Heart failure readmission

Abstract

Systems and methods of optimizing resources of a medical device system are disclosed, including analyzing physiological information of a patient generated over a particular period of time relative to a previous heart failure event, adjusting a baseline value of the patient from a long term baseline value to one of a shorter term baseline value or a static value in response to a detected or received trigger event, and outputting the adjusted baseline value. The static value is indicative of a baseline value at a previous time relative to the detected or received trigger event, determining a re-admission score for the patient based on the analyzed physiological information, and providing a control signal based on the re-admission score to control a mode or operation of the medical device system to optimize resources of the medical device system.

Description

Heart failure readmission

[0001] Priority requirements

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 588,608, filed on October 6, 2023, which is incorporated herein by reference in its entirety. Technical Field

[0003] This document generally relates to medical devices, and more specifically, but not in a restrictive manner, to systems and methods for determining the readmission metric for heart failure in patients. Background Technology

[0004] Heart failure (HF) is a reduced ability of the heart to pump enough blood to meet the body's needs. Patients with heart failure typically have an enlarged heart with weakened myocardium, resulting in reduced contractility and poor cardiac output. Signs of heart failure include pulmonary congestion, edema, and shortness of breath. Heart failure is usually a chronic condition but can also occur suddenly, affecting the left, right, or both sides of the heart. Causes of heart failure include coronary artery disease, myocardial infarction, hypertension, atrial fibrillation, valvular heart disease, alcoholism, infection, cardiomyopathy, or one or more other conditions that reduce the heart's pumping efficiency.

[0005] Ambulatory medical devices (AMDs), including implantable, subcutaneous, wearable, or other medical devices, can monitor, detect, or treat various conditions, including heart failure (HF) and atrial fibrillation (AF). AMDs may include sensors that sense physiological information from a patient, and one or more circuits that use the sensed physiological information to detect one or more physiological events or transmit the sensed physiological information or detected physiological events to one or more remote devices. Frequent patient monitoring can provide early detection of worsening patient conditions or identification of patients or patient groups at increased risk of future adverse events. Summary of the Invention

[0006] A system and method are disclosed that analyzes a patient’s physiological information generated over a specific time period relative to a previous heart failure event, adjusts the patient’s baseline value from a long-term baseline value to one of a shorter-term baseline value or a static value in response to a detected or received triggering event, the static value indicating the baseline value at a previous time relative to the detected or received triggering event, and determines the patient’s readmission score based on the analyzed physiological information, the patient’s readmission score indicating the risk of subsequent readmission following hospitalization or treatment or discharge from a previous heart failure event.

[0007] An example of the subject matter (a medical device system for improving the determination of readmission risk in heart failure to optimize the resources of the medical device system) may include means for receiving physiological information of a patient; means for analyzing physiological information generated over a specific time period relative to a previous heart failure event relative to the patient's baseline; means for adjusting the patient's baseline value from a long-term baseline value to one of a shorter-term baseline value or a static value in response to a detected or received triggering event, the static value indicating the baseline value at a time prior to the detected or received triggering event; means for determining a readmission score of the patient based on the analyzed physiological information, the patient's readmission score indicating the risk of subsequent readmission following hospitalization or treatment or discharge from a previous heart failure event; and means for providing control signals based on the readmission score to control the mode or operation of the medical device system to optimize the resources of the medical device system.

[0008] In the example, which can be combined with any one or more examples described herein, the apparatus for receiving, analyzing, adjusting, determining, and providing includes a signal receiver circuit configured to receive physiological information of a patient; and an evaluation circuit configured to analyze physiological information generated over a specific time period relative to a previous heart failure event relative to the patient's baseline value; adjust the patient's baseline value from a long-term baseline value to one of a shorter-term baseline value or a static value in response to a detected or received triggering event, the static value indicating the baseline value at a previous time relative to the detected or received triggering event; determine a readmission score for the patient based on the analyzed physiological information, the patient's readmission score indicating the risk of subsequent readmission following hospitalization or treatment or discharge from a previous heart failure event; and provide control signals based on the readmission score to control the mode or operation of the medical device system to optimize the resources of the medical device system.

[0009] An example of the subject matter (a medical device system for improving heart failure readmission risk determination to optimize the resources of the medical device system) may include signal receiver circuitry configured to receive physiological information of a patient; and evaluation circuitry configured to analyze physiological information generated over a specific time period relative to a previous heart failure event relative to the patient's baseline; adjust the patient's baseline value from a long-term baseline value to one of a shorter-term baseline value or a static value, the static value indicating the baseline value at a time prior to the detected or received trigger event, in response to a detected or received trigger event; determine a readmission score for the patient based on the analyzed physiological information, the patient's readmission score indicating the risk of subsequent readmission following hospitalization or treatment or discharge from a previous heart failure event; and provide control signals based on the readmission score to control the mode or operation of the medical device system to optimize the resources of the medical device system.

[0010] An example of the subject matter (a method for improving the determination of readmission risk in heart failure to optimize the resources of a medical device system) may include receiving physiological information of a patient using signal receiver circuitry; analyzing physiological information generated over a specific time period relative to a previous heart failure event using assessment circuitry, relative to the patient's baseline values; adjusting the patient's baseline values ​​from a long-term baseline value to one of a shorter-term baseline value or a static value, the static value indicating the baseline value at a previous time relative to the detected or received trigger event, using assessment circuitry to determine a readmission score for the patient based on the analyzed physiological information, the patient's readmission score indicating the risk of subsequent readmission following hospitalization or treatment or discharge from a previous heart failure event; and providing control signals based on the readmission score using assessment circuitry to control the mode or operation of the medical device system to optimize the resources of the medical device system.

[0011] In the examples, it can be combined with any one or more examples described herein to determine the readmission score, which involves determining the readmission score relative to the patient's adjusted baseline value.

[0012] In the examples, it can be combined with any one or more examples described herein to provide or provide control signals based on the readmission score, including responses to or in response to a determined readmission score exceeding an adjusted baseline threshold, such as those triggered by it.

[0013] In the example, it can be combined with any one or more examples described herein to determine the patient’s readmission score as a composite function of physiological information to be analyzed, including two or more of the following: S1 heart sound information, S3 heart sound information, thoracic impedance, activity information, respiratory information, and heart rate.

[0014] In the example, which may be combined with any one or more examples described herein, the signal receiver circuit or evaluation circuit is configured to receive, or the method includes receiving an instruction to discharge a patient who was hospitalized or treated for a previous heart failure event, wherein determining the patient’s readmission score includes determining that the patient may experience a readmission instruction within a specific time period following receiving the instruction to discharge a patient who was hospitalized or treated for a previous heart failure event, or that the previous heart failure event and discharge may lead to a readmission instruction.

[0015] In the examples, it can be combined with any one or more examples described herein, and the re-admission score indicates future heart failure events.

[0016] In the example, it can be combined with any one or more examples described herein, and the evaluation circuit is configured to determine or the method includes determining a patient's baseline value, including: determining a long-term baseline value as a function of the patient's previous physiological information over 30 days or more; and determining a shorter-term baseline value as a function of the patient's physiological information over a period of time between 3 days and 10 days of previous physiological information, wherein adjusting or modifying the patient's baseline value includes adjusting the patient's baseline value from a long-term baseline value to a shorter-term baseline value.

[0017] In the example, it can be combined with any one or more examples described herein, and the evaluation circuit is configured to determine or the method includes determining a patient's baseline value, including determining a long-term baseline value as a function of the patient's prior physiological information for 30 days or more; and determining a static value, the static value indicating the baseline value relative to a prior time of a detected or received triggering event, the prior time including at least 30 days prior to the detected or received triggering event, wherein adjusting or modifying the patient's baseline value includes adjusting the patient's baseline value from the long-term baseline value to the static value.

[0018] In the examples, which may be combined with any one or more examples described herein, the evaluation circuit is configured to determine the baseline value as a function of the long-term baseline value, the shorter-term baseline value, and the static value during the transition period until the determined readmission score or long-term baseline meets the static value, or the method includes determining the baseline value as a function of the long-term baseline value, the shorter-term baseline value, and the static value during the transition period until the determined readmission score or long-term baseline meets the static value.

[0019] In the example, it can be combined with any one or more examples described herein to analyze or analyze physiological information including analyzing or analyzing S3 heart sound information in a first time period and a second time period, and to determine the patient’s readmission score as a function of the difference in S3 heart sound information in the first time period and the second time period, wherein the first time period is prior to the previous heart failure event and the second time period is after discharge from hospitalization or treatment following the previous heart failure event.

[0020] In the example, it can be combined with any one or more examples described herein, with the first time period being between -25 days and -5 days prior to the previous heart failure event, and the second time period being between 0 days and 30 days after discharge from hospitalization or treatment following the previous heart failure event.

[0021] In the example, it can be combined with any one or more examples described herein to determine that the readmission score comprises a first composite function of physiological information analyzed during a first time period prior to the previous heart failure event, the physiological information including two or more of the following: S1 heart sound information, S3 heart sound information, thoracic impedance, activity information, respiratory information, and heart rate information; a second composite function of S3 heart sound information and S1 heart sound information during a time period prior to the previous heart failure event; and an integrated value of the first composite function during a second time period following discharge from hospitalization or treatment following the previous heart failure event.

[0022] In the example, it can be combined with any one or more examples described herein, and the triggering event is one or more of the following: a received request to adjust the corresponding baseline value, a detected state of alert, or a received or detected indication of a patient's first heart failure event or hospitalization or treatment for a first heart failure event, wherein the first heart failure event is a previous heart failure event.

[0023] In the examples, a system, method, or apparatus may optionally combine any portion or combination of any one or more of the examples described herein to include an "apparatus for" performing any portion of any one or more of the functions, operations, or methods of the examples described herein, or at least one "non-transient machine-readable medium" including instructions that, when executed by a machine, cause the machine to perform any portion of any one or more of the functions or methods of the examples described herein.

[0024] The present invention is intended to provide an overview of the subject matter of this patent application. It is not intended to provide an exclusive or exhaustive interpretation of this disclosure. Detailed descriptions are included to provide further information regarding this patent application. Other aspects of this disclosure will be apparent to those skilled in the art upon reading and understanding the following detailed descriptions and viewing the drawings that form a part thereof, and none of these should be taken in a limiting sense. Attached Figure Description

[0025] In drawings, they are not necessarily drawn to scale, and similar numbers can describe similar parts in different views. Similar numbers with different letter suffixes can represent different instances of similar parts. Drawings generally illustrate the various embodiments discussed in this document by way of example rather than limitation.

[0026] Figures 1 through 5 show example physiological values ​​for multiple patients who were admitted or treated for heart failure events.

[0027] Figure 6 illustrates example baseline determination, including the previous 30-day baseline, the shorter 5-day baseline, and the pre-alert baseline.

[0028] Figure 7 illustrates an example method.

[0029] Figure 8 shows an example medical device system.

[0030] Figure 9 shows an example patient management system.

[0031] Figure 10 illustrates an example implantable medical device system.

[0032] Figure 11 shows an example implantable medical device.

[0033] Figure 12 shows an example remote patient management system.

[0034] Figure 13 shows an example machine on which any one or more of the techniques discussed in this paper can be executed. Detailed Implementation

[0035] Mobile medical devices are devices configured to be implanted in or otherwise positioned on or around a patient to monitor physiological information, such as cardiac electrical activity, heart sounds, respiration, impedance, pressure, physical activity, or other physiological information or one or more other physiological parameters of the patient, or to provide electrical stimulation or one or more other therapies or treatments to optimize or control one or more bodily functions of the patient, such as the contraction of the heart. Mobile medical devices may include implantable or external (e.g., wearable) devices configured to monitor the patient or provide stimulation to the patient, and in some examples, one or more circuits to detect one or more physiological events using sensed physiological information, or to transmit or receive sensed physiological information or detected physiological events to or from one or more remote devices. Frequent patient monitoring can provide early detection of worsening patient disease or identification of patients or patient groups at increased risk of future adverse events.

[0036] Mobile medical devices can provide different monitoring, storage, communication, or treatment modes, but different patients have different power and resource requirements, and the effectiveness of variations varies. For example, various therapy modalities are available to patients, but not all patients receive optimal medical device, therapy mode, or therapy parameter settings during initial programming. A common reason for suboptimal mobile medical device programming is the detection or determination of different events, conditions, or indications. Optimal programming depends, among other things, on the accurate detection and determination of different events, conditions, or indications. Desired clinical outcomes may include, among other things, the selection of cardiac capture, pacing, or sensor vectors, pacing modes, resource usage associated with communication or transmission of physiological data from the mobile medical device, or storage of physiological data.

[0037] Identifying patients or patient groups at high risk of future adverse events allows for control over the selection of modes or features or resource management of one or more mobile medical devices; control over notifications or messages sent to various users associated with a specific patient or patient group within the connected system; organization or scheduling of physician or patient contacts or treatments; or prevention or reduction of patient hospitalizations. Properly identifying and safely managing the risk of patient disease deterioration can avoid unnecessary medical interventions, extend the lifespan of mobile medical devices, reduce healthcare costs, improve patient management, and direct limited healthcare resources to patients who need them most, such as optimizing the resources of a medical device system that includes mobile medical devices.

[0038] The inventors have recognized, among other things, systems and methods for determining a patient's risk of readmission due to heart failure (such as for predicting the risk of readmission or re-hospitalization within one or more long-term future time periods (e.g., greater than 30 days, 90 days, etc.) following the detection or receipt of a cardiac event (including a hospitalization event or a received indication of discharge from a hospitalization event). The systems and methods disclosed herein may include a readmission risk metric (e.g., a readmission score) based on a single sensor or multiple sensors, automatically determined and updated at one or more rule-based or adjustable intervals. In some examples, the readmission risk metric may predict patient-specific readmission (or re-hospitalization) risk at different time intervals (e.g., within the next 7 days, 14 days, 30 days, 90 days, etc.) or as a combination of a set of risks at several future points. Information regarding the readmission risk metric can be used to trigger changes in device operation, transitions between different operating modes, provide alarms or notifications to external devices or processes, or trigger or cause adjustments to one or more other devices to improve the performance of one or more devices associated with the patient, thereby optimizing the resources of a medical device system comprising one or more devices. For example, changes in triggering operations, such as altering monitoring modes to increase resources for sensing, storing, computing, or transmitting physiological information or different metrics in response to an increased or indicated risk of readmission (e.g., a readmission risk metric exceeding a threshold), while reducing or limiting such resource allocation during normal monitoring periods, can improve device efficiency and optimize the effective use of medical devices (including mobile or implantable medical devices or sensors with limited power or available resources) for patients.

[0039] Readmission risk measures based on single or multiple sensors can be determined. In some examples, different physiological information of the patient or various combinations thereof are used, such as heart sound information (e.g., S3 / S1, S3, S1, etc.), respiratory information (respiratory rate (RR), tidal volume (TV), rapid shallow breathing index (RSBI), etc.), activity information, impedance information, temperature information, posture information, arrhythmia information, electrolyte information, oxygen saturation information, composite measurements, or combinations thereof.

[0040] Composite measurement results can include HeartLogic TM Index, HeartLogic during alert time TM Or one or more other composite measurements or measurements thereof. HeartLogic TM The index is a composite health index determined as a function of different physiological information from one or more mobile sensors, including S1 and S3 heart sounds, thoracic impedance, activity information, respiratory information, and nighttime heart rate (nHR), and indicates the state of heart failure, the risk of future heart failure events, or the deterioration of a patient's state of heart failure or the risk of heart failure events over time. TM During the alarm time, HeartLogic TM A measure of the time when an index exceeds an alarm threshold (e.g., a nominal value of 16, but programmable (e.g., by a clinician) to one or more other higher or lower values). In some examples, where HeartLogic... TM The exponential value output can include raw values, such as those shown along the y-axis in Figures 1 through 6, used to determine HeartLogic. TM Information about index values ​​may include relative values, such as values ​​relative to one or more defined patient baselines (e.g., the difference or ratio between daily or short-term (e.g., 3-day, 7-day, 14-day, etc.) values ​​and defined patient baselines or long-term values ​​over a relatively long period of time (e.g., 30 days or longer, etc.).

[0041] In some examples, HeartLogic TM The index can be determined using different combinations or weights of physiological information, including one or more of S1 and S3 heart sounds, thoracic impedance, activity information, rapid shallow breathing index (RSBI), respiratory rate, and nocturnal heart rate (nHR), and in some examples, variations of one or more measurements relative to long-term or baseline information from the patient. In some examples, HeartLogic TMDifferent combinations or weights of the indices can be adjusted or determined based on a risk stratification. In some examples, the risk stratification can be determined as different combinations of physiological information, including one or more of S3, respiratory rate, and activity time (e.g., the amount of time spent at a specific activity level above the patient's average activity level or a specific threshold).

[0042] For example, if the risk stratifier is low or below the first threshold, then HeartLogic TM The index can be determined using a first combination of physiological information. If the risk stratification is high, or above a second threshold, then HeartLogic... TM The index can be determined using a first combination of physiological information and a second combination of physiological information (including additional information beyond that included in the first combination). If the risk stratifier falls between the first and second thresholds, then HeartLogic... TM The index can be determined using one or more indicators or components of the first and second combinations, or using the first and second combinations (but the second combination has less weight than when the risk stratifier is above the second threshold (e.g., using a smaller second combination)).

[0043] In the example, HeartLogic TMIndices and alert times may include detection of worsening heart failure or physiological events, including risk indications or stratification, such as those disclosed in U.S. Patent No. 9,968,266, co-assigned by An et al., entitled "RISK STRATIFICATIONBASED HEART FAILURE DETECTION ALGORITHM," or in U.S. Patent No. 9,622,664, co-assigned by An et al., entitled "METHODS AND APPARATUS FOR DETECTING HEART FAILURE DECOMPENSATION EVENT AND STRATIFYING THE RISK OF THE SAME," or in U.S. Patent No. 10,660,577, co-assigned by Thakur et al., entitled "SYSTEMS AND METHODS FOR DETECTING WORSENING HEART FAILURE," or in U.S. Patent No. 10,660,577, co-assigned by An et al., entitled "HEART FAILURE PATIENT." The disclosures in U.S. Patent Application No. 2014 / 0031643 entitled “STRATIFICATION”, or in U.S. Patent No. 10,085,696 entitled “DETECTION OF WORSENING HEART FAILURE EVENTSUSING HEART SOUNDS”, co-assigned Thakur et al., each of which is incorporated herein by reference in its entirety, including their disclosures on detection of heart failure and worsening heart failure, detection of heart failure risk indicators, and their stratification, etc.

[0044] The inventors have recognized, among other things, that the risk of readmission after discharge associated with treatment for a heart failure event can occur either up to the heart failure event (such as a detected or received hospitalization order), or after discharge from or associated with treatment for a detected or received hospitalization order, using a definitive HeartLogic. TM An index or its components are determined. For example, as shown in Figures 1 through 6, the determined HeartLogic index is compared to patients who have not been readmitted for 30 or 90 days. TM The trend of the index differed significantly for patients readmitted within 30 or 90 days of discharge and associated with heart failure events. This difference persisted until the definitive HeartLogic event was detected. TM The index value, or in other examples, the S1 or S3 heart sound information, is correlated with a defined HeartLogic. TMIndices, whether combined or separated, can be used to determine the risk indicator of future heart failure events, or the likelihood of readmission within a period of time following discharge or treatment associated with a heart failure event.

[0045] Furthermore, the inventors have recognized that, following a heart failure event, one or more defined baseline or long-term values ​​can be adjusted or altered, such as to more accurately track patient progression or improvement after discharge, reduce the impact of pre-event changes on post-event monitoring, and more accurately identify and track patient progression and stability after discharge. If the defined baseline includes changes that lead to heart failure events requiring treatment or hospitalization, then post-discharge HeartLogic... TM The improvement in the index may be overestimated, or the post-discharge rise or gradual increase may be underestimated due to previous high values. Despite existing HeartLogic... TM The index can be determined using a combination of daily or short-term values ​​relative to a baseline or long-term (e.g., 30 days or longer) value and subsequently compared to a threshold. However, the inventors have determined that, after discharge associated with treatment for a heart failure event, shorter baselines (e.g., 5 days, 7 days, 10 days, etc., substantially shorter than existing long-term periods) may be advantageous, more accurately reflecting post-discharge changes in patient status until a new long-term baseline can be established, regardless of the changes in value resulting from the heart failure event requiring treatment or hospitalization. Alternatively, pre-alarm baselines, such as those from 30 days prior to the heart failure event, can be maintained until the changes in value resulting from the heart failure event requiring treatment or hospitalization return to the previous or new baseline value. Once a heart failure event occurs (e.g., 30 days or longer after discharge), HeartLogic... TM When the index value approaches a new baseline value, the new baseline can replace the baseline before the alert or a shorter one, or a smoothing period (e.g., one week) between the baseline before the alert or a shorter one and the new baseline can be implemented, where a function of different baselines can mitigate the transition between different values.

[0046] In other examples, within one or more time periods following discharge associated with a heart failure event (e.g., 7 days, 14 days, 21 days, or combinations thereof, using one or more adjusted baselines in some examples, as described herein), the determined HeartLogic can be used as a basis. TM Changes in the index value, or in other examples, the S1 or S3 heart sound information versus the determined HeartLogic. TMThe exponential event recovery score is determined by combining or separating the exponential indicators. For example, changes indicating continuous improvement or stability can provide a higher exponential event recovery score than changes indicating instability or decline. Additionally, the exponential event recovery score can be determined using the aforementioned pre-alarm or shorter baseline, or a combination thereof, to provide a more accurate assessment of existing HeartLogic data at a commensurate point. TM The index value provides a more accurate indication of recovery.

[0047] In some examples, readmission risk metrics based on single or multiple sensors can be device-based metrics without requiring input of clinical information about the patient, such as clinician diagnosis or risk assessment, patient history, patient age, comorbidities, previous hospitalizations, type of implanted device, etc. In other examples, mortality risk metrics can be a combination of device-based and clinically-based readmission risk metrics, including or considering clinical information about the patient, such as clinician diagnosis or risk assessment, patient history, patient age, comorbidities, previous hospitalizations, type of implanted device, patient-reported outcomes (related to symptoms or quality of life), etc. In some examples, patient history can include previous records of diagnosis, intervention, or treatment of one or more diseases, including chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), anemia, heart failure, etc. Different combinations of clinical information can be determined individually in the examples. Table 1 below shows the determined univariate odds ratios (ORs), 95% confidence intervals (CIs), and p-values ​​(P-values) from logistic regression for additional clinical risk factors and a set of predictors. Values ​​significantly higher or lower than 1 were important predictors, including expected, diuretics, but not expected, race, and depression.

[0048]

[0049] Table 1. Clinical risk factors (30-day primary HF readmission)

[0050] Figure 1 illustrates the HeartLogic data for multiple patients (including yes-re-re-admission group 101 (20 patients) and no-re-re-admission group 102 (83 patients)) before the event, 30 days prior to and up to the event 103 and treatment or hospitalization at day 0, and after day 0 and including the post-discharge period, 30 days after discharge or treatment associated with the event 103. TMExample relationship 100 for the index value (y-axis). The duration of treatment or hospitalization can be represented as day 0, and in some examples, collapsed into a single time period, although this often varies between different patients and clinicians. While it varies within and between different patient groups (e.g., between 2 and 14 days), the length of treatment time between admission and discharge during the treatment period is surprisingly not a statistical indicator of whether it is a readmission group 101 or not a readmission group 102.

[0051] As shown in Figure 1, the re-admission group 101 had a substantially higher HeartLogic at -30 days compared to the non-re-admission group 102. TM The index value starts at approximately 17.5 versus 13, showing a faster rise between -30 days and -10 days (to 26 versus 16, a nearly 50% increase versus a 23% increase), while in alert status (HeartLogic). TM The index value (greater than a threshold, such as 16) was longer and remained higher than that of the no-re-readmission group 102 between -15 days and 0 days (above 26 for a full 15 days). For example, if the alarm threshold was 16, then although the no-re-readmission group 102 was in an alarm state greater than the threshold for less than 14 days before the heart failure event 103, the yes-readmission group 101 was in an alarm state for more than 30 days before the heart failure event 103. HeartLogic is shown in Figure 1. TM The index value can often be used as a predictor of readmission, even before the first heart failure event 103. The inventors have particularly recognized that a persistently high alert or being in an alert period (e.g., having a HeartLogic index value greater than or equal to 16 or one or more other thresholds indicating a high index value or exceeding an alert level) for 30 days prior to a heart failure event or hospitalization is a significant predictor of readmission. TM The value is a strong predictor of the risk of readmission after discharge or treatment following a heart failure event or hospitalization. Information during and after treatment can confirm or further specify the likelihood of readmission, but the prediction can be determined before the first heart failure event.

[0052] Furthermore, the relative reduction during treatment or hospitalization (day 0) was greater for the yes-re-re-admission group 101 (from 27 to 22, a reduction of 18.5%) than for the no-re-admission group 102 (from 21 to 18, a reduction of 14%). The yes-re-re-admission group 101 had approximately the same number of patients admitted to HeartLogic as the no-re-admission group 102. TM The patient was discharged with a low index value. After discharge, the patient was in the HeartLogic re-admission group (group 101). TM The index value did not decline as rapidly or steadily as in the no-re-admission group 102, and rose further between day 20 and day 30 after discharge, compared with the no-re-admission group 102.

[0053] Figure 2 illustrates example relationships 200 of S1 heart sound values ​​for multiple patients (including yes-re-re-admission group 201 and no-re-admission group 202) in the pre-event period, 30 days before and until the heart failure event 203 and treatment or hospitalization (represented by day 0), and the post-discharge period, 30 days after the discharge or treatment associated with the heart failure event 203.

[0054] As shown in Figure 2, the yes-re-re-admission group 201 started with a substantially lower S1 value at day -30 (approximately 2 vs. 2.4) than the no-re-admission group 202, and was more variable between day -30 and day 0, although it had a substantially uniform slope between day -30 and day 0 compared to the overall decline slope of the no-re-admission group 202. Additionally, the relative increase in S1 value during treatment or hospitalization (day 0) was greater for the yes-re-admission group 201 (rising to 2.3, a 15% increase) than for the no-re-admission group 202 (rising to 2.5, an 8.7% increase). In particular, S1 heart sound values ​​can be used to stratify different re-admission groups and no-re-admission groups 201 and 202, such as by comparing S1 values ​​at heart failure event 203 or during the 30 days preceding heart failure event 203 with thresholds (such as 2.2).

[0055] With HeartLogic in Figure 1 TM The index values ​​were the same, with the yes-re-re-admission group 201 discharged at approximately the same S1 value as the no-re-re-admission group 202 upon admission. After discharge, the S1 value of the yes-re-re-admission group 201 was generally more variable and generally increased slightly (compared to the relatively flat no-re-re-admission group 202).

[0056] Figure 3 illustrates example relationships 300 of S3 heart sound values ​​for multiple patients (including yes-re-re-admission group 301 and no-re-admission group 302) during the pre-event period, 30 days before and up to the heart failure event 303 and the treatment or hospitalization (represented by day 0), and the post-discharge period, 30 days after the discharge or treatment associated with the heart failure event 303.

[0057] As shown in Figure 3, the -re-re-admission group 301 started with a substantially higher S3 value at -30 days (approximately 1.45 vs. 1.25) compared to the non-re-re-admission group 302, and was much more variable between -30 days and 0 days. This is consistent with the HeartLogic in Figure 1. TMThe index value is the same as the S1 value in Figure 2, and the S3 value of the "is-re-admission" group 301 was discharged at approximately the same level as the S3 value of the "not-re-admission" group 302 upon admission. After discharge, the S3 value of the "is-re-admission" group 301 is generally more variable and unstable and higher than that of the relatively stable "not-re-admission" group 302. The S3 value can be used to stratify and predict different re-admission groups and "not-re-admission" groups 301 and 302, for example, by setting threshold values ​​before and after discharge, especially due to the separation of the S3 value between day -25 and day -5, and between day 0 and day 30 (post-discharge).

[0058] Figure 4 illustrates an example relationship 400 for the S3 / S1 heart sound values ​​(S3 values ​​normalized by S1 values) of multiple patients (including yes-re-re-admission group 401 and no-re-admission group 402) in the pre-event period, 30 days before and until the heart failure event 403 and treatment or hospitalization (represented by day 0), and the post-discharge period, 30 days after the discharge or treatment associated with the heart failure event 403.

[0059] As shown in Figure 4, the -re-re-admission group 401 started with a substantially higher S3 / S1 value at -30 days (approximately 0.75 vs. 0.6) than the non-re-re-admission group 402, and was more variable between -30 days and 0 days, generally increasing (compared to the non-re-re-admission group 402), while the non-re-re-admission group 402 was generally stable and did not increase until -10 days. (This is consistent with the HeartLogic in Figure 1.) TM The index values, the S1 value in Figure 2, and the S3 value in Figure 3 are the same, indicating that the S3 / S1 ratio of the "yes-re-admission" group 401 was discharged at approximately the same S3 / S1 ratio as the "no-re-admission" group 402 was admitted. After discharge, the S3 / S1 ratio of the "yes-re-admission" group 401 is generally more variable and unstable, and higher, than that of the relatively stable "no-re-admission" group 402. The S3 / S1 ratio can be used for stratification and prediction between different re-admission groups and "no-re-admission" groups 401 and 402, for example, by setting threshold values ​​before and after discharge (e.g., 0.7 before and 0.65 after, etc.), especially due to the separation of the S3 / S1 ratio throughout different time periods.

[0060] Figure 5 illustrates the integrated HeartLogic data for multiple patients (including yes-read group 501 and no-read group 502) in the pre-event period, 30 days before and up to the heart failure event 503 and treatment or hospitalization (indicated by day 0), and the post-discharge period, 30 days after the discharge or treatment associated with the heart failure event 503. TM Example relationship of index value 500. Daily integration of HeartLogic. TM The index value represents HeartLogic's performance over the previous 30 days of consolidation.TM Index value (e.g., daily value, one per day).

[0061] As shown in Figure 5, the re-admission group 501 had a higher integration HeartLogic at -30 days compared to the non-re-admission group 402. TM The index value started (approximately 460 vs. 375) and increased at a substantially higher rate between day -20 and day 0 (approximately 50% vs. 33%). At day 0, there was an integrated HeartLogic value between the readmission group and the non-readmission groups 501 and 502. TM Substantial separation of index values ​​(700 vs. 500). Compared to the values ​​in Figures 1 through 4, the readmission group 501 did not show the same level of integration HeartLogic at approximately no-ready admission as the readmission group 502. TM Discharge index value. Both values ​​increased during treatment or hospitalization on day 0, with the increase in the readmission group 501 being only [increased / decreased]. After discharge, the integrated HeartLogic [value] in the readmission group 501 [increased / decreased]. TM The index values ​​were generally more variable and unstable than those in the relatively decreased non-re-ready group (502), and were significantly higher, with a clear separation between the two groups. Therefore, the integrated HeartLogic... TM Index values ​​can be used to stratify and predict different readmission groups and non-readmission groups 501, 502, for example, by setting threshold values ​​before and after discharge (such as 600), especially due to the separation of these values ​​throughout different time periods, the increase in differences before day 0, etc.

[0062] Figure 6 shows the patient's HeartLogic. TM The index value 601 and the example relationship 600 between different baselines (including existing previous 30-day baselines 602, shorter 5-day baselines 603, and pre-alarm baselines 604, such as those from the 30 days prior to the heart failure event on day 0 605). Compared to Figures 1 through 5, Figure 6 does not show the collapsed treatment or hospitalization periods, and during treatment (such as hospitalization), elevated HeartLogic is shown. TM Index value 601, during treatment, for example, one or more drugs, such as diuretics, may be used to treat heart failure event 605.

[0063] In some examples, the baseline HeartLogic TM The index value may be influenced by HeartLogic prior to and leading to heart failure events. TM Index value contamination. Changing the alarm window can improve alarm / hospitalization HeartLogic. TM Exponential value sensitivity. For example, the existing 30-day baseline HeartLogic. TMThe index value can be reduced to a shorter time period, such as the TX baseline HeartLogic. TM An index value (e.g., a short-term baseline value) where X is substantially less than 30 (e.g., 5 days, etc.) is used to increase sensitivity to changes after discharge or alert. In other examples, the existing 30-day baseline HeartLogic... TM The index value can be maintained up to the value prior to the detected alarm state, or the time interval prior to the detected heart failure event, such as the TY alarm pre-alarm baseline HeartLogic. TM The index value, where “Y” is the baseline 30 days prior to the detection of the event, is maintained at that value until a new baseline approaches the pre-alarm baseline of the detected event, to reflect the patient’s current status compared to their pre-hospitalization status.

[0064] Different baselines, such as the TX or TY pre-baseline described herein, can effectively increase the starting and resetting thresholds used for alarm state determination by reducing the impact of improved patient condition during hospitalization, as shown in Figures 1 through 5, effectively improving the determination of the current patient state or condition relative to the determined risk of readmission or future heart failure events. In other examples, the alarm threshold itself may be adjusted in response to a detected or received indication of a heart failure event or associated hospitalization or treatment, or the reset threshold may be adjusted once an alarm state is reached, such as taking into account short-term improvements in the patient's condition, e.g., until the previous baseline is reached.

[0065] Figure 7 illustrates an example method 700, which includes selecting or determining a new patient baseline after a heart failure event to determine HeartLogic. TM An index value or one or more other composite values ​​(e.g., a readmission score). In the examples, the patient's physiological information may be received, such as through signal receiver circuitry or one or more other circuits or components. The physiological information may be analyzed, such as through assessment circuitry of a mobile medical device or one or more other circuits or components (such as external systems). In some examples, physiological information generated over one or more specific time periods, such as relative to an alarm state, a previous heart failure event, or one or more other triggers or events, may be analyzed, such as with respect to the patient's baseline values, one or more thresholds, etc. In the examples, analyzing physiological information may include measuring one or more parameters or values ​​of the physiological information or one or more parameters or values ​​associated with the physiological information, or monitoring values ​​or information that change over time, or otherwise determining one or more other composite measurements, scores, or indications.

[0066] At step 701, the alarm state, such as being in an alarm state or above an alarm threshold in HeartLogic. TMThe index value can be detected, such as using evaluation circuitry or one or more other circuits or components, as described herein. In other examples, the alarm status can be received, such as through signal receiver circuitry or one or more other circuits.

[0067] At step 702, an event, such as a heart failure event, hospitalization, or treatment, can be detected using one or more physiological sensors, such as detecting information indicating treatment or treatment location, or receiving information such as from a clinician, hospital, or one or more systems associated with the patient, indicating treatment or hospitalization, including admission, detected location, or proximity of one or more specific external system components (such as an external programmer).

[0068] At step 703, a new baseline may be triggered, such as by a clinician or one or more other users. In some examples, the new baseline may be triggered separately from, or in response to, one or more alarm states or detected events detected at steps 701, 702, etc.

[0069] At step 704, a new baseline window can be selected, such as one or more of a new baseline triggered in response to a detected alarm state, a detected event, or a new baseline in steps 701, 702, and 703. Step 704 may optionally include one or both of the TX baseline at step 705 or the TY pre-alarm (TY PRE) baseline at step 706. The TX baseline may include a relatively short baseline, where X equals 5 days or one or more shorter intervals, less than the existing 30-day baseline (e.g., 3 days, 5 days, 7 days, 10 days, etc.). The TY pre-alarm baseline may include a pre-alarm baseline, where Y is the number of days prior to the heart failure event, such as 30 days. The TY pre-alarm baseline may remain at that past baseline value for a period of time (e.g., 30 days) after treatment or discharge associated with the heart failure event. In contrast, the TX baseline may be a rolling baseline, indicating short-term changes in the patient (e.g., improvement, stabilization, deterioration, etc.). At step 707, a deterioration index may be determined using daily values ​​or combinations of daily values ​​relative to the TX baseline, indicating the patient's short-term status (e.g., improvement or deterioration). At step 708, the relative pre-event status can be determined using a daily value or a combination of daily values ​​relative to the TY alarm pre-baseline, indicating the patient's relative status compared to the patient's heart failure pre-event baseline.

[0070] In some examples, at step 704, the new baseline window can be determined as a function of one or both of the TX baseline determined at step 705, the TY pre-alarm baseline determined at step 706, or a combination thereof, and in some examples, additionally as a function of the existing 30-day baseline described elsewhere herein. For example, as time moves away from discharge, or as on day 0 as described herein, the transition period can blend differences between different baselines.

[0071] At step 709, the patient's status can be determined, such as a readmission score or one or more other determinations of readmission risk, such as a deterioration indicator determined at step 707, a relative pre-event status determined at step 708, or a combination thereof. In this example, the function may additionally include differences between the different baselines described herein, taking into account relative increases or decreases in baseline values ​​in addition to daily values ​​or combinations of daily values. For example, the function may include a score or indication of relative changes in patient status (e.g., improvement or deterioration) relative to one or more other determined weighted combinations to provide a score or indication of relative changes in patient status (e.g., improvement or deterioration), determination of a new or adjusted baseline, and an indication of a heart failure readmission metric or a determined readmission probability (e.g., an indication of the likelihood that the patient will experience readmission) following discharge or treatment associated with a heart failure event.

[0072] Systolic heart rate and the third heart sound (S1 and S3, respectively) indicate long-term stratification for readmission. Low systolic heart rate and high third heart sound are strong risk factors. A determined patient status prior to a heart failure event can indicate to the user or clinician that the patient may be at higher risk of readmission for a heart failure event or subsequent heart failure event and should be monitored more closely, such as for alternative therapies. For example, a readmission score can be determined during treatment and before discharge and provided or reported to the clinician, indicating the likelihood of readmission. Readmission scores can be determined, for example, using assessment circuits such as S3 / S1, HeartLogic, etc. TM Index values ​​and integrated HeartLogic TM A composite of index values. For example, a readmission score can be determined from HeartLogic scores prior to hospitalization or alert. TM Index values, S3 / S1 from before hospitalization or alert, and integrated HeartLogic after discharge. TM A composite function of exponential values ​​(e.g., the integrated value of the first 30 days, as described and shown in Figure 5, etc.).

[0073] For example, the probability of readmission, when compared between being on alert and off alert, can include the following values: (1) If on alert 14 days before ex-admission: the probability of readmission due to heart failure within 30 days is 3.05 [1.02 (5%), 9.10 (95%)] times higher; the probability of readmission due to all causes within 30 days is 2.25 [0.84, 6.03] times higher; (2) If on alert 7 days after discharge: the probability of readmission due to heart failure within 90 days is 3.01 [1.29, 7.01] times higher; (3) If on alert 14 days after discharge: the probability of readmission due to heart failure within 90 days is 2.67 [1.16, 6.14] times higher.

[0074] At step 710, the determination, indication, score, or physiological information from the associated monitoring window may be stored, for example, using evaluation circuitry, and transmitted via the control of the evaluation circuitry or using one or more communication circuits, such as to one or more additional processes or components, such as output circuitry (e.g., a display, a controller for the display, etc.). At step 711, an alarm may optionally be provided, such as via the evaluation circuitry, for example, if the determination, indication, score, or one or more baselines are available for review or transmission, or if one or more of the determination, indication, score, or one or more baselines exceed a threshold, etc. In the example, the output of the determination, indication, score, or one or more baselines may be provided to a user interface for display to a user, or to another circuitry to control or adjust the process or function of the implantable or mobile medical device.

[0075] At step 712, the evaluation circuitry or one or more modes or functions of the implantable or mobile medical device may be optionally adjusted based on one or more of a determination, indication, score, or one or more baselines. For example, if the determination, indication, score, or one or more of the baselines exceeds one or more thresholds or values, one or more hardware limitations may be adjusted, such as to: record more or less patient physiological information; increase the frequency of communication between the implantable or mobile medical device and external devices (e.g., remote devices, programmers, etc.), such as to increase the frequency of patient monitoring; switch to different or more power- or resource-intensive monitoring algorithms; etc.

[0076] At step 713, one or more therapies may be optionally provided or adjusted based on determination, instruction, scoring, or one or more baselines, as described herein.

[0077] Implantable or mobile medical devices may include one or more electrodes or sensors located within, on, or near the heart (such as those coupled to leads and located in one or more chambers of the heart, or within the vascular system of the heart near one or more chambers), or configured to receive cardiac electrical information therefrom. Implantable or mobile medical devices may additionally include one or more accelerometer sensors or be configured to receive mechanical acceleration information from one or more accelerometer sensors to determine and monitor patient acceleration information, such as cardiac acceleration or vibration information associated with blood flow or movement in the heart or the patient's vascular system (e.g., heart sounds, heart wall motion, etc.), patient body activity or location information (e.g., patient posture, activity, number of steps, etc.), respiratory information (e.g., respiratory rate, volume, phase, breath sounds, etc.), impedance information, plethysmography information, chemical information, temperature information, or other physiological information of the patient.

[0078] To balance the power and resource usage of physiological monitoring and mobile medical devices, physiological information can be detected within one or more detection windows, typically between 30 seconds and 2 minutes, although in some examples longer or shorter, such as between 15 seconds and 5 minutes, occurring multiple times a day (e.g., every hour, every 2 hours, daily, etc.). The mobile medical device can determine information about each detection window or one or more detection windows, such as summary information or information representing the respective detection windows, daily information representing information about a specific day of the patient, etc., and can, based on this determination, additionally determine information to store or transmit, such as for transmission to remote devices. In some examples, the mobile medical device can aggregate information from multiple sensors, using information from each sensor individually or in combination to detect various events, update the detection status based on this information, and send messages or alerts to one or more remote devices indicating that a detection has been performed and information has been stored or transmitted, allowing one or more additional processes or systems to use the stored or transmitted detection or information for one or more other reviews or processes.

[0079] Mobile medical devices, such as short-term or long-term insertable cardiac monitors (ICMs), measure physiological information over a period of time and can determine the condition of one or more patients, including detected episodes of patient deterioration, status, baseline, etc. Physiological information associated with a specific detection window can be recorded, stored, and made available to clinicians. The amount of detection windows and associated physiological information recorded, transmitted, and reviewed by clinicians is typically only a subset of the total atrial fibrillation burden reported to or available for review (e.g., stored but not transmitted, transmitted but not reviewed, etc.). In many instances, the reported burden is greater than the reviewable burden (e.g., not recorded, stored, or transmitted, etc.).

[0080] Some mobile medical devices have limitations, such as those based on hardware limitations (e.g., power, processing resources, circuit components, etc.), the number of episodes or events that can be recorded over a period of time (e.g., 5 detected episodes per day, etc.), the amount of physiological information that can be recorded in a single episode or event (e.g., 6 minutes per episode at a specific sampling frequency, etc.), or the amount of physiological information that can be transmitted over a period of time (e.g., 30 minutes per day at a specific sampling frequency, etc.). Recorded and transmitted episodes can be reviewed and adjudicated (e.g., by a clinician, using one or more additional procedures, or a combination thereof). However, incomplete physiological information recorded and transmitted by such limited mobile medical devices (e.g., ECG information from a portion of an identified episode, but not representative of every detection window of the identified episode unless such detection window is identified as an atrial fibrillation episode) may lead to a greater number of false positives than actually occurred. Furthermore, devices and algorithms can learn from the decisions, allowing false positive decisions to be fed back directly to mobile medical devices, saving processing, storage, and transmission resources. This further reduces the likelihood that false positives will consume any limited resources or reduce operational resources, thus extending the lifespan or service life of mobile medical devices.

[0081] Figure 8 illustrates an example system 800 (e.g., a medical device system). In this example, one or more aspects of the example system 800 may be components of or communicatively coupled to a medical device, such as an implantable medical device (IMD), an insertable cardiac monitor (ICM), a mobile medical device (AMD), etc. The system 800 may be configured to monitor, detect, or treat various physiological conditions of the body, such as cardiac conditions associated with a reduced ability of the heart to adequately deliver blood to the body, including heart failure, arrhythmias, asynchrony, etc., or one or more other physiological conditions, and in some examples, may be configured to provide electrical stimulation or one or more other therapies or treatments to the patient.

[0082] System 800 may include a single or multiple medical devices implanted in or otherwise positioned on or around a patient to monitor the patient’s physiological information using information from one or more sensors, such as sensor 801. In the example, sensor 801 may include one or more of the following: a respiratory sensor configured to receive respiratory information (e.g., respiratory rate, respiratory volume (tidal volume), etc.); an acceleration sensor (e.g., accelerometer, microphone, etc.) configured to receive cardiac acceleration information (e.g., cardiac vibration information, pressure waveform information, heart sound information, endocardial acceleration information, acceleration information, activity information, posture information, etc.); an impedance sensor (e.g., intrathoracic impedance sensor, transthoracic impedance sensor, pleural impedance sensor, etc.) configured to receive impedance information; a cardiac sensor configured to receive cardiac electrical information; an activity sensor configured to receive information about body movement (e.g., activity, steps, etc.); a posture sensor configured to receive posture or position information; a pressure sensor configured to receive pressure information; a volumetric sensor (e.g., photoplethysmography sensor, etc.); a chemical sensor (e.g., electrolyte sensor, pH sensor, anion gap sensor, etc.); a temperature sensor; a skin elasticity sensor; or one or more other sensors configured to receive physiological information of a patient.

[0083] Example system 800 may include signal receiver circuitry 802 and evaluation circuitry 803. Signal receiver circuitry 802 may be configured to receive physiological information from sensor 801 about a patient (or patient group). Evaluation circuitry 803 may be configured to receive information from signal receiver circuitry 802 and use the received physiological information (such as that described herein) to determine one or more parameters (e.g., physiological parameters, stratification, etc.) or existing or altered patient conditions (e.g., indications of patient dehydration, respiratory status, cardiac status (e.g., heart failure, arrhythmia), sleep apnea, etc.). Physiological information may include, among other things, cardiac electrical information, impedance information, respiratory information, heart sound information, activity information, posture information, temperature information, or one or more other types of physiological information.

[0084] In some examples, evaluation circuit 803 can aggregate information from multiple sensors or devices, use information from each sensor or device individually or in combination to detect various events, update the detection status of one or more patients based on the information, and transmit messages or alarms to one or more remote devices that one or more patients have been detected, or that information has been stored or transmitted, so that one or more other processes or systems can use the stored or transmitted detection or information for one or more other reviews or processes.

[0085] In some examples, such as to detect improvement or deterioration in a patient's condition, some initial assessment is often required to establish a baseline level or condition from one or more sensors or physiological information. Subsequent detections of deviations from the baseline level or condition can be used to determine whether the patient's condition has improved or deteriorated. However, in other examples, the amount of change or alteration (e.g., relative or absolute change) in physiological information over different time periods can be used to determine the risk of adverse medical events, or to predict or stratify the risk of a patient experiencing an adverse medical event (e.g., heart failure) some time after the change is detected, in conjunction with or separately from any baseline level or condition.

[0086] Changes in different physiological information can be aggregated and weighted based on one or more patient-specific stratifiers and, in some examples, compared to one or more thresholds, such as those that are clinically sensitive and specific in the target population relative to a specific disease (e.g., heart failure), and one or more specific time periods, such as daily values, short-term averages (e.g., daily values ​​aggregated over several days), long-term averages (e.g., daily values ​​aggregated over multiple short-term periods or more than one day (sometimes different from the number of days used for short-term averages, e.g., non-overlapping)).

[0087] System 800 may include output circuitry 804 configured to provide output to a user, or to make output available to a user via an output, display, or one or more other user interfaces, including ratings, trends, alarms, or other indications. In other examples, output circuitry 804 may be configured to provide output to another circuit, machine, or process (such as treatment circuitry 805, e.g., cardiac resynchronization therapy (CRT) circuitry, chemotherapy circuitry, stimulation circuitry, etc.)) to control, adjust, or stop treatment by a medical device, drug delivery system, etc., or otherwise alter one or more processes or functions of one or more other aspects of the medical device system, such as one or more CRT parameters, drug delivery, dosage determination, or recommendations. In examples, treatment circuitry 805 may include one or more of stimulation control circuitry, cardiac stimulation circuitry, neural stimulation circuitry, dosage determination, or control circuitry. In other examples, treatment circuitry 805 may be controlled by evaluation circuitry 803 or one or more other circuitry. In some examples, evaluation circuit 803 may include output circuit 804, or may be configured to determine the output to be provided by output circuit 804, and output circuit 804 may provide a signal that causes the user interface to provide output to the user based on the output determined by evaluation circuit 803.

[0088] Technical challenges exist in medical devices and systems where, in low-power monitoring modes, mobile medical devices (e.g., including IMDs) powered by one or more rechargeable or non-rechargeable batteries must make trade-offs between battery life, or, in instances of implantable medical devices with non-rechargeable batteries, between device replacement periods (typically including surgical procedures and sampling resolution), the processing, storage, and transmission of sensed physiological information, or the sampling period for features or modes selected within the medical device. Medical devices may include higher-power and lower-power modes. Physiological information, such as indications of potential adverse physiological events, can be used to transition from a low-power mode to a high-power mode. In some examples, a low-power mode may include a low-resource mode characterized by requiring less power, processing time, memory, or communication time or bandwidth (e.g., transmitting less data) than a corresponding high-power mode. A high-power mode may include a relatively high-resource mode characterized by requiring more power, processing time, memory, or communication time or bandwidth than a corresponding low-power mode. However, by the time the physiological information detected in the low-power mode indicates a possible event, valuable information has been lost and cannot be recorded in the high-power mode.

[0089] The opposite is also true, as erroneous or inaccurate determinations that trigger high-power modes unnecessarily and excessively limit the usable lifespan of some mobile medical devices. For many reasons, accurate detection and determination of physiological events, and avoiding unnecessary transitions from low-power to high-power modes, are beneficial for improving the utilization of medical device resources.

[0090] For example, a change in mode can enable higher resolution sampling or sampling frequency, or an increase in the number or type of sensors used to sense physiological information, up to and including potential events. For instance, different physiological information is often sensed using non-overlapping time periods of the same sensor, in some examples, at different sampling frequencies and power costs. In one example, heart sounds and patient activity can be detected using the same non-overlapping time periods of a single-axis or multi-axis accelerometer, at different sampling frequencies and power costs. In some examples, a shift to a high-power mode may include detecting heart sounds using an accelerometer throughout the high-power mode or at a larger percentage of the high-power mode than the corresponding low-power mode. In other examples, waveforms of medical events can be recorded, stored in long-term memory, and transmitted to remote devices for review by clinicians. In some examples, only a notification that an event has been stored, or a summary of the event, is transmitted. In response, the full event can be requested for subsequent transmission and review. However, even when events are stored but not transmitted, the resources for storing and processing the events still reside with the medical device.

[0091] Figure 9 illustrates an example patient management system 900 and a portion of the environment in which the patient management system 900 may operate. The patient management system 900 can perform a range of activities, including remote patient monitoring and diagnosis of disease conditions, programming mobile medical devices, and controlling one or more therapies. These activities can be performed near the patient 901 (such as in the patient's home or office), via a centralized server (such as in a hospital, clinic, or doctor's office), or via a remote workstation (such as a secure wireless mobile computing device).

[0092] The patient management system 900 may include one or more medical devices, an external system 905, and a communication link 911, which provides communication between the one or more mobile medical devices and the external system 905. The one or more medical devices may include mobile medical devices (AMDs), such as implantable medical devices (IMDs) 902, wearable medical devices 903, or one or more other implantable, leadless, subcutaneous, external, wearable, or medical devices configured to monitor, sense, or detect information from the patient 901, determine physiological information about the patient 901, or provide one or more therapies to treat various conditions of the patient 901, such as one or more cardiac or non-cardiac conditions (e.g., dehydration, sleep apnea, etc.).

[0093] In one example, implantable medical device 902 may include one or more cardiac rhythm management devices implanted in a patient's chest, the device having a lead system including one or more transvenous, subcutaneous, or non-invasive leads or catheters to position one or more electrodes or other sensors (e.g., heart sound sensors) in, on, or around the heart or in one or more other locations in the chest, abdomen, or neck of patient 901. In another example, implantable medical device 902 may include, for example, a monitor subcutaneously implanted in the chest of patient 901, implantable medical device 902 including a housing containing circuitry, and in some examples, one or more sensors, such as temperature sensors.

[0094] Cardiac rhythm management devices are typically configured to receive cardiac electrical information from one or more electrodes located inside, on, or near the heart, and in some examples to provide electrical stimulation thereto. These electrodes may be coupled to one or more leads and located in one or more chambers of the heart, within the vascular system of the heart near one or more chambers, or otherwise attached to, in contact with, or near the heart. Cardiac rhythm management devices may, among other things, include pacemakers, implantable cardioverter defibrillators (ICDs), subcutaneous implantable cardioverter defibrillators, cardiac resynchronization therapy defibrillators (CRT-Ds), insertable cardiac monitors, leadless cardiac pacemakers (LCPs), or wearable or remote monitoring systems.

[0095] Cardiac resynchronization therapy (CRT) generally refers to a stimulatory therapy that generates and delivers stimulation to one or more chambers of the heart (e.g., often two or more of the right ventricle (RV), left ventricle (e.g., usually via the cardiac vascular system), or right atrium (RA)) to improve cardiac function, such as improving contractile coordination between different chambers of the heart (e.g., right and left ventricles, right atrium and right ventricle, etc.) or otherwise improving cardiac output or efficiency. Cardiac resynchronization therapy can include biventricular pacing (e.g., both right and left ventricular pacing), single-chamber pacing (e.g., right ventricular pacing, left ventricular pacing, etc.), sensing or pacing in one or more other chambers or combinations of chambers (e.g., right atrium, etc.), and multi-site pacing (MSP) (e.g., applying one or more stimulation signals to multiple (e.g., two or more) electrodes in or near a chamber (e.g., typically the left ventricle, but in some examples the right ventricle, right atrium, or a combination thereof) within a single cardiac cycle), and in some examples, HIS bundle pacing, interval pacing, etc. The timing of the stimulation signal during or relative to one or more cardiac events often varies depending on many factors, including lead or electrode placement, propagation of the stimulation signal through tissue, and stimulation parameters such as stimulation amplitude, type, timing, etc.

[0096] Therefore, a cardiac rhythm management device may include an aspect located subcutaneously but close to the patient's distal skin, and an aspect located near one or more of the patient's organs, such as leads or electrodes. Separate from or other than the leads and the one or more electrodes or other sensors, the cardiac rhythm management device may include one or more electrodes or other sensors (e.g., pressure sensors, accelerometers, gyroscopes, microphones, etc.) powered by a power source within the cardiac rhythm management device. The leads, the cardiac rhythm management device, or a combination thereof, and the one or more electrodes or other sensors may be configured to detect physiological information from the patient or to provide the patient with one or more therapies or stimuli.

[0097] Implantable devices may additionally or separately include leadless pacemakers (LCPs), small (e.g., smaller than conventional implantable rhythm management devices, having a volume of about 1 cc in some examples), self-contained devices comprising one or more sensors, circuitry, or electrodes configured to monitor physiological information from the heart (e.g., heart rate, etc.), detect physiological conditions associated with the heart (e.g., tachycardia), or deliver one or more therapies or stimuli to the heart without the complications of conventional leaded or implantable rhythm management devices (e.g., required incisions and pockets, complications associated with lead placement, breakage, or displacement, etc.). In some examples, leadless pacemakers may have more limited power and processing capabilities than conventional rhythm management devices; however, multiple leadless pacemaker devices may be implanted in or around the heart to detect physiological information from one or more chambers of the heart, or to deliver one or more therapies or stimuli to one or more chambers of the heart. Multiple leadless pacemaker devices may communicate between themselves or one or more other implantable or external devices.

[0098] Implantable medical device 902 may include signal receiver circuitry or evaluation circuitry configured to detect or determine specific physiological information of patient 901, or to determine one or more conditions, or to provide information or alerts to users such as patient 901 (e.g., a patient), a clinician, or one or more other caregivers or processes, as described herein. Implantable medical device 902 may alternatively or additionally be configured as a therapeutic device configured to treat one or more medical conditions of patient 901. Therapies may be delivered to patient 901 via a lead system and associated electrodes or using one or more other delivery mechanisms. Therapies may include delivering one or more medications to patient 901, such as using one or more of the implantable medical device 902 or other mobile medical devices. In some examples, the therapy may include cardiac resynchronization therapy to correct asynchrony in patients with heart failure and improve cardiac function in patients with heart failure. In other examples, implantable medical device 902 may include a medication delivery system, such as a medication infusion pump, to deliver medication to a patient for managing arrhythmias or complications arising from arrhythmias, hypertension, hypotension, or one or more other physiological conditions. In other examples, the implantable medical device 902 may include one or more electrodes configured to stimulate the patient’s nervous system or to provide stimulation to the muscles of the patient’s airway, etc.

[0099] Wearable medical device 903 may include one or more wearable or external medical sensors or devices (e.g., automatic external defibrillator (AED), Holter monitor, patch-based device, smartwatch, smart accessory, wrist or finger-worn medical device, such as finger-based photoplethysmography sensor, etc.).

[0100] External system 905 may include dedicated hardware / software systems, such as a programmer, a remote server-based patient management system, or alternatively, a software-defined system primarily running on a standard personal computer. External system 905 may manage patient 901 via implantable medical device 902 or one or more other mobile medical devices connected to external system 905 via communication link 911. In other examples, implantable medical device 902 may be connected to wearable medical device 903, or wearable medical device 903 may be connected to external system 905 via communication link 911. This may include, for example, programming or reprogramming implantable medical device 902 with different parameter settings to perform one or more of the following: acquiring physiological data, performing at least one self-diagnostic test (such as for device operating status), analyzing physiological data, or optionally delivering or adjusting therapy for patient 901. Additionally, external system 905 may send or receive information from implantable medical device 902 or wearable medical device 903 via communication link 911. Examples of information may include real-time or stored physiological data from patient 901, diagnostic data such as detection of patient hydration status, hospitalization, response to therapy delivered to patient 901, or device operating status (e.g., battery status, lead impedance, etc.) of implantable medical device 902 or wearable medical device 903. Communication link 911 may be an inductive telemetry link, a capacitive telemetry link, or a radio-frequency (RF) telemetry link, such as based on, for example, "strong" Bluetooth. Alternatively, wireless telemetry using the IEEE 802.11 Wireless Fidelity "Wi-Fi" interface standard. Other configurations and combinations of the patient data source interface are possible.

[0101] External system 905 may include external device 906 located near one or more mobile medical devices, and remote device 908 located relatively far from the one or more mobile medical devices, communicating with external device 906 via communication network 907. Examples of external device 906 may include a medical device programmer. Remote device 908 may be configured to evaluate collected patient or patient information and provide alarm notifications, among other possible functions. In an example, remote device 908 may include a centralized server acting as a central hub for storing and analyzing collected data from multiple different sources. The combination of information from multiple sources may be used to make determinations and update individual patient statuses, or to adjust one or more alarms or determinations for one or more other patients. The server may be configured as a single, multiple, or distributed computing and processing system. Remote device 908 may receive data from multiple patients. Data may be collected by one or more mobile medical devices, in addition to other data acquisition sensors or devices associated with patient 901. The server may include storage devices to store data in a patient database. The server may include alarm analyzer circuitry to evaluate the collected data to determine whether specific alarm conditions are met. The fulfillment of alarm conditions can trigger the generation of alarm notifications, such as those provided by one or more human-perceptible user interfaces. In some examples, alarm conditions may be evaluated alternatively or additionally by one or more mobile medical devices, such as implantable medical devices. For example, alarm notifications may include webpage updates, telephone or pager calls, emails, SMS messages, text or "instant" messages, as well as messages to patients and simultaneous direct notifications to emergency services and clinicians. Other alarm notifications are possible. The server may include alarm priority sorting circuitry configured to prioritize alarm notifications. For example, alarms for detected medical events may be prioritized using a similarity metric between physiological data associated with a detected medical event and physiological data associated with historical alarms.

[0102] In the example, similar to the alarm notifications discussed above, external system 905 or one or more of its components (e.g., external device 906, remote device 908, assessment circuitry, etc.) can be configured to schedule one or more follow-up appointments for the patient or adjust the schedule of one or more follow-up appointments, such as in response to one or more alarm notifications or other determinations, or at the request of a clinician.

[0103] Remote device 908 may additionally include one or more locally configured clients or remote clients securely connected to the server via communication network 907. Examples of clients may include personal desktop computers, laptops, mobile devices, or other computing devices. System users, such as clinicians or other qualified medical professionals, can use the clients to securely access stored patient data aggregated in a database on the server and select and prioritize patients and alerts for healthcare provisioning. In addition to generating alert notifications, remote device 908, including the server and interconnected clients, can also execute follow-up protocols by sending follow-up requests to one or more mobile health devices, or by sending messages or other communications as compliance notifications to patients 901 (e.g., patients), clinicians, or authorized third parties.

[0104] Communication network 907 can provide wired or wireless interconnection. In this example, communication network 907 may be based on the Transmission Control Protocol / Internet Protocol (TCP / IP) network communication specification, although other types or combinations of network implementations are possible. Similarly, other network topologies and arrangements are possible.

[0105] One or more of the external device 906 or remote device 908 may output detected medical events to system users, such as patients or clinicians, or to instances including, for example, computer programs executable in a microprocessor. In examples, this process may include automatically generating programmed recommendations for mobile medical devices to optimize or improve a patient's condition or otherwise provide desired clinical outcomes. In examples, external device 906 or remote device 908 may include corresponding display units for displaying physiological or functional signals, or alarms, alerts, emergency calls, or other forms of warning to signal the detection of one or more conditions. In some examples, external system 905 may include signal receiver circuitry and evaluation circuitry, such as an external data processor, configured to analyze physiological or functional signals received by one or more mobile medical devices and to confirm or reject one or more determinations made by one or more mobile medical devices (such as implantable medical device 902, wearable medical device 903, etc.), or to make additional determinations, etc. Computationally intensive algorithms, such as machine learning algorithms, may be implemented in the external data processor.

[0106] In some examples, when one or more trained machine learning models are used to analyze the parameter settings of a mobile medical device and detect one or more differences between the mobile medical device's parameter settings and stored model parameter settings, recommendations for the reprogrammed medical device can be generated and presented to clinician 908 via the user interface of remote device 908 or via the user interface of a software application running on a client device communicatively connected to remote device 908. The recommendations for the reprogrammed medical device can be determined by identifying differences between the mobile medical device's parameter settings and stored model parameter settings via one or more machine learning models—differences that would otherwise be undetectable by clinician or medical device programmer.

[0107] One or more portions of a mobile medical device or external system 905 may be implemented using hardware, software, firmware, or a combination thereof. One or more portions of a mobile medical device or external system 905 may be implemented using dedicated circuitry, which may be constructed or configured to perform one or more functions, or may be implemented using general-purpose circuitry, which may be programmed or otherwise configured to perform one or more functions. Such general-purpose circuitry may include a microprocessor or a portion thereof, a microcontroller or a portion thereof, or programmable logic circuitry, memory circuitry, a network interface, and various components for interconnecting these components. For example, among others, a “comparator” may include an electronic circuit comparator that may be constructed to perform a specific function of comparing two signals, or a comparator may be implemented as part of a general-purpose circuitry that may be driven by code instructing a portion of the general-purpose circuitry to perform a comparison between two signals. A “sensor” may include electronic circuitry configured to receive information and provide an electronic output representing such received information.

[0108] Treatment device 910 can be configured to send or receive information from one or more mobile medical devices or external systems 905 using communication link 911. In the example, one or more mobile medical devices, external devices 906, or remote devices 908 can be configured to control one or more parameters of treatment device 910. External system 905 can allow programming or reprogramming of one or more mobile medical devices and can receive information about one or more signals acquired by one or more mobile medical devices, such as those received via communication link 911. External system 905 may include a local external implantable medical device programmer. External system 905 may include a remote patient management system that can monitor patient status or adjust one or more therapies, such as from a remote location.

[0109] In some examples, event storage can be triggered, such as by receiving physiological information, or in response to one or more detected events or determined parameters meeting or exceeding a threshold (e.g., a static threshold, a dynamic threshold, or one or more other thresholds based on patient or population information). Information sensed or recorded in high-power mode can be converted from short-term storage (such as in a cyclic recorder) to long-term or non-volatile memory, or in some examples, prepared for communication to an external device separate from the medical device. In examples, cardiac electrical or cardiac mechanical information up to and, in some examples, including detected events can be stored, such as to increase the specificity of the detection. In examples, multiple cyclic recorder windows (e.g., 2-minute windows, 4-minute windows, etc.) can be stored sequentially. In systems without early detection, recording this information would require a cyclic recorder with a long time period at a significant additional cost (e.g., power, processing resources, component costs, memory requirements, etc.). Storing multiple windows using early detection up to a single event provides a complete event assessment, saving power and cost compared to a long cyclic recorder window. Furthermore, early detection can trigger additional parameter calculations or storage at different resolutions or sampling frequencies without unduly burdening limited system resources.

[0110] In some examples, one or more alerts may be provided to the patient, clinician, or one or more other caregivers (e.g., using the patient's smartwatch, cellular or smartphone, computer, etc.). In some examples, this may be in response to a transition to a high-power mode, in response to a detected event or condition, or after information is updated or transmitted from a first device to a remote device. In other examples, the medical device itself may provide audible or tactile alerts to warn the patient of a detected condition. For example, the patient may be alerted in response to a detected condition so that they can take corrective actions, such as sitting down.

[0111] In some examples, therapy may be provided in response to a detected condition. For example, pacing therapy may be provided, activated, or adjusted, such as to interrupt or reduce the effects of a detected atrial fibrillation event. In other examples, the delivery of one or more medications (e.g., vasoconstrictors, vasopressors, etc.) may be triggered, provided, or adjusted, such as by using a drug pump, in response to a detected condition, alone or in combination with pacing therapy, such as as described above, for example to increase arterial pressure, maintain cardiac output, and interrupt or reduce the effects of a detected atrial fibrillation event, or a combination thereof.

[0112] In some examples, a patient's physiological information can be sensed, for example, by one or more sensors (such as a cardiac sensor, a heart sound sensor, or one or more other sensors described herein) located within, on, or near the patient. For instance, a cardiac sensor can be used to sense the patient's cardiac electrical information. In other examples, a heart sound sensor can be used to sense the patient's cardiac acceleration information. The cardiac sensor and heart sound sensor can be components of one or more (e.g., the same or different) medical devices (e.g., implantable medical devices, mobile medical devices, etc.). Timing measures between different features (e.g., first cardiac features and second cardiac features, etc.) can be determined, such as through processing circuitry of the cardiac sensor or one or more other medical devices or medical device components. In some examples, timing measures may include the interval or measure between a first cardiac feature and a second cardiac feature in a patient's first cardiac interval (e.g., the duration of a cardiac cycle or interval, QRS width, etc.), or the interval or measure between a first cardiac feature and a second cardiac feature in a patient's corresponding successive first cardiac interval and second cardiac interval. In the example, the first cardiac feature and the second cardiac feature include equivalent detection features in successive first cardiac intervals and second cardiac intervals, such as successive R waves (e.g., RR intervals) or one or more other features of cardiac electrical signals.

[0113] Heart sounds are recurring mechanical signals associated with the vibrations of the heart or accelerations from blood flow through the heart or other cardiac movements during each cardiac cycle or interval. They can be distinguished and classified according to the activities associated with these vibrations, accelerations, movements, pressure waves, or blood flow. Heart sounds comprise four main characteristics: the first to the fourth heart sounds (S1 to S4, respectively). The first heart sound (S1) is the vibration produced by the heart during the closure of the atrioventricular (AV), mitral, and tricuspid valves, and during the opening of the aortic valve at the onset of systole or ventricular systole. The second heart sound (S2) is the vibration produced by the heart during the closure of the aortic and pulmonary valves at the onset of diastole or ventricular diastole. The third and fourth heart sounds (S3, S4) are related to the filling pressure of the left ventricle during diastole. A sudden cessation of early diastolic filling can result in the third heart sound (S3). Vibrations caused by atrial kicks can result in the fourth heart sound (S4). The closure of valves in the heart and changes in blood flow and pressure can cause acceleration, vibration, or movement of the heart wall, which can be detected using an accelerometer or microphone, thus providing an output referred to in this paper as cardiac acceleration information.

[0114] In the examples, the values ​​of the respective heart sound signals of a heart sound signal segment or a cardiac interval can be detected as amplitudes relative to one or more cardiac electrical features, or as one or more energy values ​​relative to a window of the heart sound signal, typically determined relative to one or more cardiac electrical features. For example, the value and timing of the S1 signal can be detected using the amplitude or energy of the heart sound signal occurring at or near the R wave of the cardiac interval. The S4 signal segment can be determined, for example, by processing circuitry of a heart sound sensor or one or more other medical devices or medical device components. In some examples, the S4 signal segment may include a filtered signal from the S4 window of the cardiac interval. In the examples, the S4 interval can be defined as a set time period within the cardiac interval relative to one or more other cardiac electrical or mechanical features, such as forward from one or more features of the R wave, T wave, or one or more heart sound waveforms (such as the first heart sound, second heart sound, or third heart sound (S1, S2, S3)), or backward from the detected S1 of a subsequent R wave or subsequent cardiac interval. In some examples, the length of the S4 window may depend on heart rate or one or more other factors. In the example, the timing measure of cardiac electrical information can be the timing measure of the first cardiac interval, and the S4 signal portion can be the S4 signal portion of the same first cardiac interval.

[0115] In the example, heart sound parameters may include information or information about the same heart sound parameter or different combinations of heart sound parameters within one or more cardiac cycles or a specified time period (e.g., 1 minute, 1 hour, 1 day, 1 week, etc.). For example, heart sound parameters may include composite S1 parameters representing, for example, multiple S1 parameters within a certain time period (e.g., multiple cardiac cycles, representative time period, etc.).

[0116] In the examples, the heart sound parameter may include a ensemble average of a specific heart sound on a heart sound waveform, such as that disclosed in commonly assigned U.S. Patent No. 7,115,096, entitled "THIRD HEART SOUND ACTIVITY INDEX FOR HEART FAILURE MONITORING" by Siejko et al., or in commonly assigned U.S. Patent No. 7,853,327, entitled "HEARTSOUND TRACKING SYSTEM AND METHOD" by Patangay et al., each of which is incorporated herein by reference in its entirety, including their disclosures of ensemble averaging of acoustic signals and determination of a specific heart sound on a heart sound waveform. In other examples, the signal receiver circuitry may receive at least one heart sound parameter or composite parameter, such as from a heart sound sensor or heart sound sensor circuitry.

[0117] In the example, a patient's cardiac electrical information can be received from a cardiac sensor (e.g., one or more electrodes) or a cardiac sensor circuit (e.g., including one or more amplifier or filter circuits) using a signal receiver circuit, such as a medical device. In the example, the received cardiac electrical information may include a timing measurement between a first cardiac characteristic and a second cardiac characteristic of the patient.

[0118] In the examples, the patient's cardiac acceleration information can be received from a heart sound sensor (e.g., an accelerometer) or a heart sound sensor circuit (e.g., including one or more amplifier or filter circuits) using the same or different signal receiver circuitry of a medical device. In the examples, the received cardiac acceleration information may include an S4 signal portion appearing between the patient's first and second cardiac features. In some examples, additional physiological information, such as heart rate information, patient activity information, or patient posture information, may be received from one or more other sensors or sensor circuits.

[0119] In some examples, a high-power mode may be contrasted with a low-power mode and may include one or more of the following: enabling one or more additional sensors, switching from a low-power sensor or set of sensors to a higher-power sensor or set of sensors, triggering additional sensing from one or more additional sensors or medical devices, increasing the sensing frequency or sensing or storage resolution, increasing the amount of data to be collected, communicated (e.g., from a first medical device to a second medical device, etc.), or stored, triggering the storage of currently available information from the loop recorder in long-term storage, or increasing the storage capacity or time period of the loop recorder, or otherwise altering the device behavior to capture additional or higher-resolution physiological information or perform more processing, etc.

[0120] Additionally or alternatively, event storage can be triggered. Information sensed or recorded in high-power mode can be converted from short-term storage (such as in a cyclic recorder) to long-term or non-volatile memory, or in some examples, prepared for communication to an external device separate from the medical device. In examples, cardiac electrical or cardiac mechanical information up to and, in some examples, including detected atrial fibrillation events can be stored, such as to increase the specificity of detection. In examples, multiple cyclic recorder windows (e.g., 2-minute windows) can be stored sequentially. In systems without early detection, recording this information would require a cyclic recorder with a long time period at a significant additional cost (e.g., power, processing resources, component costs, etc.).

[0121] Figure 10 illustrates an example implantable medical device system 1000, including an implantable medical device 1001 electrically coupled to a heart 1005, such as via one or more leads coupled to the implantable medical device 1001 (via one or more lead ports in the head 1002 of the implantable medical device 1001, including a first lead port, a second lead port, or a third lead port 1041, 1042, 1043)). In the example, the implantable medical device 1001 may include an antenna, such as in the head 1002, configured to enable communication with external systems and one or more electronic circuits (e.g., evaluation circuitry, etc.) in a hermetically sealed housing (CAN).

[0122] Implantable medical device 1001 may include an insertable cardiac monitor, pacemaker, defibrillator, cardiac resynchronization therapy device, or other subcutaneous implantable medical device or cardiac rhythm management device configured for implantation in a patient's chest, having one or more leads to position one or more electrodes or other sensors in or near the heart 1005, such as in one or more atria or ventricles. Separate from or in addition to the one or more electrodes or other sensors connected to the leads, implantable medical device system 1000 may also include one or more electrodes or other sensors (e.g., pressure sensors, accelerometers, gyroscopes, microphones, etc.) powered by a power source within implantable medical device 1001. The one or more electrodes or other sensors connected to the leads, implantable medical device 1001, or combinations thereof may be configured to detect physiological information from the patient or to provide the patient with one or more therapies or stimuli.

[0123] Implantable devices may additionally include leadless pacemakers, small (e.g., smaller than conventional implantable devices, having a volume of about 1 cc in some examples), self-contained devices comprising one or more sensors, circuitry, or electrodes configured to monitor physiological information from the heart 1005 (e.g., heart rate, etc.), detect physiological conditions associated with the heart 1005 (e.g., tachycardia), or deliver one or more therapies or stimulations to the heart 1005 without the complications of conventional leaded or implantable devices (e.g., required incisions and pockets, complications associated with lead placement, breakage, or displacement, etc.). In some examples, leadless pacemakers may have more limited power and processing capabilities than conventional CRM devices; however, multiple leadless pacemaker devices may be implanted in or around the heart to detect physiological information from one or more chambers of the heart, or to deliver one or more therapies or stimulations to one or more chambers of the heart. Multiple leadless pacemaker devices may communicate between themselves or one or more other implantable or external devices.

[0124] The implantable medical device 1001 may include one or more electronic circuits configured to sense one or more physiological signals, such as electrograms or signals representing the mechanical function of the heart 1005. In some examples, the housing may act as electrodes, such as for sensing or pulse delivery. For example, electrodes from one or more leads may be used with the housing, such as for unipolar sensing for electrograms or for delivering one or more pacing pulses. Defibrillation electrodes (e.g., first defibrillation coil electrode 1028, second defibrillation coil electrode 1029, etc.) may be used with the housing to deliver one or more cardioversion / defibrillation pulses.

[0125] In the example, the implantable medical device 1001 can sense impedance such as that between electrodes located on one or more leads or on the housing. The implantable medical device 1001 can be configured to inject current between a pair of electrodes, sense the resulting voltage between the same or different pairs of electrodes, and determine impedance, such as using Ohm's law. Impedance can be sensed in a bipolar configuration (where the same pair of electrodes can be used for both current injection and voltage sensing), a tripolar configuration (where a pair of electrodes for current injection and a pair of electrodes for voltage sensing can share a common electrode), or a quadrupole configuration (where the electrodes for current injection can be different from those for voltage sensing), etc. In the example, the implantable medical device 1001 can be configured to inject current between electrodes on one or more of the first, second, third, or fourth leads 1020, 1025, 1030, 1035 and the housing, and sense the resulting voltage between the same or different electrodes and the housing.

[0126] The implantable medical device 1001 may integrate one or more other physiological sensors to sense one or more other physiological signals, such as heart rate, heart rate variability, thoracic or intrathoracic impedance, intracardiac impedance, arterial pressure, pulmonary artery pressure, right ventricular pressure, left ventricular coronary artery pressure, coronary artery blood temperature, blood oxygen saturation, one or more heart sounds, physical activity or exertion level, physiological response to activity, posture, respiration, body weight, or body temperature, or one or more of these. The arrangement and function of these leads and electrodes are described above by way of example rather than by way of limitation. Other arrangements and uses of these leads and electrodes may be considered depending on the patient's needs and the capabilities of the implantable device.

[0127] Figure 11 illustrates example implantable medical devices, including a subcutaneously implantable cardioverter defibrillator 1101, a leadless pacemaker 1102 including cusps for placement, and an insertable cardiac monitor 1103.

[0128] Figure 12 illustrates an example remote patient management system 1200 for communicating with mobile medical devices, such as for receiving or providing information to one or more mobile medical devices, including programming instructions. The example remote patient management system 1200 includes a first remote patient management device 1201 (e.g., a LATITUDE). TM The NXT Remote Patient Management System is used for home monitoring and RF telemetry capabilities with mobile medical devices via one or more communication circuits, and communicates with cloud-based servers or clinician programming environments via network connections; a second remote patient management device 1202 (e.g., EMBLEM) TM An S-ICD programmer, with RF telemetry capabilities via one or more communication circuits and an optional external telemetry stick, is used to communicate with mobile medical devices; and a third remote patient management system 1203 (e.g., LATITUDE). TM The programming system (e.g., Model 3300, etc.) has RF telemetry capability via one or more communication circuits and an external telemetry rod 1204 (e.g., Model 6395 telemetry rod, etc.) (including an external telemetry coil configured for inductive communication with a corresponding telemetry coil of an implantable medical device). Although not shown herein, the remote patient management system 1200 may include one or more other remote patient management systems, such as one or more other latitudes. TM Programming systems, remote patient monitoring applications for mobile devices used by patients or other caregivers, etc. Each type of remote patient monitoring or management system has different capabilities, and in some examples, different permissions are required relative to different programming instructions or features.

[0129] Figure 13 shows a block diagram of example machine 1300 on which any one or more of the techniques (e.g., methods) discussed herein can be executed. Parts of this description can be applied to the computational framework of one or more of the medical devices described herein (such as implantable medical devices, external programmers, etc.). Furthermore, as described herein with respect to medical device components, systems, or machines, this may require regulatory compliance that cannot be met by general-purpose computers, components, or machines.

[0130] Examples, as described herein, may include logic or multiple components or mechanisms in machine 1300, or those that can be operated by them. A circuit system (e.g., a processing circuit system, an evaluation circuit, etc.) is a collection of circuits implemented in a tangible entity of machine 1300, which includes hardware (e.g., simple circuits, gates, logic, etc.). Circuit system members may be flexible over time. A circuit system includes members that can perform a specified operation individually or in combination during operation. In the example, the hardware of the circuit system may be invariably designed to perform a specific operation (e.g., hardwired). In the example, the hardware of the circuit system may include physically connected components (e.g., execution units, transistors, simple circuits, etc.) including machine-readable media that are physically modified (e.g., magnetic ground, electrical ground, movable placement of invariant aggregate particles, etc.) to encode instructions for a specific operation. When the physical components are connected, the underlying electrical characteristics of the hardware composition change, for example, from an insulator to a conductor, or vice versa. Instructions enable embedded hardware (e.g., an execution unit or loading mechanism) to create members of the circuit system in the hardware via variable connections to perform portions of a specific operation during operation. Therefore, in this example, the machine-readable medium element is part of the circuit system, or communicatively coupled to other components of the circuit system during device operation. In this example, any physical component can be used in more than one member of more than one circuit system. For example, under operation, an execution unit can be used at one point in time in a first circuit of a first circuit system, and reused at different times by a second circuit of the first circuit system or by a third circuit of the second circuit system. Additional examples of these components of machine 1300 are as follows.

[0131] In alternative embodiments, machine 1300 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, machine 1300 may operate as a server machine, a client machine, or both in a server-client network environment. In the example, machine 1300 may act as a peer machine in a peer-to-peer (P2P) (or other distributed) network environment. Machine 1300 may be a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, network device, network router, switch, or bridge, or any machine capable of executing instructions (sequentially or otherwise) specifying actions to be taken by that machine. Furthermore, while only a single machine is shown, the term "machine" should also be considered to include any collection of machines, such as cloud computing, software as a service (SaaS), and other computer cluster configurations, that individually or jointly execute a set (or more) of instructions to perform any one or more of the methods discussed herein.

[0132] Machine 1300 (e.g., a computer system) may include a hardware processor 1302 (e.g., a central processing unit (CPU), graphics processing unit (GPU), hardware processor core, or any combination thereof), main memory 1304, static memory 1306 (e.g., memory or storage device for firmware, microcode, basic-input-output (BIOS), unified extensible firmware interface (UEFI), etc.), and mass storage device 1308 (e.g., hard disk drive, tape drive, flash memory, or other block device), some or all of which may communicate with each other via interconnect 1330 (e.g., a bus). Machine 1300 may also include a display unit 1310, an input device 1312 (e.g., a keyboard), and a user interface (UI) navigation device 1314 (e.g., a mouse). In this example, the display unit 1310, input device 1312, and UI navigation device 1314 may be a touchscreen display. Machine 1300 may additionally include signal generating device 1318 (e.g., a speaker), network interface device 1320, and one or more sensors 1316, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or one or more other sensors. Machine 1300 may include output controller 1328, such as a serial (e.g., universal serial bus, USB), parallel, or other wired or wireless (e.g., infrared, near field communication, NFC, etc.) connection, to communicate with or control one or more peripheral devices (e.g., a printer, a card reader, etc.).

[0133] The registers, main memory 1304, static memory 1306, or mass storage device 1308 of the hardware processor 1302 may be or include a machine-readable medium 1322 on which one or more sets of data structures or instructions 1324 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein are stored. During execution of the instructions 1324 by the machine 1300, the instructions 1324 may also reside wholly or at least partially within any one of the registers, main memory 1304, static memory 1306, or mass storage device 1308 of the hardware processor 1302. In the example, one or any combination of the hardware processor 1302, main memory 1304, static memory 1306, or mass storage device 1308 may constitute the machine-readable medium 1322. Although the machine-readable medium 1322 is shown as a single medium, the term "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) configured to store one or more instructions 1324.

[0134] The term "machine-readable medium" can include any medium capable of storing, encoding, or carrying instructions executed by machine 1300 and causing machine 1300 to perform any one or more techniques of this disclosure, or any medium capable of storing, encoding, or carrying data structures used by or associated with those instructions. Examples of non-limiting machine-readable media can include solid-state memory, optical media, magnetic media, and signals (e.g., radio frequency signals, other photon-based signals, sound signals, etc.). In examples, non-transient machine-readable media include machine-readable media with a plurality of particles having invariant (e.g., rest) mass, and thus are compositions of matter. Therefore, a non-transient machine-readable medium is a machine-readable medium that does not include transiently propagating signals. Specific examples of non-transient machine-readable media may include: non-volatile memories, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM) and electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable hard disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0135] Instruction 1324 can be further transmitted or received on communication network 1326 via network interface device 1320 using a transmission medium, utilizing any of a variety of transport protocols (e.g., Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Example communication networks may include, among others, local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), plain old telephone (POTS) networks, and wireless data networks (e.g., referred to as Wi-Fi). The Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard family, known as WiMax The network interface device 1320 may include one or more physical jacks (e.g., Ethernet, coaxial, or telephone jacks) or one or more antennas to connect to the communication network 1326. In the example, the network interface device 1320 may include multiple antennas to perform wireless communication using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technologies. The term "transmission medium" should be considered to include any intangible medium capable of storing, encoding, or carrying instructions executed by machine 1300, and includes digital or analog communication signals or other intangible media to facilitate communication of such software. The transmission medium is a machine-readable medium.

[0136] Various embodiments are illustrated in the figures above. One or more features from one or more of these embodiments may be combined to form other embodiments. The method examples described herein may be implemented, at least in part, by a machine or computer. Some examples may include a computer-readable or machine-readable medium encoded with instructions operable to configure an electronic device or system to perform the methods described in the examples above. Implementations of such methods may include code, such as microcode, assembly language code, or high-level language code or similar code. Such code may include computer-readable instructions for performing various methods. The code may form part of a computer program product. Furthermore, the code may be tangibly stored on one or more volatile or non-volatile computer-readable media during execution or at other times.

[0137] The detailed description above is intended to be illustrative and not restrictive. Therefore, the scope of this disclosure should be determined by reference to the appended claims together with the full scope of the equivalents to which such claims are entitled.

Claims

1. A medical device system for improving the determination of heart failure readmission risk to optimize the resources of the medical device system, the medical device system comprising: A device for receiving physiological information from patients; And means for analyzing physiological information generated over a specific time period relative to a previous heart failure event relative to the patient's baseline values; A means for adjusting the patient’s baseline value from a long-term baseline value to a shorter-term baseline value or a static value in response to a detected or received triggering event, the static value indicating the baseline value relative to a previous time of the detected or received triggering event; Device for determining the patient’s readmission score based on analyzed physiological information, the patient’s readmission score indicating the risk of subsequent readmission following hospitalization or treatment or discharge from the previous heart failure event; And means for providing control signals based on the readmission score to control the mode or operation of the medical device system to optimize the resources of the medical device system.

2. The medical device system according to claim 1, wherein, The apparatus for receiving, analyzing, adjusting, determining, and providing includes: a signal receiver circuit configured to receive physiological information of the patient; and an evaluation circuit configured to: analyze physiological information generated over a specific time period relative to the previous heart failure event, relative to the patient's baseline value; adjust the patient's baseline value from a long-term baseline value to one of a shorter-term baseline value or a static value, the static value indicating the baseline value at a time prior to the detected or received triggering event, based on the analyzed physiological information, determine a readmission score for the patient, the readmission score indicating the risk of subsequent readmission following hospitalization or treatment or discharge from the previous heart failure event; and provide control signals based on the readmission score to control the mode or operation of the medical device system to optimize the resources of the medical device system.

3. The medical device system according to claim 2, wherein, The assessment circuit is configured to determine the readmission score relative to the patient's adjusted baseline.

4. The medical device system according to claim 2, wherein, The evaluation circuit is configured to provide the control signal based on the readmission score in response to the determined readmission score exceeding an adjusted baseline threshold.

5. The medical device system according to any one of claims 2 to 4, wherein, The assessment circuit is configured to determine the patient's baseline value, including: determining the long-term baseline value as a function of the patient's previous physiological information over 30 days or more; and determining the shorter-term baseline value as a function of the patient's physiological information over a period of time, between 3 days and 10 days, wherein adjusting the patient's baseline value includes adjusting the patient's baseline value from the long-term baseline value to the shorter-term baseline value.

6. The medical device system according to any one of claims 2 to 5, wherein, The assessment circuit is configured to determine the patient's baseline value, including: determining the long-term baseline value as a function of the patient's previous physiological information for 30 days or more; and determining the static value, the static value indicating the baseline value relative to a previous time of a detected or received triggering event, the previous time including at least 30 days prior to the detected or received triggering event, wherein adjusting the patient's baseline value includes adjusting the patient's baseline value from the long-term baseline value to the static value.

7. The medical device system according to any one of claims 2 to 6, wherein, The assessment circuit is configured to determine the baseline value as a function of the long-term baseline value, the shorter-term baseline value, and the static value during the transition period, until the determined readmission score or the long-term baseline meets the static value.

8. The medical device system according to any one of claims 2 to 6, wherein, The assessment circuit is configured to determine the patient's readmission score as a composite function of analyzed physiological information, which includes two or more of the following: S1 heart sound information, S3 heart sound information, thoracic impedance, activity information, respiratory information, and heart rate information.

9. The medical device system according to claim 8, wherein, The signal receiver circuit or the evaluation circuit is configured to receive a discharge instruction from a patient hospitalized or treated for the previous heart failure event, wherein determining the patient's readmission score includes determining that the patient may experience a readmission instruction within a specific time period following receiving the discharge instruction from the patient hospitalized or treated for the previous heart failure event, or that the previous heart failure event and discharge may lead to a readmission instruction.

10. The medical device system according to any one of claims 2 to 7, wherein, The assessment circuit is configured to analyze S3 heart sound information within a first time period and a second time period, and to determine the patient's readmission score as a function of the difference in S3 heart sound information within the first time period and the second time period, wherein the first time period is prior to the previous heart failure event, and the second time period is after discharge from hospitalization or treatment following the previous heart failure event.

11. The medical device system according to claim 10, wherein, The first time period is between -25 days and -5 days prior to the previous heart failure event, and the second time period is between 0 days and 30 days after discharge from hospitalization or treatment following the previous heart failure event.

12. The medical device system according to any one of claims 2 to 7, wherein, The assessment circuit is configured to determine the readmission score as a function of: a first composite function of physiological information analyzed during a first time period prior to the previous heart failure event, the physiological information including two or more of the following: S1 heart sound information, S3 heart sound information, thoracic impedance, activity information, respiratory information, and heart rate information; The second composite function of S3 heart sound information and S1 heart sound information during a period of time prior to the previous heart failure event; and the integrated value of the first composite function during a second period of time after discharge from hospitalization or treatment following the previous heart failure event.

13. The medical device system according to any one of claims 2 to 12, wherein, The triggering event is one or more of the following: a received request to adjust the corresponding baseline value, a detected state of alert, or a received or detected indication of the patient's first heart failure event or hospitalization or treatment for the first heart failure event, wherein the first heart failure event is the previous heart failure event.

14. A method for improving the determination of heart failure readmission risk to optimize the resources of a medical device system, comprising: Use a signal receiver circuit to receive the patient's physiological information; Using an evaluation circuit, physiological information generated within a specific time period relative to the patient's baseline values ​​was analyzed relative to the previous heart failure event. Using the evaluation circuit, in response to a detected or received triggering event, the patient's baseline value is adjusted from a long-term baseline value to one of a shorter-term baseline value or a static value, the static value indicating the baseline value relative to a previous time of the detected or received triggering event; Using the assessment circuitry, a readmission score is determined for the patient based on analyzed physiological information. The readmission score indicates the risk of subsequent readmission following hospitalization or treatment or discharge from the previous heart failure event. The assessment circuitry also provides control signals based on the readmission score to control the mode or operation of the medical device system to optimize its resources.

15. The method according to claim 14, wherein, Determining the patient's readmission score involves determining a composite function of physiological information to be analyzed, which includes two or more of the following: S1 heart sound information, S3 heart sound information, thoracic impedance, activity information, respiratory information, and heart rate.

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