDS 190) can suspend insulin delivery, but the recommendations 356 can include instructions to manually override the AID system to resume insulin delivery (e.g., to mitigate the risk of euDKA), e.g., if ketone levels are high while glucose levels are low or moderate. In some aspects, for an AID system application (e.g., the IDS 190), the software application 300 can be connected (e.g., wirelessly or electronically) to the AID system (e.g., the IDS 190) and automatically override the AID system to resume insulin delivery if the AID system suspends insulin delivery, e.g., if ketone levels are high while glucose levels are low or moderate (e.g., to mitigate the risk of euDKA). For example, the IDS control 372 can send control instructions to resume insulin delivery and the recommendations 356 can include instructions for the patient to ingest carbohydrates as part of the treatment.

[0217] Dosage guidance 374 can be configured to provide patients with dosage guidance (e.g., insulin dose, carbohydrate dose, etc.). FIG. 11 As shown, dose guidance 374 may be coupled to notification system 350 and provide dose guidance to the patient (e.g., suggestion 356), and / or coupled to IDS control 372 and provide dose guidance to insulin delivery system (e.g., IDS 190). In some aspects, dose guidance 374 may be operatively coupled to software application 300, such as prediction model 324. In some aspects, dose guidance 374 may include a large dose calculator.

[0218] In some respects, for patients using large-dose calculators or dose guidance systems, dose guidance 374 can provide specific recommendations on dosage and carbohydrate intake. For example, dose guidance 374 may instruct software application 300 to provide one or more recommendations 356 that guide the patient to inject a certain amount of insulin (e.g., inject 5U of insulin, 1U = 34.7 μg of insulin) and ingest a certain amount of carbohydrates (e.g., ingest 15 grams of carbohydrates) as part of treatment.

[0219] In some aspects, dose guidance 374 may include one or more lists of analytes (e.g., arrays, matrices, etc.) for use in dose-guided systems. For example, as FIG. 11 As shown, the exemplary dose guidance system 1100 may include a glucose and ketone table (e.g., low glucose, normal glucose, high glucose, moderate ketone, high ketone) to provide insulin dose values ​​and carbohydrate amount recommendations for six different conditions (e.g., low glucose-moderate ketone, normal glucose-moderate ketone, high glucose-moderate ketone, low glucose-high ketone, normal glucose-high ketone, high glucose-high ketone). For example, as FIG. 3 As shown, in the case of low glucose and high ketone levels in the exemplary dose guidance system 1100, dose guidance 374 can provide the patient with advice 356 to take 10U of insulin and ingest 5 / CR+15g of carbohydrates.

[0220] In some aspects, the software application 300 (e.g., mobile application) can include and collocate a bolus calculator or dose guidance system (referred to herein as a “dose calculator”) on the mobile application. For example, the dose guidance 374 can include a dose calculator. In some aspects, a web server or cloud server (e.g., remote server 180) supporting the software application 300 (e.g., mobile application) remote from the mobile application can include a dose calculator. For example, a remote dose calculator (e.g., on the remote server 180) can include an application program interface (API) that allows the software application 300 to fetch key parameters (e.g., insulin sensitivity, carbohydrate ratio, insulin on board, etc.) from the remote dose calculator. In some aspects, the software application 300 can fetch key parameters (e.g., insulin sensitivity, carbohydrate ratio, etc.) from the remote dose calculator and, for example, via the dose guidance 374, calculate a recommended insulin dose value and carbohydrate amount for the patient to take. For example, for a low glucose level (e.g., a low glucose alert), the software application 300 can recommend ingesting carbohydrates to increase the glucose level (e.g., ingest 15 grams to cover the low glucose). In some aspects, the remote dose calculator can receive data from the software application 300 and calculate a recommended insulin dose value and carbohydrate amount for the patient to take and send that data (e.g., recommendations) to the software application 300. For example, the remote dose calculator (e.g., on the remote server 180) can include an API that allows the software application 300 to request these outputs and send analyte data (e.g., glucose levels, ketone levels, glucose ROC, ketone ROC, time series data, other derived metrics, etc.) and / or predetermined settings (e.g., glucose threshold and ketone threshold) as inputs to the remote dose calculator.

[0221] In some aspects, the dose guidance 374 can be transmitted to an insulin delivery system (e.g., insulin pump, insulin pen, AID system, IDS 190, etc.) via an API. For example, the UI subsystem 344 of the software application 300 can provide a confirmation (e.g., a response to a prompt 358) that the patient intends to activate the recommended insulin dose and / or carbohydrate amount from the dose guidance 374.

[0222] In some aspects, the software application 300 with conditional logic (e.g., conditional logic system 310) and corresponding user interface (e.g., UI subsystem 344) can be integrated into the logic and user interface of an IDS (e.g., insulin pump, insulin pen, AID system, IDS 190, etc.), such as an insulin pump-based AID system or an insulin decision support system for multiple daily injections (MDI) or a pump. For example, the conditional logic of the software application 300 can run concurrently with the IDS or decision support functionality on the software application 300, and notifications 352 can be issued asynchronously from the IDS or decision support functionality or buffered and synchronized for display based on priority rules.

[0223] In some aspects, the dose guidance 374 can provide a button or an identification that displays appropriate mealtime dose guidance and other recommendations based on detected conditions (e.g., glucose-ketone conditional logic). For example, if the patient is advised to take in carbohydrates and cover with insulin, and the current time is approximately lunchtime, the software application 300 can display a button labeled "Lunch" and, when selected, display dose guidance for lunch.

[0224] In some aspects, the conditional logic (e.g., glucose-ketone conditional logic) of the software application 300 can be integrated with an AID system (e.g., IDS 190), and the conditional logic (e.g., conditional logic system 310) can prevent the AID system from fully suspending insulin delivery, for example, when the patient's glucose level is or is predicted to be low.

[0225] In some aspects, the dose guidance 374 can estimate the amount of insulin needed to reduce the patient's glucose to a desired level and / or to compensate for an anticipated meal. For example, the dose guidance 374 can take into account the current glucose level, the desired glucose level, the estimated active insulin in the body, and / or the estimated anticipated carbohydrate intake. In some aspects, the dose guidance 374 can assume that the patient's glycemic response is constant or has different values throughout the day. For example, the patient's glycemic response can be characterized by the patient's individual "basal insulin," "insulin sensitivity," and "carbohydrate ratio" factors. Basal insulin ("basal") is the amount of long-acting insulin taken throughout the day (or, for insulin pump users, the amount of rapid-acting insulin continuously provided), basal = constant (units per day). Insulin sensitivity ("IS") is the estimated amount of insulin needed to lower 1 unit of glucose, IS = constant (mg / dL per unit). Carbohydrate ratio ("CR") is the amount of insulin needed to compensate for 1 unit of carbohydrate intake, CR = constant (grams per unit). In some aspects, the software application 300 can include a glycemic response model (e.g., glycemic response model 376) that is an improvement over traditional insulin bolus calculators that are limited to constant factors (e.g., constant functions).

[0226] The glycemic response model 376 can be configured to provide a suggested dose based on one or more parameters. The glycemic response model 376 can also be configured to enhance traditional dose calculators by utilizing non-constant factors (e.g., non-constant functions). As shown, the glycemic response model 376 can include a dose suggestion 378 and / or parameters 380 (e.g., basal insulin, insulin sensitivity, carbohydrate ratio, second analyte). In some aspects, the software application 300 can be configured to provide the dose suggestion 378 to the patient and / or an IDS (e.g., glucose IDS 190) based on the glycemic response model 376. FIG. 12

[0227] In some aspects, the glycemic response model 376 can be based on basal insulin (basal), insulin sensitivity (IS), carbohydrate ratio (CR), and a second analyte. For example, the second analyte can include ketones or lactate. In some aspects, the basal insulin, insulin sensitivity, and / or carbohydrate ratio can be a function of the second analyte. For example, as shown, the parameters 380, including but not limited to basal insulin (basal), insulin sensitivity (IS), and carbohydrate ratio (CR), can each be defined as a function f(ketones) of the ketone value, such as a ketone level, a ketone ROC, a ketone-based index, etc. FIG. 12

[0228] ​​In some aspects, the blood glucose response model 376 can improve a traditional bolus calculator (e.g., an insulin bolus calculator) by utilizing one or more additional analyte measurements (e.g., ketones, lactate, lactate, alcohol) and / or additional information (e.g., basal insulin, insulin sensitivity, carbohydrate ratio). For example, the parameters 380 of the blood glucose response model 376 can include basal insulin (basal), insulin sensitivity (IS), carbohydrate ratio (CR), and a ketone level measurement. In some aspects, the blood glucose response model 376 can be modified to replace constant factors (e.g., basal insulin, insulin sensitivity, carbohydrate ratio) of a dose calculation with one or more functions of a second analyte level (e.g., ketone level, time series of ketone level, rate of change of ketones, etc.). For example, as shown in FIG. 3 the constant values of basal insulin (basal), insulin sensitivity (IS), and carbohydrate ratio (CR) can be replaced with a function of ketone level, e.g., IS = 4 mg / dL / U if ketone level is less than 1.0 mmol / L; IS = 4 mg / dL / U + {[ (ketone level - 1) / 2] x 4.0 mmol / L} mg / dL / U if ketone level is greater than or equal to 1.0 mmol / L but less than 3.0 mmol / L [note: 1.0 mmol / L of ketones is approximately 5.81 mg / dL]; otherwise ketone level = 8 mmol / L.

[0229] In some aspects, the blood glucose response model 376 can increase the accuracy of a traditional bolus calculator (e.g., an insulin bolus calculator) by continuously monitoring one or more analytes (e.g., glucose and ketones, lactate, lactate, and / or alcohol). For example, the software application 300 can perform continuous ketone monitoring to account for the dynamic effects of ketones on blood glucose response, thereby increasing the accuracy of the blood glucose response model 376.

[0230] The dose recommendation 378 can be configured to recommend a dose to a patient based on the blood glucose response model 376. As shown in FIG. 3 the dose recommendation 378 can be coupled to the notification system 350 and provide a dose recommendation (e.g., the recommendation 356) to a patient, and / or coupled to the IDS control 372 and provide a dose recommendation to an insulin delivery system (e.g., the IDS 190). In some aspects, the software application 300 can provide (e.g., display) the dose recommendation 378 to a patient and / or a clinician or HCP based on the blood glucose response model 376.

[0231] The parameters 380 can be configured to be used in the blood glucose response model 376 to calculate a recommended dose. The parameters 380 can be configured to be based on non-constant factors and enhance a traditional bolus calculator. As shown in FIG. 12As shown, parameters 380 can be coupled to dose suggestion 378 and form key parameters of blood glucose response model 376. In some aspects, parameters 380 can include basal insulin (basal), insulin sensitivity (IS), carbohydrate ratio (CR), and / or a second analyte level (e.g., a ketone level, a lactate level, etc.). In some aspects, parameters 380 can be defined as a function of a second analyte (e.g., ketones, lactate, lactic acid, alcohol). For example, parameters 380, including but not limited to basal insulin (basal), insulin sensitivity (IS), and / or carbohydrate ratio (CR), can be defined as a function of ketones, f(ketones), such as a ketone level, a ketone ROC, a ketone-based indicator, etc.

[0232] In some aspects, blood glucose response model 376 can utilize one or more additional analyte measurements or additional information. For example, parameters 380 can include ketones, lactate, lactic acid, and / or alcohol to enhance the dose calculator of blood glucose response model 376 and increase the accuracy of blood glucose response model 376. In some aspects, blood glucose response model 376 can be based on multiple dose calculators corresponding to one or more additional analyte measurements. For example, blood glucose response model 376 can utilize a ketone measurement for a first dose calculator and a lactate measurement for a second dose calculator.

[0233] In some aspects, blood glucose response model 376 can dynamically change parameters 380 to account for dynamic changes in one or more analytes. For example, a sustained rise in ketones can be an indication of decreased insulin sensitivity (IS) and / or increased carbohydrate ratio (CR), requiring more basal insulin (basal) because more insulin is needed to achieve the same effect on glucose when ketones are higher than normal, and blood glucose response model 376 can dynamically modify parameters 380 to account for this effect by replacing the constant factors with functions based on the ketone level. For example, as shown in FIG. 12 As shown, basal insulin (basal) can be defined as a function of ketone value, basal = f(ketones) [units per day], insulin sensitivity (IS) can be defined as a function of ketone value, IS = f(ketones) [mg / dL per unit], and carbohydrate ratio (CR) can be defined as a function of ketone value, CR = f(ketones) [grams per unit].

[0234] In some aspects, parameters 380 can be defined as a function of ketone value, f(ketones), with conditional logic. For example, as shown in FIG. 3 As shown, parameters 380 can be based on a piecewise linear model as follows: if ketones < 1.0 mmol / L, then IS = 4 mg / dL / unit (or in common notation, 1:4), if ketones > 1.0 and < 3.0 mmol / L, IS = 4 + { (Keto - 1) / 2} 4 mmol / L Otherwise, Keto = 8 mmol / L.

[0235] In some aspects, the parameter 380 can be defined as a function of more than one ketone measurement or calculation. For example, the parameter 380 can be based on a time series of ketone measurements (e.g., a ketone ROC).

[0236] In some aspects, the blood glucose response model 376 can be based on the fitted parameters (e.g., the parameter 380) and trained with data from a similar population of patients. For example, the blood glucose response model 376 can utilize the population model 326 and / or the prediction algorithm 328 of the prediction model 324.

[0237] In some aspects, the blood glucose response model 376 can be based on an adaptive model (e.g., the prediction model 324) that initially starts from a population trained model and adjusts the model over time with the patient’s data to further train the model. For example, the blood glucose response model 376 can track previous insulin doses paired with data derived from glucose measurements (e.g., glucose values at a particular time point relative to insulin dose, glucose ROC at a particular time point relative to insulin dose, glucose area under the curve above a predetermined threshold within a predetermined time relative to insulin dose, etc.) and / or data derived from ketone measurements (e.g., ketone values at a particular time point relative to insulin dose, ketone ROC at a particular time point relative to insulin dose, ketone area under the curve above a predetermined threshold within a predetermined time relative to insulin dose, etc.).

[0238] In some aspects, the blood glucose response model 376 can utilize a predetermined model to account for the effect of data before an insulin dose on data after an insulin dose. For example, the parameters 380 can be periodically or recursively updated to account for such effects (e.g., recursive estimation methods, parametric regression methods, etc.). In some aspects, the blood glucose response model 376 can utilize data before an insulin dose to construct features (e.g., subsystems, models, etc.), for example, for each insulin administration event in the past, which can predict a particular attribute associated with a ground truth (e.g., conditional logic, truth table, etc.) based on data after the insulin administration. For example, the constructed features can be used in various prediction algorithms (e.g., prediction algorithms 328) or machine learning frameworks to develop an estimator that can update administration parameters by accounting for glucose and ketone-derived data, for example, for each insulin administration decision in progress. In some aspects, the blood glucose response model 376 can utilize different sets of equations under different conditions, for example, as determined by glucose and / or ketone-derived data. For example, the blood glucose response model 376 can utilize a machine learning framework (e.g., random forest, prediction algorithms 328, etc.) to provide condition thresholds and equations to determine an insulin dose.

[0239] In some aspects, the blood glucose response model 376 can extend to an IDS (e.g., an AID system, IDS 190) in which insulin is automatically delivered to a patient. For example, the blood glucose response model 376 can utilize ketone and lactate measurements as inputs to an automatic dose calculator. In some aspects, the blood glucose response model 376 can utilize multiple models (e.g., for an AID system), in which ketone and lactate measurements can define which model to use. For example, a first model can utilize a lower IS than a second model, and the blood glucose response model 376 can switch to using the first model that uses a lower IS if ketone levels are above a particular threshold. In some aspects, the blood glucose response model 376 can utilize a first analyte level 314a (e.g., glucose) and a second analyte level 314b (e.g., ketones, lactate, lactate, alcohol) to adjust basal insulin delivery, for example, via the IDS 190. In some aspects, the blood glucose response model 376 can utilize a first analyte level 314a (e.g., glucose) and a second analyte level 314b (e.g., ketones, lactate, lactate, alcohol) to adjust bolus administration, for example, via the IDS 190.

[0240] In some aspects, the blood glucose response model 376 can change parameters (e.g., parameters 380) and / or switch to a different model based on the patient’s response to a prompt (e.g., prompt 358). For example, when the software application 300 detects elevated ketones, the software application 300 can prompt (e.g., prompt 358) the patient to indicate whether the patient is sick or ill (e.g., request context data 362), and based on the patient’s response (e.g., ill, sick, ill, nauseous, etc.), the blood glucose response model 376 can change parameters 380 and / or switch to a different model more appropriate for when the patient is sick.

[0241] In some aspects, the blood glucose response model 376 can treat lactate or lactate salt as an indicator of patient activity, as high activity tends to make the reduction of glucose more sensitive to insulin than normal. For example, lactate or lactate salt can be measured in frequent cycles and / or continuous sensors (e.g., analyte sensors 122). In some aspects, the blood glucose response model 376 can change parameters (e.g., parameters 380) and / or switch to a different model based on the patient’s activity. For example, when the software application 300 detects high lactate (e.g., indicating high patient activity), the blood glucose response model 376 can change parameters 380 and / or switch to a different model more appropriate for when the patient is performing high activity to account for increased insulin sensitivity (IS).

[0242] In some aspects, the blood glucose response model 376 can be designed based on a model that takes into account any number of additional analyte measurements that can better describe a patient's blood glucose response. For example, the blood glucose response model 376 can include a dose calculator based on one or more analytes including, but not limited to, glucose, ketones, lactate, lactic acid, oxygen, hemoglobin AlC, lactones, lactose, galactose, vitamin C, glucuronate, glycogen, mannose, phosphate, diphosphate, fructose, glyceraldehyde, glycerol, triglycerides, sorbitol, phosphogluconate, phosphogluconate, xylulose, ribose, bile, cysteine, serine, homoserine, pyruvate, phenylpyruvate, glutamate, glycine, taurine, threonine, methionine, ethanol, acetone, acetate, oxaloacetate, alanine, phenylalanine, aspartate, asparagine, alcohols, cholesterol, vitamin D, progesterone, testosterone, estrogen, squalene, insulin, oxybutyrate, leucine, isoleucine, malonyl, malonate, glucagon, epinephrine, norepinephrine, palmitate, lysine, eicosanoids, melanin, dopamine, tyrosine, tryptophan, niacin, melatonin, serotonin, citrate, isocitrate, valine, porphyrin, histidine, urocanate, histamine, glutamine, proline, creatine, putrescine, spermidine, spermine, arginine, ornithine, citrulline, fumarate, succinate, arginyl succinate, succinyl, ketoglutarate, aconitate, glyoxylic acid, caffeine, sugars, carbohydrates, or combinations thereof.

[0243] The additional data 382 can be configured to supplement the software application 300 and increase the accuracy of the estimation and / or modeling (e.g., blood glucose response model 376, prediction model 324, condition detection 320, etc.) of the software application 300. In addition to the analyte sensor 122, the additional data 382 can be configured to supplement the data with additional sensor measurements. As shown, the additional data 382 can be coupled to the blood glucose response model 376 and / or the IDS control 372 to supplement the modeling and / or control instructions. FIG. 3

[0244] ​In some aspects, software application 300 can be configured to receive data from a second sensor. For example, software application 300 can receive additional data 382 from a second sensor separate from analyte sensor 122. In some aspects, the second sensor can be similar to analyte sensor 122. For example, the second sensor can measure one or more analytes of the patient. In some aspects, the second sensor can measure additional information about the patient. For example, additional data 382 can include, but is not limited to, activity data, heart rate, respiratory rate, body temperature, perspiration data, location data, and / or lactate levels. In some aspects, additional data 382 from the second sensor can increase the accuracy and precision of estimates made by software application 300, such as detected conditions (e.g., condition detection 320, hyperketosis), predictive models (e.g., predictive model 324, future analyte levels), and blood glucose models (e.g., blood glucose response model 376, dose calculator). For example, a predictive model (e.g., predictive model 324, probabilistic model) can be built between additional data 382 (e.g., activity data, heart rate, lactate, etc.) and the occurrence of high ketone levels to estimate the likelihood of a high ketone condition and present that likelihood to the patient (e.g., notification 352).

[0245] The Dosage Titration 384 can be configured to titrate doses (e.g., determine the amount of a component in a solution, such as insulin). The Dosage Titration 384 can also be configured to automatically titrate a patient's medication dose. Exemplary state diagrams and display notifications for software applications As shown, dose titration 384 can be coupled to notification system 350 and provide dose titration guidance to the patient (e.g., recommendation 356), and / or coupled to IDS control 372 and provide dose titration control instructions to insulin delivery system (e.g., IDS 190). In some aspects, software application 300 can be configured to titrate the dose based on a first analyte level 314a (e.g., glucose) and / or a second analyte level 314b (e.g., ketones, lactates, lactate, alcohols).

[0246] In some aspects, the patient can be automatically titrated for a drug dosage value (e.g., SGLT-2 inhibitor) (e.g., determining the amount of an ingredient in a solution) by dose titration 384. In some aspects, dose titration 384 can determine whether a drug (e.g., SGLT-2 inhibitor) dosage value should be increased, decreased, or maintained based on ketone levels, other analyte levels (e.g., glucose), and / or other measurements (e.g., insulin delivered, carbohydrate intake, etc.). For example, glucose levels and ketone levels can be periodically (e.g., once a month) processed to determine whether a dosage value of SGLT-2 inhibitor should be increased, decreased, or maintained, and any changes (e.g., recommendations 356) can be displayed to the patient by software application 300, or sent to a clinician or HCP for approval before being sent to the patient. In some aspects, software application 300 can provide a user interface (e.g., UI subsystem 344) to enable reporting that enables periodic processing of one or more analyte levels (e.g., glucose levels and ketone levels) and / or other measurements (e.g., insulin delivered, carbohydrate intake, etc.) and display the results in the report (e.g., report 364).

[0247] In some aspects, software application 300 can calculate an analyte variability metric (e.g., glucose standard deviation) and / or a baseline analyte level (e.g., baseline ketone level). For example, software application 300 can take recent glucose and ketone measurement data (e.g., the last two weeks) and calculate a standard deviation of the glucose data based on a time period, e.g., all glucose data, a one-day time period, glucose-time area metrics associated with a period of 5 hours after each meal, etc., and calculate a moderate ketone level, filtering out any moderate or high ketone data to establish a baseline ketone level.

[0248] In some aspects, dose titration 384 can consider glucose variability and baseline ketone level and provide a recommendation for a drug dosage value (e.g., SGLT-2 inhibitor). For example, if (a) glucose variability is greater than a predetermined threshold, (b) baseline ketone level is less than a predetermined threshold, (c) no high ketone levels have been recorded in the past year, and (d) no moderate ketone levels associated with a contemporaneous glucose level below a predetermined threshold have been recorded in the past year, then dose titration 384 can recommend an increase in the dosage value of SGLT-2 inhibitor. For example, if (a) baseline ketone level exceeds a predetermined threshold, or (b) any high ketone levels have been recorded in the past year, then dose titration 384 can recommend a decrease in the dosage value of SGLT-2 inhibitor. For example, if (a) baseline ketone level is below a predetermined threshold, and (b) no high ketone levels have been recorded in the past year, then dose titration 384 can recommend no change in the dosage value of SGLT-2 inhibitor.

[0249] In some aspects, the dose titration 384 can include configurable dose parameters and / or titration logic criteria. For example, the dose titration 384 can include a default dose parameter amount determined or preset by a clinician or HCP, or the software application 300 can provide a user interface (e.g., UI subsystem 344) to configure or change the dose parameter amount.

[0250] In some aspects, the software application 300 can output or otherwise output the measured ketone data and data related to the measured ketone data. Thus, a user can download the ketone data and send the downloaded data to a care provider of interest. The data can be exported, printed, or downloaded. For example, the data can be exported into a spreadsheet that can be attached to an email. As shown in Table 1 below, the exported data can be in the form of a spreadsheet that includes how the sensor control device outputs each ketone level, how each ketone level is displayed on the home screen as a current ketone level (e.g., in a banner), how each ketone level is displayed in a graph, the value of each ketone level saved into a data document, and notifications associated with each ketone level, if any.

[0251] Table 1: Ketone Sensor Data

[0252] FIG. 4A to FIG. 10B

[0253] FIG. 4AState diagrams 400A-1000A and corresponding display notifications 400B-1000B of different conditional logic states 10, 20, 30, 40, 50, 60, 70 of the software application 300 according to example aspects are shown. The state diagrams 400A-1000A can be configured to detect the conditional logic states 10, 20, 30, 40, 50, 60, 70 based on predetermined settings (e.g., predetermined settings 332, threshold values 334) and subsequently provide the display notifications 400B-1000B based on the detected conditional logic states. The state diagrams 400A-1000A can also be configured to provide alerts and / or notifications based on one or two analyte levels reaching a particular predetermined threshold value and provide recommendations based on the analyte levels being within a particular range. The state diagrams 400A-1000A can also be configured to request contextual data (e.g., contextual data 362) based on the analyte levels being within a particular range. The display notifications 400B-1000B can be configured to provide customized notifications (e.g., alerts, recommendations, guidance, etc.) to the patient based on the different conditional logic states 10, 20, 30, 40, 50, 60, 70 to take action. The display notifications 400B-1000B can also be configured to prompt the patient for additional information (e.g., contextual data 362) related to the detected conditional logic states 10, 20, 30, 40, 50, 60, 70.

[0254] FIG. 4B and FIG. 4A A state diagram 400A and corresponding display notification 400B of the first conditional logic state 10 of the software application 300 according to example aspects are shown. As shown, the state diagram 400A can include a step 402, a step 404, a step 406, and a step 408. In the step 402, as shown, sensor data 312 (e.g., second analyte level 314b) can be acquired from the analyte measurement system 110 (e.g., via the software application 300 (e.g., conditional logic system 310)). In the step 404, as shown, the second analyte level 314b (e.g., ketone level) can be compared to the second analyte threshold 338 (e.g., high ketone threshold of about 3.0 mmol / L) to determine whether the second analyte level 314b (e.g., ketone level) transitions from being below the high second analyte threshold 338 (e.g., high ketone threshold of about 3.0 mmol / L) to being above the high second analyte threshold, thereby detecting the first conditional logic state 10 (e.g., condition detection 320). In the step 406, as shown, an alert (e.g., alert 354) can be provided to the patient (e.g., via the display 140) based on the detected first conditional logic state 10. For example, as shown, the display notification 400B can include a message 402 (e.g., “High ketones detected”) and a recommendation 404 (e.g., “Please contact your healthcare provider”). FIG. 4A FIG. 4A FIG. 4A FIG. 4B FIG. 4A ​​​​As shown, the alert 406b can be displayed on the display device 130. In step 408, as shown, a recommendation (e.g., recommendation 356) can be provided to the patient (e.g., via the display 140) based on the detected first conditional logic state 10. For example, as shown, the recommendation 408b can be displayed on the display device 130. FIG. 4B As shown, the alert 406b can be displayed on the display device 130. In step 408, as shown, a recommendation (e.g., recommendation 356) can be provided to the patient (e.g., via the display 140) based on the detected first conditional logic state 10. For example, as shown, the recommendation 408b can be displayed on the display device 130. FIG. 5A As shown, the alert 406b can be displayed on the display device 130. In step 408, as shown, a recommendation (e.g., recommendation 356) can be provided to the patient (e.g., via the display 140) based on the detected first conditional logic state 10. For example, as shown, the recommendation 408b can be displayed on the display device 130.

[0255] In some aspects, the display notification 400B can include a button or other input element for the patient to acknowledge and silence the display notification 400B, e.g., the alarm display 156 of the display 140. In some aspects, if the display notification 400B (e.g., alarm, alert) is ignored, the display notification 400B will sound for a short period of time (e.g., about 15 seconds) and then reappear periodically (e.g., every 15 minutes) until acknowledged. In some aspects, the above alarm functionality is similar for all display notifications 400B-1000B.

[0256] In some aspects, the display notification 400B (e.g., alert 406b) can continue to be displayed each time the patient accesses the current analyte reading and / or analyte display in the software application 300, e.g., the current ketones reading on the display 140. In some aspects, the display notification 400B can include a button or other input element to display a recommendation (e.g., recommendation 408b) and / or other text associated with the first conditional logic state 10, or the software application 300 can automatically display the recommendation (e.g., recommendation 408b) and / or other text associated with the first conditional logic state 10, e.g., on the display 140 (e.g., numerical display 146) and / or ketones display (e.g., graphical display 142) showing the current ketones reading. In some aspects, the display notification 400B can include a recommended insulin and / or carbohydrate amount (e.g., consume 15 grams of carbohydrates). In some aspects, the above recommendation functionality is similar for all display notifications 400B-1000B.

[0257] FIG. 5B and FIG. 5A A state diagram 500A and corresponding display notification 500B of a second conditional logic state 20 of the software application 300 according to exemplary aspects is shown. As shown, the state diagram 500A can include a step 502, a step 504, a step 506, and a step 508. In step 502, as shown, sensor data 312 (e.g., second analyte level 314b) can be acquired from the analyte measurement system 110 via the software application 300 (e.g., conditional logic system 310). In step 504, as shown, a determination can be made as to whether the second analyte level 314b is greater than a second threshold 316b (e.g., 200 mg / dL). FIG. 5A As shown, the alert 406b can be displayed on the display device 130. In step 408, as shown, a recommendation (e.g., recommendation 356) can be provided to the patient (e.g., via the display 140) based on the detected first conditional logic state 10. For example, as shown, the recommendation 408b can be displayed on the display device 130. FIG. 5A As shown, the alert 406b can be displayed on the display device 130. In step 408, as shown, a recommendation (e.g., recommendation 356) can be provided to the patient (e.g., via the display 140) based on the detected first conditional logic state 10. For example, as shown, the recommendation 408b can be displayed on the display device 130. FIG. 5AAs shown, the second analyte level 314b (e.g., ketone level) can be compared to the second analyte threshold 338 (e.g., a high ketone threshold of about 3.0 mmol / L) to determine whether the second analyte level 314b (e.g., ketone level) transitions from being above the high second analyte threshold 338 (e.g., a high ketone threshold of about 3.0 mmol / L) to being below the high second analyte threshold, thereby detecting the second conditional logic state 20 (e.g., condition detection 320). In step 506, as FIG. 5B As shown, an alert (e.g., alert 354) can be provided to the patient (e.g., via the display 140) based on the detected second conditional logic state 20. For example, as FIG. 5A As shown, the alert 506b can be displayed on the display device 130. In step 508, as FIG. 5B As shown, optionally, a prompt (e.g., prompt 358) can be provided to the patient (e.g., via the display 140) based on the detected second conditional logic state 20. For example, as FIG. 6A As shown, optionally, the prompt 408b can be displayed on the display device 130.

[0258] In some aspects, the display notification 500B (e.g., alert 506b) will repeat periodically (e.g., every hour) for a time period (e.g., 4 hours (e.g., which can be configured in the predetermined settings 332)) until the ketone level is below the moderate ketone threshold (e.g., about 1.0 mmol / L). In some aspects, the software application 300 can record or store the patient’s responses or selections to the prompt 508b (e.g., contextual data 362). For example, once this information (e.g., contextual data 362) is entered and recorded, the display notification 500B can be discontinued until the next time the condition is detected.

[0259] FIG. 6B and FIG. 6A A state diagram 600A and corresponding display notification 600B of a third conditional logic state 30 of the software application 300 according to exemplary aspects is shown. As FIG. 6A As shown, the state diagram 600A can include a step 602, a step 604, a step 606, a step 608, and a step 610. In step 602, as FIG. 6A As shown, sensor data 312 (e.g., second analyte level 314b) can be acquired from the analyte measurement system 110 via the software application 300 (e.g., conditional logic system 310). In step 604, as FIG. 6AAs shown, the second analyte level 314b (e.g., ketone level) can be compared to the second analyte threshold 338 (e.g., a high ketone threshold of about 3.0 mmol / L, a low ketone threshold of about 0.5 mmol / L) to determine whether the second analyte level 314b (e.g., ketone level) transitions from above the high second analyte threshold 338 (e.g., a high ketone threshold of about 3.0 mmol / L) to below the low second analyte threshold 338 (e.g., a low ketone threshold of about 0.5 mmol / L), thereby detecting a third conditional logic state 30 (e.g., condition detection 320). In step 606, as shown, FIG. 6B As shown, based on the detected third conditional logic state 30, an alert (e.g., alert 354) can be provided to the patient (e.g., via display 140). For example, as shown, FIG. 6A As shown, an alert 606b (e.g., notification) can be displayed on the display device 130 to indicate that the ketone level has returned. In step 608, as shown, FIG. 6B As shown, based on the detected third conditional logic state 30, a first prompt (e.g., prompt 358) can be provided to the patient (e.g., via display 140). For example, as shown, FIG. 6A As shown, a first prompt 608b can be displayed on the display device 130. In step 610, as shown, FIG. 6B As shown, based on the detected third conditional logic state 30, a second prompt (e.g., prompt 358) can be provided to the patient (e.g., via display 140). For example, as shown, FIG. 7A As shown, a second prompt 610b can be displayed on the display device 130.

[0260] In some aspects, if the answer display notification 600B (e.g., first prompt 608b, second prompt 610b) is not answered, the answer display notification 600B will sound for a short period of time (e.g., about 15 seconds) and then reappear periodically (e.g., every hour) until the answer display notification 600B is answered. In some aspects, the third conditional logic state 30 will only occur after the ketone level remains below the low ketone threshold (e.g., about 0.5 mmol / L) for a period of time (e.g., 2 hours) (e.g., which can be configured in the predetermined settings 332). In some aspects, the software application 300 can record or store the patient’s answer or selection to the first prompt 608b and / or the second prompt 610b (e.g., contextual data 362). For example, once this information (e.g., contextual data 362) is entered and recorded, the answer display notification 600B can be discontinued until the next time the condition is detected.

[0261] FIG. 7B and FIG. 7A A state diagram 700A and corresponding display notification 700B showing a fourth conditional logic state 40 of the software application 300 according to exemplary aspects are shown. As shown,FIG. 7A As shown, the state diagram 700A can include a step 702, a step 704, a step 706, and a step 708. In the step 702, as FIG. 7A As shown, sensor data 312 (e.g., second analyte levels 314b) can be acquired from the analyte measurement system 110 via the software application 300 (e.g., conditional logic system 310). In the step 704, as FIG. 7A As shown, the second analyte levels 314b (e.g., ketone levels) can be compared to the second analyte thresholds 338 (e.g., a medium ketone threshold of about 1.0 mmol / L, a low ketone threshold of about 0.5 mmol / L) to determine whether the second analyte levels 314b (e.g., ketone levels) transition from above the medium second analyte threshold 338 (e.g., a medium ketone threshold of about 1.0 mmol / L) to below the low second analyte threshold 338 (e.g., a low ketone threshold of about 0.5 mmol / L), thereby detecting a fourth conditional logic state 40 (e.g., condition detection 320). In the step 706, as FIG. 7B As shown, an alert (e.g., alert 354) can be provided to the patient (e.g., via the display 140) based on the detected fourth conditional logic state 40. For example, as FIG. 7A As shown, an alert 706b (e.g., a notification) can be displayed on the display device 130 to indicate that ketone levels have returned. In the step 708, as FIG. 7B As shown, a prompt (e.g., prompt 358) can be provided to the patient (e.g., via the display 140) based on the detected fourth conditional logic state 40. For example, as FIG. 8A As shown, a prompt 708b can be displayed on the display device 130.

[0262] In some aspects, if the answer display notification 700B (e.g., prompt 708b) is not answered, the answer display notification 700B will sound for a short period of time (e.g., about 15 seconds) and then reappear periodically (e.g., every hour) until the answer display notification 700B is answered. In some aspects, the software application 300 can record or store the patient’s answer or selection to the prompt 708b (e.g., contextual data 362). For example, once this information (e.g., contextual data 362) is entered and recorded, the answer display notification 700B can be discontinued until the next time the condition is detected.

[0263] FIG. 8B and FIG. 8A A state diagram 800A and a corresponding answer display notification 800B of a fifth conditional logic state 50 of the software application 300 according to exemplary aspects are shown. As FIG. 8A As shown, the state diagram 800A can include a step 802, a step 804, a step 806, a step 808, a step 810, and a step 812. In the step 802, asFIG. 8A As shown, sensor data 312 (e.g., first analyte level 314a, second analyte level 314b) can be acquired from analyte measurement system 110 via software application 300 (e.g., conditional logic system 310). In step 804, as shown, FIG. 8A As shown, second analyte level 314b (e.g., ketone level) can be compared to second analyte threshold 338 (e.g., high ketone threshold of about 3.0 mmol / L, low ketone threshold of about 0.5 mmol / L) to determine whether second analyte level 314b (e.g., ketone level) is below high second analyte threshold 338 (e.g., high ketone threshold of about 3.0 mmol / L) and above low second analyte threshold 338 (e.g., low ketone threshold of about 0.5 mmol / L).

[0264] In step 806, as shown, FIG. 8A As shown, (e.g., ketone level is below high ketone threshold and above low ketone threshold), first analyte level 314a (e.g., glucose level) can be compared to first analyte threshold 336 (e.g., high glucose threshold of about 180 mg / dL) to determine whether first analyte level 314a (e.g., glucose level) is above high first analyte threshold 336 (e.g., high glucose threshold of about 180 mg / dL), thereby detecting fifth conditional logic state 50 (e.g., condition detection 320). In some aspects, step 806 can precede step 804, such that first analyte level 314a (e.g., glucose level) is checked first before second analyte level 314b (e.g., ketone level) is checked. In some aspects, as shown, FIG. 8A As shown, step 804 can precede step 806, such that second analyte level 314b (e.g., ketone level) is checked first before first analyte level 314a (e.g., glucose level) is checked.

[0265] In step 808, as shown, FIG. 8B As shown, an alert (e.g., alert 354) can be provided to the patient (e.g., via display 140) based on the detected fifth conditional logic state 50. For example, as shown, FIG. 8A As shown, alert 808b can be displayed on display device 130. In step 810, as shown, FIG. 8B As shown, a suggestion (e.g., suggestion 356) can be provided to the patient (e.g., via display 140) based on the detected fifth conditional logic state 50. For example, as shown, FIG. 8A As shown, suggestion 810b can be displayed on display device 130. In step 812, as shown, FIG. 8BAs shown, a prompt (e.g., prompt 358) can be provided to the patient based on the detected fifth conditional logic state 50 (e.g., via display 140). For example, as FIG. 9A As shown, prompt 812b can be displayed on display device 130.

[0266] In some respects, as long as the fifth conditional logic state 50 remains unchanged (e.g., ketone levels are below the high ketone threshold and above the low ketone threshold, and glucose levels are above the high glucose threshold), the display notification 800B (e.g., alarm 808b) will repeat periodically (e.g., every 70 minutes). In some respects, the software application 300 may record or store the patient's response or selection to the prompt 812b (e.g., contextual data 362). For example, once this information (e.g., contextual data 362) is entered and recorded, the display notification 800B may be interrupted until the next time the condition is detected.

[0267] FIG. 9B and FIG. 9A A state diagram 900A and a corresponding display notification 900B are shown for the sixth conditional logic state 60 of software application 300 according to an exemplary aspect. (See diagram 900A for example.) FIG. 9A As shown, state diagram 900A may include steps 902, 904, 906, 908, 910, and 912. In step 902, as... FIG. 9A As shown, sensor data 312 (e.g., first analyte level 314a, second analyte level 314b) can be acquired from the analyte measurement system 110 via software application 300 (e.g., conditional logic system 310). In step 904, as... FIG. 9A As shown, a second analyte level 314b (e.g., ketone level) can be compared with a second analyte threshold 338 (e.g., a high ketone threshold of about 3.0 mmol / L and a low ketone threshold of about 0.5 mmol / L) to determine whether the second analyte level 314b (e.g., ketone level) is below the high second analyte threshold 338 (e.g., the high ketone threshold of about 3.0 mmol / L) and above the low second analyte threshold 338 (e.g., the low ketone threshold of about 0.5 mmol / L).

[0268] In step 906, as FIG. 9AAs shown (e.g., ketone levels below a high ketone threshold and above a low ketone threshold), a first analyte level 314a (e.g., glucose level) can be compared to a first analyte threshold 336 (e.g., a high glucose threshold of approximately 180 mg / dL and a low glucose threshold of approximately 70 mg / dL) to determine whether the first analyte level 314a (e.g., glucose level) is below a high first analyte threshold 336 (e.g., a high glucose threshold of approximately 180 mg / dL) and above a low first analyte threshold 336 (e.g., a low glucose threshold of approximately 70 mg / dL), thereby detecting a sixth conditional logic state 60 (e.g., state detection 320). In some aspects, step 906 can be performed before step 904, such that the first analyte level 314a (e.g., glucose level) is checked before the second analyte level 314b (e.g., ketone level). In some aspects, as FIG. 9A As shown, step 904 can be performed before step 906, such that the second analyte level 314b (e.g., ketone level) is examined first before the first analyte level 314a (e.g., glucose level).

[0269] In step 908, as FIG. 9B As shown, an alarm (e.g., alarm 354) can be provided to the patient based on the detected sixth conditional logic state 60 (e.g., via display 140). For example, as FIG. 9A As shown, alarm 908b can be displayed on display device 130. In step 910, as... FIG. 9B As shown, suggestions (e.g., suggestion 356) can be provided to the patient based on the detected sixth conditional logic state 60 (e.g., via display 140). For example, as FIG. 9A As shown, it is suggested that 910b can be displayed on display device 130. In step 912, as... FIG. 9B As shown, a prompt (e.g., prompt 358) can be provided to the patient based on the detected sixth conditional logic state 60 (e.g., via display 140). For example, as FIG. 10A As shown, prompt 912b can be displayed on display device 130.

[0270] In some respects, as long as the sixth conditional logic state 60 remains unchanged (e.g., ketone levels are below the high ketone threshold and above the low ketone threshold, and glucose levels are below the high glucose threshold and above the low glucose threshold), the display notification 900B (e.g., alarm 908b) will repeat periodically (e.g., every 70 minutes). In some respects, the software application 300 may record or store the patient's response or selection to the prompt 912b (e.g., contextual data 362). For example, once this information (e.g., contextual data 362) is entered and recorded, the display notification 900B may be interrupted until the next time the condition is detected.

[0271] FIG. 10B and FIG. 10A A state diagram 1000A and a corresponding display notification 1000B are shown for the seventh conditional logic state 70 of a software application 300 according to an exemplary aspect. (As...) FIG. 10A As shown, state diagram 1000A may include steps 1002, 1004, 1006, 1008, 1010, and 1012. In step 1002, as... FIG. 10A As shown, sensor data 312 (e.g., first analyte level 314a, second analyte level 314b) can be acquired from the analyte measurement system 110 via software application 300 (e.g., conditional logic system 310). In step 1004, as... FIG. 10A As shown, a second analyte level 314b (e.g., ketone level) can be compared with a second analyte threshold 338 (e.g., a high ketone threshold of about 3.0 mmol / L and a low ketone threshold of about 0.5 mmol / L) to determine whether the second analyte level 314b (e.g., ketone level) is below the high second analyte threshold 338 (e.g., the high ketone threshold of about 3.0 mmol / L) and above the low second analyte threshold 338 (e.g., the low ketone threshold of about 0.5 mmol / L).

[0272] In step 1006, as FIG. 10A As shown (e.g., ketone levels below a high ketone threshold and above a low ketone threshold), a first analyte level 314a (e.g., glucose level) can be compared to a first analyte threshold 336 (e.g., a low glucose threshold of approximately 70 mg / dL) to determine whether the first analyte level 314a (e.g., glucose level) is below the lower first analyte threshold 336 (e.g., a low glucose threshold of approximately 70 mg / dL), thereby detecting a seventh conditional logic state 70 (e.g., condition detection 320). In some aspects, step 1006 may be performed before step 1004, such that the first analyte level 314a (e.g., glucose level) is checked before the second analyte level 314b (e.g., ketone level). In some aspects, such as FIG. 10A As shown, step 1004 can be performed before step 1006, such that the second analyte level 314b (e.g., ketone level) is examined first before the first analyte level 314a (e.g., glucose level).

[0273] In step 1008, as FIG. 10B As shown, an alarm (e.g., alarm 354) can be provided to the patient based on the detected seventh conditional logic state 70 (e.g., via display 140). For example, as FIG. 10AAs shown, the alert 1008b can be displayed on the display device 130. In step 1010, as shown, a suggestion (e.g., suggestion 356) can be provided to the patient (e.g., via the display 140) based on the detected seventh conditional logic state 70. For example, as shown, the suggestion 1010b can be displayed on the display device 130. In step 1012, as shown, a prompt (e.g., prompt 358) can be provided to the patient (e.g., via the display 140) based on the detected seventh conditional logic state 70. For example, as shown, the prompt 1012b can be displayed on the display device 130. FIG. 10B FIG. 10A FIG. 10B Clause

[0274] In some aspects, if the display notification 1000B (e.g., the alert 1008b) is ignored, the display notification 1000B will sound for a short period of time (e.g., about 15 seconds) and then repeat periodically (e.g., every 20 minutes) as long as the sixth conditional logic state 60 remains unchanged (e.g., the ketone level is below the high ketone threshold and above the low ketone threshold, and the glucose level is below the low glucose threshold). In some aspects, the software application 300 can record or store the patient’s response or selection to the prompt 1012b (e.g., contextual data 362). For example, once that information (e.g., contextual data 362) is entered and recorded, the display notification 1000B can be discontinued until the next time the condition is detected.

[0275] It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or

[0276] The following examples are illustrative of the various aspects of the present disclosure and are not meant to be limiting in any way. Other suitable modifications and adaptations of various conditions and parameters normally encountered in the field and of materials used for implementing the disclosure as set forth herein can be employed without departing from the spirit and scope of the disclosure.

[0277] While specific aspects have been described above, it is to be understood that these aspects can be practiced otherwise than as described. This description is not intended to limit the scope of claims.

[0278] It is to be understood that the detailed description is merely intended to illustrate the application and that changes can be made in the form, details and arrangement of parts without departing from the sprit and scope of the application. While the application has been described in an illustrative manner, equivalent and alternative implementations are possible.

[0279] ​​​​The aspects have been described above, by way of illustration, with respect to specific embodiments and implementations, together with some of the features they utilize. The boundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed.

[0280] The foregoing description of certain aspects of the present subject matter will so fully reveal the general nature of these aspects that others can, by applying knowledge within the skill in the art, readily modify and / or adapt for various applications such specific aspects, without undue experimentation, notwithstanding any variations, without departing from the general concept, and that such adaptations and modifications are intended to be comprehended within the meaning and range of equivalents of the disclosed aspects.

[0281] The breadth and scope of these aspects should not be limited to any of the above-described exemplary aspects, but should be defined in accordance with the following claims and their equivalents.

[0282] In review of and / or supplemental to the implementations described thus far, various aspects of the subject matter are set forth below, with emphasis on the interrelationships and interchangeability of the following implementations. In other words, emphasis is on the fact that each feature of an implementation can be combined with each and every other feature, unless otherwise explicitly noted or logically inconsistent. The implementations described herein are restated and expanded upon in the following paragraphs, without explicit reference to the drawings.

[0283] In many implementations, a system for continuous ketone monitoring of a subject includes: a sensor control device including an in vivo analyte sensor including: a proximal portion configured to be positioned above a skin of the subject and electrically coupled with electronics disposed in an electronics housing of the sensor control device; and a distal portion configured to be positioned transcutaneously through the skin of the subject and in contact with a bodily fluid of the subject, wherein the distal portion of the in vivo analyte sensor is further configured to sense a ketone level in the bodily fluid; a reader device including: wireless communication circuitry configured to receive data indicative of the ketone level from the sensor control device; a display; and one or more processors coupled with the wireless communication circuitry, the display, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: display at least one of: (1) a current ketone level and an indicator of a current ketone trend based on the data indicative of the ketone level, (2) a ketone trend graph including ketone levels over a time period, wherein the ketone levels are based on the data indicative of the ketone level, and (3) a total amount of time over the time period that the ketone level was above at least one predetermined threshold level; determine whether the current ketone level is above the at least one predetermined threshold level; and output an alert in response to determining that the current ketone level is above the at least one predetermined threshold level, wherein the alert is output periodically while the current ketone level is above the at least one predetermined threshold level.

[0284] In some embodiments, the instructions cause the one or more processors to display at least two of: (1) a current ketone level and an indicator of a current ketone trend based on the data indicative of the ketone level, (2) a ketone trend graph including ketone levels over a time period, wherein the ketone levels are based on the data indicative of the ketone level, and (3) a total amount of time over the time period that the ketone level was above at least one predetermined threshold level.

[0285] In some embodiments, the instructions cause the one or more processors to display: (1) a current ketone level and an indicator of a current ketone trend based on the data indicative of the ketone level, (2) a ketone trend graph including ketone levels over a time period, wherein the ketone levels are based on the data indicative of the ketone level, and (3) a total amount of time over the time period that the ketone level was above at least one predetermined threshold level.

[0286] In some embodiments, the alert is output at least about every 5 minutes when the current ketone level is above the at least one predetermined threshold level.

[0287] In some embodiments, the sensor control device includes wireless communication circuitry configured to periodically transmit the data indicative of the ketone level.

[0288] In some embodiments, the at least one predetermined threshold level is 1.0 mmol / L.

[0289] In some embodiments, the at least one predetermined threshold level includes a first threshold level and a second threshold level, wherein the first threshold level is 1.0 mmol / L and the second threshold level is 1.5 mmol / L.

[0290] In some embodiments, the at least one predetermined threshold level includes a first threshold level and a second threshold level greater than the first threshold level, wherein the alert is periodically output according to a first time interval when the current ketone level is above the first threshold level, and wherein the alert is periodically output according to a second time interval less than the first time interval when the current ketone level is above the second threshold level.

[0291] In some embodiments, the instructions further cause the one or more processors to: determine whether the rate of change is above a predetermined rate of change threshold over a second time period; and output a notification including a recommendation to check the subject’s ketone level with a blood ketone measurement.

[0292] In some embodiments, the instructions further cause the one or more processors to output ketone data to a document, the ketone data based on the data indicative of the ketone level.

[0293] In some embodiments, the in vivo analyte sensor is a ketone sensor.

[0294] In some embodiments, the ketone data is exported to an email.

[0295] In some embodiments, the document is an attachment to an email.

[0296] In some embodiments, the ketone data in the document is arranged according to a plurality of fields, the plurality of fields including a first field associated with a current ketone level.

[0297] In some embodiments, the plurality of fields further includes a second field associated with a ketone trend graph.

[0298] In some embodiments, the plurality of fields further includes a third field associated with an alert notification.

[0299] In some embodiments, the instructions further cause the one or more processors to output a suggested treatment.

[0300] In some embodiments, the system further comprises a medication delivery device, and the suggested treatment comprises a suggested insulin dose, and wherein the instructions further cause the one or more processors to: output the suggested insulin dose to the medication delivery device.

[0301] In some embodiments, the instructions further cause the one or more processors to cause the medication delivery device to deliver medication to the subject according to the suggested insulin dose.

[0302] In some embodiments, if the current ketone level is between about 1.5 mmol / L and 2.5 mmol / L, the instructions further cause the one or more processors to: prompt the subject to indicate whether an insulin dose was missed; and in response to an indication that an insulin dose was missed, display a suggestion to administer the missed insulin dose and to ingest carbohydrates.

[0303] In some embodiments, if the current ketone level is higher than about 2.5 mmol / L, the instructions further cause the one or more processors to: prompt the subject to indicate whether an insulin dose was missed; and in response to an indication that an insulin dose was missed, display a suggestion to administer the missed insulin dose and to ingest carbohydrates.

[0304] In some embodiments, the instructions further cause the one or more processors to update the current ketone level at least about every minute.

[0305] In some embodiments, the instructions further cause the one or more processors to update the current ketone level at least about every 5 minutes.

[0306] In some embodiments, the instructions further cause the one or more processors to update the ketone trend graph at least about every 5 minutes.

[0307] In some embodiments, the instructions further cause the one or more processors to update the ketones trend graph at least about every 10 minutes.

[0308] In many embodiments, a system comprises: an analyte measurement system configured to measure a first analyte and a second analyte of a patient, the analyte measurement system comprising at least one analyte sensor; a processor in communication with the analyte measurement system, wherein the processor is coupled to a memory storing instructions that, when executed, cause the processor to: obtain sensor data of the first analyte level and the second analyte level, and detect at least one condition associated with the sensor data; and an insulin delivery system in communication with the processor, wherein the processor is configured to cause the insulin delivery system to deliver insulin based on the first analyte level and the second analyte level.

[0309] In many embodiments, a system comprises: an analyte measurement system configured to measure a first analyte and a second analyte of a patient, the analyte measurement system comprising at least one analyte sensor; a processor in communication with the analyte measurement system, wherein the processor is coupled to a memory storing instructions that, when executed, cause the processor to: obtain sensor data of the first analyte level and the second analyte level, and detect at least one condition associated with the sensor data; and an insulin delivery system in communication with the processor, wherein the processor is configured to cause the insulin delivery system to deliver insulin in response to detecting the at least one condition.

[0310] In some embodiments, the first analyte comprises glucose and the second analyte comprises ketones.

[0311] In some embodiments, the insulin dose is calculated based on the glucose level, and the calculated insulin dose is further adjusted based on the ketone level.

[0312] In some embodiments, the insulin dose is calculated based on a glucose response model, wherein inputs in the glucose response model comprise basal insulin, insulin sensitivity, and carbohydrate ratio.

[0313] In some embodiments, the insulin sensitivity and / or the carbohydrate ratio are adjusted based on the glucose level and the ketone level.

[0314] In some embodiments, the system further comprises a display coupled to the processor and configured to visually present information, wherein the processor causes the display to display one or more first trend arrows of the first analyte and one or more second trend arrows of the second analyte.

[0315] In some embodiments, the one or more first trend arrows and the one or more second trend arrows comprise a same number of trend arrows.

[0316] In some embodiments, the one or more first trend arrows and the one or more second trend arrows comprise different numbers of trend arrows.

[0317] In some embodiments, the one or more first trend arrows and the one or more second trend arrows display the same rate of change units for the first analyte and the second analyte.

[0318] In some embodiments, the rate of change units are mmol / L / min.

[0319] In some embodiments, the one or more first trend arrows and the one or more second trend arrows display different rate of change units for the first analyte and the second analyte.

[0320] In some embodiments, the one or more first trend arrows comprise flat arrows to indicate, for the one or more first trend arrows, different rate of change units relative to the one or more second trend arrows.

[0321] In some embodiments, the detected at least one condition is insulin deficiency.

[0322] In some embodiments, the insulin deficiency is caused by occlusion of an insulin delivery system.

[0323] In some embodiments, the at least one sensor comprises: a proximal portion configured to be positioned above the skin of the subject and electrically coupled with electronics disposed in an electronics housing of the sensor control device; and a distal portion configured to be positioned transcutaneously through the skin of the subject and in contact with a bodily fluid of the subject, wherein the distal portion of the sensor is further configured to sense at least one of the first analyte and the second analyte in the bodily fluid.

[0324] In some embodiments, the at least one sensor comprises a first sensor configured to measure the first analyte and a second sensor configured to measure the second analyte.

[0325] In some embodiments, the at least one sensor comprises a single sensor configured to measure the first analyte and the second analyte.

[0326] In some embodiments, the single sensor comprises a first sensing region configured to measure the first analyte and a second sensing region configured to measure the second analyte.

[0327] In many embodiments, a system includes an analyte measurement system configured to measure a first analyte and a second analyte of a patient, the analyte measurement system including an analyte sensor and a display device; a processor in communication with the analyte measurement system, wherein the processor is coupled to a memory storing instructions that, when executed, cause the processor to: obtain sensor data of a first analyte level and a second analyte level, detect at least one condition associated with the sensor data, provide a notification to the patient associated with the detected at least one condition, prompt the patient to input contextual data, wherein the contextual data is associated with the detected at least one condition, save a record of the detected at least one condition with the contextual data, and generate a report including the detected at least one condition and the associated contextual data.

[0328] In some embodiments, the processor is configured to operate based on conditional logic associated with predetermined settings.

[0329] In some embodiments, the processor is configured to adjust the predetermined settings.

[0330] In some embodiments, the predetermined settings include a first threshold for the first analyte and a second threshold for the second analyte.

[0331] In some embodiments, the predetermined settings include a first threshold and a second threshold for the first analyte and a third threshold and a fourth threshold for the second analyte.

[0332] In some embodiments, the processor is configured to adjust the conditional logic based on a rate of change of the first analyte level and / or the second analyte level.

[0333] In some embodiments, the processor is configured to adjust the conditional logic based on insulin sensitivity, carbohydrate ratio, patient disease state, patient medication regimen, and / or pairing with a remote bolus system.

[0334] In some embodiments, the first analyte and the second analyte are measured continuously in real time.

[0335] In some embodiments, the first analyte is measured at a different frequency than the second analyte.

[0336] In some embodiments, the analyte sensor includes a dual analyte sensor to measure the first analyte and the second analyte.

[0337] In some embodiments, the analyte sensor includes two separate analyte sensors to measure the first analyte and the second analyte.

[0338] In some embodiments, the first analyte includes glucose and the second analyte includes ketones.

[0339] In some embodiments, the analyte sensor includes a proximal portion configured to be positioned above a skin of a subject and electrically coupled with electronics disposed in an electronics housing of a sensor control device, and a distal portion configured to be positioned transcutaneously through the skin of the subject and in contact with a bodily fluid of the subject, wherein the distal portion of the sensor is further configured to sense at least one of the first analyte and the second analyte in the bodily fluid.

[0340] In some embodiments, the analyte measurement system includes at least one sensor control device including a housing and wireless communication circuitry disposed in the housing configured to transmit sensor data of the first analyte level and the second analyte level.

[0341] In some embodiments, the system further includes an insulin delivery system operatively coupled to the processor, wherein the processor is configured to control the insulin delivery system based on the detected at least one condition.

[0342] In some embodiments, the processor is configured to continue insulin delivery if the ketone level is above the high ketone threshold.

[0343] In some embodiments, the notification includes an alert to the patient associated with the detected at least one condition.

[0344] In some embodiments, the notification includes a suggestion to the patient associated with the detected at least one condition to take action.

[0345] In some embodiments, the processor is configured to provide a second notification to the patient if the detected at least one condition remains unchanged after a predetermined time period.

[0346] In some embodiments, the processor is configured to adjust a threshold value of the first analyte and / or the second analyte based on the contextual data.

[0347] In some embodiments, the contextual data includes a frequency of occurrence of the detected at least one condition. In some embodiments, the frequency includes a number of occurrences of the detected at least one condition in at least one of 1 hour, 6 hours, 12 hours, one day, one week, one month, or a combination thereof.

[0348] In some embodiments, the contextual data includes a degree of discomfort of the patient.

[0349] In some embodiments, the contextual data includes at least one of a pathogenic factor of the patient, a medication taken by the patient, whether the patient has received emergency medical services, or a combination thereof.

[0350] In some embodiments, the processor is configured to perform an analysis of the sensor data to determine a baseline first analyte level and / or a baseline second analyte level.

[0351] In some embodiments, the processor is configured to perform an analysis of the sensor data to determine a predictive model. In some embodiments, the predictive model is based on a population model and one or more parameters that adjust the predictive model to within a known range of variation relative to the population model. In some embodiments, the predictive model is based on at least one of: a regression, a model-based parameter adaptation, a supervised machine learning, an unsupervised machine learning, a neural network, a classification model, or a combination of these.

[0352] In some embodiments, the system further comprises a dose guidance system operatively coupled to the processor.

[0353] In some embodiments, the processor is configured to provide a dose recommendation based on a glycemic response model. In some embodiments, the glycemic response model is based on a basal insulin, an insulin sensitivity, a carbohydrate ratio, and a second analyte.

[0354] In some embodiments, the second analyte comprises a ketone or a lactate.

[0355] In some embodiments, the basal insulin, the insulin sensitivity, and / or the carbohydrate ratio are a function of the second analyte.

[0356] In some embodiments, the processor is configured to obtain additional data from a second sensor. In some embodiments, the additional data comprises at least one of: activity data, a heart rate, a respiratory rate, a body temperature, sweat data, location data, and / or a lactate level.

[0357] In some embodiments, the processor is configured to titrate a dose based on the first analyte level and / or the second analyte level.

[0358] In some embodiments, the processor is configured to determine an erroneous reading based on the first analyte level and / or the second analyte level.

[0359] In some embodiments, the processor is part of an analyte measurement system.

[0360] In some embodiments, the processor comprises a mobile application on a display device.

[0361] In some embodiments, the system further comprises a remote server configured to support the processor.

[0362] In some embodiments, the processor is configured to change a default home screen of the display device based on the detected at least one condition.

[0363] In many embodiments, a method includes measuring, with an analyte measurement system, a first analyte and a second analyte of a patient, the analyte measurement system including an analyte sensor and a display device; acquiring, with a processor in communication with the analyte measurement system, sensor data of the first analyte level and the second analyte level; detecting at least one condition associated with the sensor data; and providing a notification to the patient associated with the detected at least one condition.

[0364] In some embodiments, the method further includes the step of providing a prompt to the patient to acquire additional information regarding the detected at least one condition.

[0365] In some embodiments, the detecting includes utilizing conditional logic associated with predetermined settings.

[0366] In some embodiments, the predetermined settings include a first threshold and a second threshold for the first analyte and a third threshold and a fourth threshold for the second analyte.

[0367] In some embodiments, the measuring includes continuously measuring the first analyte and the second analyte in real time.

[0368] In many embodiments, the sensor includes: a proximal portion configured to be positioned above a skin of a subject and electrically coupled with electronics disposed in an electronics housing of a sensor control device; and a distal portion configured to be positioned transcutaneously through the skin of the subject and in contact with a bodily fluid of the subject, wherein the distal portion of the sensor is further configured to sense at least one of the first analyte and the second analyte in the bodily fluid.

[0369] In many embodiments, the sensor includes: a proximal portion configured to be positioned above a skin of a subject and electrically coupled with electronics disposed in an electronics housing of a sensor control device; and a distal portion configured to be positioned transcutaneously through the skin of the subject and in contact with a bodily fluid of the subject, wherein the distal portion of the sensor is further configured to sense at least one analyte in the bodily fluid.

[0370] ​

[0371] The present invention can also be described in terms of the following clauses: Clause 1. A system comprising: an analyte measurement system configured to measure a first analyte and a second analyte of a patient, the analyte measurement system including an analyte sensor and a display device; and a processor in communication with the analyte measurement system, wherein the processor is coupled to a memory storing instructions that, when executed, cause the processor to: acquiring sensor data of a first analyte level and a second analyte level, detecting at least one condition associated with the sensor data, providing a notification to the patient associated with the detected at least one condition, prompting the patient to input context data, wherein the context data is associated with the detected at least one condition, saving a record of the detected at least one condition with the context data, and generating a report including the detected at least one condition and the associated context data.

[0372] Clause 2. The system of clause 1, wherein the processor is configured to operate based on conditional logic associated with predetermined settings.

[0373] Clause 3. The system of clause 2, wherein the processor is configured to adjust the predetermined settings.

[0374] Clause 4. The system of clause 2 or clause 3, wherein the predetermined settings include a first threshold for the first analyte and a second threshold for the second analyte.

[0375] Clause 5. The system of clause 2 or clause 3, wherein the predetermined settings include a first threshold and a second threshold for the first analyte and a third threshold and a fourth threshold for the second analyte.

[0376] Clause 6. The system of any of clauses 2-5, wherein the processor is configured to adjust the conditional logic based on a rate of change of the first analyte level and / or the second analyte level.

[0377] Clause 7. The system of any of clauses 2-6, wherein the processor is configured to adjust the conditional logic based on insulin sensitivity, carbohydrate ratio, patient disease state, patient medication regimen, and / or pairing with a remote bolus system.

[0378] Clause 8. The system of any of clauses 1-7, wherein the first analyte and the second analyte are continuously measured in real-time.

[0379] Clause 9. The system of any of clauses 1-8, wherein the first analyte is measured at a different frequency than the second analyte.

[0380] Clause 10. The system of any of clauses 1-9, wherein the analyte sensor includes a dual analyte sensor to measure the first analyte and the second analyte.

[0381] Clause 11. The system of any of clauses 1-9, wherein the analyte sensor comprises two separate analyte sensors to measure a first analyte and a second analyte.

[0382] Clause 12. The system of any of clauses 1-11, wherein the first analyte comprises glucose and the second analyte comprises ketones.

[0383] Clause 13. The system of clause 12, further comprising an insulin delivery system operatively coupled to the processor, wherein the processor is configured to control the insulin delivery system based on the detected at least one condition.

[0384] Clause 14. The system of clause 13, wherein the processor is configured to continue insulin delivery if the ketone level is above a high ketone threshold.

[0385] Clause 15. The system of any of clauses 1-14, wherein the notification comprises an alert to the patient associated with the detected at least one condition.

[0386] Clause 16. The system of any of clauses 1-15, wherein the notification comprises a suggestion to the patient associated with the detected at least one condition to take action.

[0387] Clause 17. The system of any of clauses 1-16, wherein the processor is configured to provide a second notification to the patient if the detected at least one condition remains unchanged after a predetermined time period.

[0388] Clause 18. The system of any of clauses 1-17, wherein the processor is configured to adjust a threshold value for the first analyte and / or the second analyte based on the contextual data.

[0389] Clause 19. The system of any of clauses 1-18, wherein the contextual data comprises a frequency at which the detected at least one condition occurs.

[0390] Clause 20. The system of clause 19, wherein the frequency comprises a number of times the detected at least one condition occurs in 1 hour, 6 hours, 12 hours, one day, one week, one month, or a combination thereof.

[0391] Clause 21. The system of any of clauses 1-20, wherein the contextual data comprises a degree of discomfort of the patient.

[0392] Clause 22. The system of any of clauses 1-21, wherein the contextual data comprises a causative factor of the patient, a medication taken by the patient, whether the patient has received emergency medical services, or a combination thereof.

[0393] Clause 23. The system of any of clauses 1-22, wherein the processor is configured to perform an analysis of the sensor data to determine a baseline first analyte level and / or a baseline second analyte level.

[0394] Clause 24. The system of any of clauses 1-23, wherein the processor is configured to perform an analysis of the sensor data to determine a predictive model.

[0395] Clause 25. The system of clause 24, wherein the predictive model is based on a population model and one or more parameters that adjust the predictive model to within a known range of variation relative to the population model.

[0396] Clause 26. The system of clause 25, wherein the predictive model is based on a regression, a model-based parameter adaptation, a supervised machine learning, an unsupervised machine learning, a neural network, a classification model, or a combination of these.

[0397] Clause 27. The system of any of clauses 1-26, further comprising a dose guidance system operatively coupled to the processor.

[0398] Clause 28. The system of any of clauses 1-27, wherein the processor is configured to provide a dose recommendation based on a glycemic response model.

[0399] Clause 29. The system of clause 28, wherein the glycemic response model is based on a basal insulin, an insulin sensitivity, a carbohydrate ratio, and a second analyte.

[0400] Clause 30. The system of any of clauses 1-29, wherein the second analyte comprises a ketone or a lactate.

[0401] Clause 31. The system of clause 29 or clause 30 dependent on clause 29, wherein the basal insulin, the insulin sensitivity, and / or the carbohydrate ratio is a function of the second analyte.

[0402] Clause 32. The system of any of clauses 1-31, wherein the processor is configured to obtain additional data from a second sensor.

[0403] Clause 33. The system of clause 32, wherein the additional data comprises activity data, a heart rate, a respiration rate, a body temperature, a sweat data, a location data, and / or a lactate level.

[0404] Clause 34. The system of any of clauses 1-33, wherein the processor is configured to titrate a dose based on the first analyte level and / or the second analyte level.

[0405] Clause 35. The system of any of clauses 1-34, wherein the processor is configured to determine an erroneous reading based on the first analyte level and / or the second analyte level.

[0406] Clause 36. The system of any of clauses 1-35, wherein the processor is part of an analyte measurement system.

[0407] Clause 37. The system of any of clauses 1-36, wherein the processor comprises a mobile application on a display device.

[0408] Clause 38. The system of any of clauses 1-37, further comprising a remote server configured to support the processor.

[0409] Clause 39. The system of any of clauses 1-38, wherein the processor is configured to change a default home screen of the display device based on the detected at least one condition.

[0410] Clause 40. A method comprising: measuring a first analyte and a second analyte of a patient with an analyte measurement system comprising an analyte sensor and a display device; obtaining sensor data of the first analyte level and the second analyte level with a processor in communication with the analyte measurement system; detecting at least one condition associated with the sensor data; and providing a notification to the patient associated with the detected at least one condition.

[0411] Clause 41. The method of clause 40, further comprising providing a prompt to the patient to obtain additional information regarding the detected at least one condition.

[0412] Clause 42. The method of clause 40 or clause 41, wherein the detecting comprises utilizing conditional logic associated with predetermined settings.

[0413] Clause 43. The method of clause 42, wherein the predetermined settings comprise a first threshold and a second threshold for the first analyte and a third threshold and a fourth threshold for the second analyte.

[0414] Clause 44. The method of any of clauses 40-43, wherein the measuring comprises continuously measuring the first analyte and the second analyte in real-time.

[0415] Clause 45. A system comprising: an analyte measurement system configured to measure a first analyte and a second analyte of a patient, the analyte measurement system comprising an analyte sensor; a processor in communication with the analyte measurement system, wherein the processor is coupled to a memory storing instructions that, when executed, cause the processor to: obtain sensor data of a first analyte level and a second analyte level, and detect at least one condition associated with the sensor data; and an insulin delivery system in communication with the processor, wherein the processor is configured to cause the insulin delivery system to deliver insulin based on the first analyte level and the second analyte level.

[0416] Clause 46. The system of Clause 45, wherein the first analyte comprises glucose and the second analyte comprises ketones.

[0417] Clause 47. The system of Clause 46, wherein an insulin dose is calculated based on the glucose level and the calculated insulin dose is further adjusted based on the ketone level.

[0418] Clause 48. The system of Clause 46 or Clause 47, wherein insulin sensitivity and / or carbohydrate ratio is adjusted based on the glucose level and the ketone level.

[0419] Clause 49. The system of any one of Clauses 45-48, further comprising a display configured to display one or more first trend arrows of the first analyte and one or more second trend arrows of the second analyte.

[0420] Clause 50. The system of Clause 49, wherein the one or more first trend arrows and the one or more second trend arrows comprise a same number of trend arrows.

[0421] Clause 51. The system of Clause 49, wherein the one or more first trend arrows and the one or more second trend arrows comprise a different number of trend arrows.

[0422] Clause 52. The system of any one of Clauses 49-51, wherein the one or more first trend arrows and the one or more second trend arrows display a same rate of change unit for the first analyte and the second analyte.

[0423] Clause 53. The system of Clause 52, wherein the rate of change unit is mmol / L / min.

[0424] Clause 54. The system of any one of Clauses 49-51, wherein the one or more first trend arrows and the one or more second trend arrows display a different rate of change unit for the first analyte and the second analyte.

[0425] Clause 55. The system of clause 54, wherein the one or more first trend arrows comprise flat arrows to indicate a different rate of change unit relative to the one or more second trend arrows for the one or more first trend arrows.

[0426] Clause 56. A system for continuous ketone monitoring of a subject, the system comprising: a sensor control device comprising an in vivo analyte sensor comprising: a proximal portion configured to be positioned above the skin of the subject and electrically coupled with electronics disposed in an electronics housing of the sensor control device; and a distal portion configured to be positioned transcutaneously through the skin of the subject and in contact with a bodily fluid of the subject, wherein the distal portion of the in vivo analyte sensor is further configured to sense a ketone level in the bodily fluid; a reader device comprising: wireless communication circuitry configured to receive data indicative of the ketone level from the sensor control device; a display; and one or more processors coupled with the wireless communication circuitry, the display, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to: display at least one of: (1) a current ketone level and an indicator of a current ketone trend based on the data indicative of the ketone level, (2) a ketone trend graph comprising ketone levels over a time period, wherein the ketone levels are based on the data indicative of the ketone level, and (3) a total amount of time over the time period that the ketone level was above at least one predetermined threshold level; determine whether the current ketone level is above the at least one predetermined threshold level; and output an alert in response to determining that the current ketone level is above the at least one predetermined threshold level, wherein the alert is output periodically while the current ketone level is above the at least one predetermined threshold level.

[0427] Clause 57. The system of clause 56, wherein the alert is output at least about every 5 minutes while the current ketone level is above the at least one predetermined threshold level.

[0428] Clause 58. The system of any of clauses 56-57, wherein the sensor control device comprises wireless communication circuitry configured to periodically transmit the data indicative of the ketone level.

[0429] Clause 59. The system of any of clauses 56-57, wherein the at least one predetermined threshold level is 1.0 mmol / L

[0430] Clause 60. The system of any of clauses 56-59, wherein the at least one predetermined threshold level comprises a first threshold level and a second threshold level, wherein the first threshold level is 1.0 mmol / L and the second threshold level is 1.5 mmol / L.

[0431] Clause 61. The system of any of clauses 56-60, wherein the at least one predetermined threshold comprises a first threshold level and a second threshold level greater than the first threshold level, wherein the alert is output periodically according to a first time interval when the current ketone level is above the first threshold level, and wherein the alert is output periodically according to a second time interval less than the first time interval when the current ketone level is above the second threshold level.

[0432] Clause 62. The system of any of clauses 56-61, wherein the instructions further cause the one or more processors to: determine whether the rate of change is above a predetermined rate of change threshold for a second time period; and output a notification comprising a recommendation to check the subject’s ketone level with a blood ketone measurement.

[0433] Clause 63. The system of any of clauses 56-62, wherein the instructions further cause the one or more processors to: output ketone data to a document, the ketone data based on the data indicative of the ketone level.

[0434] Clause 64. The system of any of clauses 56-63, wherein the in vivo analyte sensor is a ketone sensor.

[0435] Clause 65. The system of clause 64, wherein the ketone data is exported to an email.

[0436] Clause 66. The system of clause 63 or 65, wherein the document is an attachment to the email.

[0437] Clause 67. The system of clause 63, 65, or 66, wherein the ketone data in the document is arranged according to a plurality of fields, the plurality of fields comprising a first field associated with a current ketone level.

[0438] Clause 68. The system of clause 67, wherein the plurality of fields further comprises a second field associated with a ketone trend graph.

[0439] Clause 69. The system of any of clauses 67-68, wherein the plurality of fields further comprises a third field associated with an alert notification.

[0440] Clause 70. The system of any of clauses 56-69, wherein the instructions further cause the one or more processors to: outputting the suggested treatment.

[0441] Clause 71. The system of clause 70, further comprising a medication delivery device, wherein the suggested treatment comprises a suggested insulin dose, and wherein the instructions further cause the one or more processors to: output the suggested insulin dose to the medication delivery device.

[0442] Clause 72. The system of clause 71, wherein the instructions further cause the one or more processors to: cause the medication delivery device to deliver medication to the subject according to the suggested insulin dose.

[0443] Clause 73. The system of any of clauses 70-72, wherein if the current ketone level is between about 1.5 mmol / L and 2.5 mmol / L, the instructions further cause the one or more processors to: prompt the subject to indicate whether an insulin dose was missed; and in response to an indication that an insulin dose was missed, display a suggestion to administer the missed insulin dose and to ingest carbohydrates.

[0444] Clause 74. The system of any of clauses 70-73, wherein if the current ketone level is higher than about 2.5 mmol / L, the instructions further cause the one or more processors to: prompt the subject to indicate whether an insulin dose was missed; and in response to an indication that an insulin dose was missed, display a suggestion to administer the missed insulin dose and to ingest carbohydrates.

[0445] Clause 75. The system of any of clauses 56-74, wherein the instructions further cause the one or more processors to: update the current ketone level at least every minute.

[0446] Clause 76. The system of any of clauses 56-75, wherein the instructions further cause the one or more processors to: update the current ketone level at least about every 5 minutes.

[0447] Clause 77. The system of any of clauses 56-76, wherein the instructions further cause the one or more processors to: update the ketone trend graph at least about every 5 minutes.

[0448] Clause 78. The system of any of clauses 56-77, wherein the instructions further cause the one or more processors to: update the ketone trend graph at least about every 10 minutes.

Claims

1. A system for continuous ketone monitoring of a subject, the system comprising: Sensor control device, including an in vivo analyte sensor, said in vivo analyte sensor comprising: The proximal portion is configured to be located above the subject's skin and electrically coupled to electronics disposed in the electronic housing of the sensor control device; and The distal portion is configured to be percutaneously positioned to penetrate the subject's skin and contact the subject's bodily fluids, wherein the distal portion of the in vivo analyte sensor is also configured to sense ketone levels in the bodily fluids; Reader device, including: A wireless communication circuitry system is configured to receive data indicating the ketone level from the sensor control device; Displays; and One or more processors are coupled to the wireless communication circuitry, the display, and a memory storing instructions, which, when executed by the one or more processors, cause the one or more processors to: Display at least one of the following: (1) the current ketone level and an indicator of the current ketone trend based on data indicating the ketone level; (2) a ketone trend graph including the ketone level over a time period, wherein the ketone level is based on data indicating the ketone level; and (3) the total amount of time during which the ketone level is above at least one predetermined threshold level. Determine whether the current ketone level is higher than the at least one predetermined threshold level; and An alarm is output in response to determining that the current ketone level is higher than the at least one predetermined threshold level, wherein the alarm is output periodically when the current ketone level is higher than the at least one predetermined threshold level.

2. The system of claim 1, wherein, When the current ketone level is higher than the at least one predetermined threshold level, the alarm shall be output at least approximately every 5 minutes.

3. The system of claim 1, wherein, The sensor control device includes a wireless communication circuitry system configured to periodically transmit data indicating the ketone level.

4. The system of claim 1, wherein, The at least one predetermined threshold level is 1.0 mmol / L.

5. The system of claim 1, wherein, The at least one predetermined threshold level includes a first threshold level and a second threshold level, wherein the first threshold level is 1.0 mmol / L and the second threshold level is 1.5 mmol / L.

6. The system of claim 1, wherein, The at least one predetermined threshold level includes a first threshold level and a second threshold level greater than the first threshold level, wherein when the current ketone level is higher than the first threshold level, the alarm is periodically output according to a first time interval, and wherein when the current ketone level is higher than the second threshold level, the alarm is periodically output according to a second time interval less than the first time interval.

7. The system of claim 1, wherein, The instruction also causes the one or more processors to: Determine whether the rate of change exceeds a predetermined rate of change threshold during the second time period; and The system outputs a notification that includes a recommendation to check the subject's ketone levels using a blood ketone measurement.

8. The system of claim 1, wherein, The instruction also causes the one or more processors to: Output ketone data to a document, the ketone data being based on data indicating the ketone level.

9. The system of claim 1, wherein, The in vivo analyte sensor is a ketone sensor.

10. The system of claim 8, wherein, The ketone data was exported to an email.

11. The system of claim 8, wherein, The document was an email attachment.

12. The system of claim 8, wherein, The ketone data in the document is arranged according to multiple fields, including a first field associated with the current ketone level.

13. The system of claim 12, wherein, The plurality of fields also includes a second field associated with the ketone trend graph.

14. The system of claim 12, wherein, The multiple fields also include a third field associated with the alarm notification.

15. The system of claim 1, wherein, The instruction also causes the one or more processors to: Provide suggested treatments.

16. The system of claim 15, further comprising a medication delivery device, wherein, The recommended treatment includes a recommended insulin dose, and wherein the instructions further cause the one or more processors to: The recommended insulin dose is output to the drug delivery device.

17. The system of claim 16, wherein, The instruction also causes the one or more processors to: This allows the drug delivery device to deliver the drug to the subject according to the recommended insulin dose.

18. The system of claim 15, wherein, If the current ketone level is between approximately 1.5 mmol / L and 2.5 mmol / L, the instruction also causes the one or more processors to: The subject is prompted to indicate whether an insulin dose has been missed; and In response to an indication that the insulin dose was missed, a recommendation to administer the missed insulin dose and to consume carbohydrates is displayed.

19. The system of claim 15, wherein, If the current ketone level is higher than approximately 2.5 mmol / L, the instruction also causes the one or more processors to: The subject is prompted to indicate whether an insulin dose has been missed; and In response to an indication that the insulin dose was missed, a recommendation to administer the missed insulin dose and to consume carbohydrates is displayed.

20. The system of claim 1, wherein, The instruction also causes the one or more processors to: The current ketone level is updated at least every minute.

21. The system of claim 1, wherein, The instruction also causes the one or more processors to: The current ketone level is updated at least approximately every 5 minutes.

22. The system of claim 1, wherein, The instruction also causes the one or more processors to: The ketone trend chart is updated at least approximately every 5 minutes.

23. The system of claim 1, wherein, The instruction also causes the one or more processors to: The ketone trend graph is updated at least approximately every 10 minutes.

24. A system comprising: An analyte measurement system configured to measure a patient’s first and second analytes, the analyte measurement system including an analyte sensor and a display device; and A processor, communicating with the analyte measurement system, wherein the processor is coupled to a memory storing instructions that, when executed, cause the processor to: Acquire sensor data for the first and second analyte levels. Detect at least one condition associated with the sensor data. Provide the patient with a notification associated with the detected at least one condition. The patient is prompted to input contextual data, wherein the contextual data is associated with the detected at least one condition. The record of the detected at least one condition is saved together with the context data, and Generate a report that includes the detected at least one condition and associated contextual data.

25. The system of claim 24, wherein, The processor is configured to operate based on conditional logic associated with predetermined settings.

26. The system of claim 25, wherein, The processor is configured to adjust the predetermined settings.

27. The system of claim 25, wherein, The predetermined settings include a first threshold for the first analyte and a second threshold for the second analyte.

28. The system of claim 25, wherein, The predetermined settings include a first threshold and a second threshold for the first analyte, and a third threshold and a fourth threshold for the second analyte.

29. The system of claim 25, wherein, The processor is configured to adjust the conditional logic based on the rate of change of the first analyte level and / or the second analyte level.

30. The system of claim 25, wherein, The processor is configured to adjust the conditional logic based on insulin sensitivity, carbohydrate ratio, patient disease status, patient medication regimen, and / or pairing with a remote dosing system.

31. The system of claim 24, wherein, The first analyte and the second analyte are continuously measured in real time.

32. The system of claim 24, wherein, The first analyte is measured at a frequency different from that of the second analyte.

33. The system of claim 24, wherein, The analyte sensor includes a dual analyte sensor to measure the first analyte and the second analyte.

34. The system of claim 24, wherein, The analyte sensor includes two separate analyte sensors to measure the first analyte and the second analyte.

35. The system of claim 24, wherein, The first analyte includes glucose, and the second analyte includes ketones.

36. The system of claim 35, further comprising an insulin delivery system operatively coupled to the processor, wherein, The processor is configured to control the insulin delivery system based on the detected at least one condition.

37. The system of claim 36, wherein, The processor is configured to continue insulin delivery if the ketone level is above a high ketone threshold.

38. The system of claim 24, wherein, The notification includes an alert to the patient associated with the detected at least one condition.

39. The system of claim 24, wherein, The notification includes recommendations related to the detected at least one condition, prompting the patient to take action.

40. The system of claim 24, wherein, The processor is configured to provide a second notification to the patient if the detected at least one condition remains unchanged after a predetermined time period.

41. The system of claim 24, wherein, The processor is configured to adjust the thresholds of the first analyte and / or the second analyte based on the contextual data.

42. The system of claim 24, wherein, The contextual data includes the frequency of occurrence of the detected at least one condition.

43. The system of claim 42, wherein, The frequency includes the number of times the at least one detected condition occurs in 1 hour, 6 hours, 12 hours, 1 day, 1 week, 1 month, or a combination thereof.

44. The system of claim 24, wherein, The contextual data includes the patient's level of discomfort.

45. The system of claim 24, wherein, The contextual data includes the patient's causative factors, the medications the patient is taking, whether the patient received emergency medical services, or a combination of these.

46. The system of claim 24, wherein, The processor is configured to perform analysis of the sensor data to determine baseline first analyte levels and / or baseline second analyte levels.

47. The system of claim 24, wherein, The processor is configured to perform analysis of the sensor data to determine a predictive model.

48. The system of claim 47, wherein, The prediction model is based on a population model and one or more parameters that adjust the prediction model to a known range of variation relative to the population model.

49. The system of claim 48, wherein, The prediction model is based on: regression, model-based parameter adaptation, supervised machine learning, unsupervised machine learning, neural networks, classification models, or a combination thereof.

50. The system of claim 24, further comprising a dose-guiding system operatively coupled to the processor.

51. The system of claim 24, wherein, The processor is configured to provide dosage recommendations based on a blood glucose response model.

52. The system of claim 51, wherein, The blood glucose response model is based on basal insulin, insulin sensitivity, carbohydrate ratio, and the second analyte.

53. The system of claim 52, wherein, The second analyte includes ketones or lactic acid.

54. The system of claim 52, wherein, The basal insulin, the insulin sensitivity, and / or the carbohydrate ratio are functions of the second analyte.

55. The system of claim 24, wherein, The processor is configured to acquire additional data from the second sensor.

56. The system of claim 55, wherein, The additional data includes activity data, heart rate, respiratory rate, body temperature, sweating data, location data, and / or lactate levels.

57. The system of claim 24, wherein, The processor is configured to titrate the dose based on the first analyte level and / or the second analyte level.

58. The system of claim 24, wherein, The processor is configured to determine erroneous readings based on the first analyte level and / or the second analyte level.

59. The system of claim 24, wherein, The processor is part of the analyte measurement system.

60. The system of claim 24, wherein, The processor includes mobile applications on the display device.

61. The system of claim 24 further includes a remote server configured to support the processor.

62. The system of claim 24, wherein, The processor is configured to change the default home screen of the display device based on the detected at least one condition.

63. A method comprising: The patient's first and second analytes are measured using an analyte measurement system, the analyte measurement system including an analyte sensor and a display device; The processor, which communicates with the analyte measurement system, acquires sensor data for the first and second analyte levels. Detect at least one condition associated with the sensor data; and The patient is provided with a notification associated with the detected at least one condition.

64. The method of claim 63, further comprising providing the patient with prompts to obtain additional information about the detected at least one condition.

65. The method of claim 63, wherein, The detection includes using conditional logic associated with predetermined settings.

66. The method of claim 65, wherein, The predetermined settings include a first threshold and a second threshold for the first analyte, and a third threshold and a fourth threshold for the second analyte.

67. The method of claim 63, wherein, The measurement includes continuous real-time measurement of the first analyte and the second analyte.

68. A system comprising: An analyte measurement system configured to measure a patient’s first and second analytes, the analyte measurement system including an analyte sensor; A processor, communicating with the analyte measurement system, wherein the processor is coupled to a memory storing instructions that, when executed, cause the processor to: Acquire sensor data for the first and second analyte levels, and Detect at least one condition associated with the sensor data; and An insulin delivery system communicating with the processor, wherein the processor is configured to cause the insulin delivery system to deliver insulin based on a first analyte level and a second analyte level.

69. The system of claim 68, wherein, The first analyte includes glucose, and the second analyte includes ketones.

70. The system according to claim 69, wherein, Insulin dosage is calculated based on glucose levels, and the calculated insulin dosage is further adjusted based on ketone levels.

71. The system according to claim 69, wherein, Insulin sensitivity and / or carbohydrate ratio are adjusted based on glucose and ketone levels.

72. The system of claim 68 further includes a display configured to display one or more first trend arrows of the first analyte and one or more second trend arrows of the second analyte.

73. The system according to claim 72, wherein, The one or more first trend arrows and the one or more second trend arrows comprise the same number of trend arrows.

74. The system according to claim 72, wherein, The one or more first trend arrows and the one or more second trend arrows include different numbers of trend arrows.

75. The system according to claim 72, wherein, The one or more first trend arrows and the one or more second trend arrows show the same rate of change units for the first analyte and the second analyte.

76. The system according to claim 75, wherein, The rate of change is measured in mmol / L / min.

77. The system according to claim 72, wherein, The one or more first trend arrows and the one or more second trend arrows show the different rate of change units of the first analyte and the second analyte.

78. The system according to claim 77, wherein, The one or more first trend arrows include flat arrows to indicate different rate of change units relative to the one or more second trend arrows.

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