WO2025210417A1

WO2025210417A1 - Medical system for determination of risk score for predicting occurrence of arrhythmias and/or sudden cardiac arrest

Info

Publication number: WO2025210417A1
Application number: PCT/IB2025/052350
Authority: WO
Inventors: Gautham Rajagopal; Shantanu Sarkar; Maudeline R. DEUS; Yong K. Cho; Sean R. LANDMAN
Original assignee: Medtronic Inc
Current assignee: Medtronic Inc
Priority date: 2024-04-03
Filing date: 2025-03-04
Publication date: 2025-10-09
Anticipated expiration: 2026-10-03

Abstract

An example system includes an insertable cardiac monitoring device including sensing circuitry configured to sense continuous electrocardiogram (ECG) data based on electrical activity of a heart of the patient via a plurality of electrodes on a beat-by-beat basis. The system includes processing circuitry configured to determine one or more first parameters based on the continuous ECG data, the one or more first parameters including at least one of an absolute corrected QT (QTc) interval value or a magnitude of change in QTc interval values over time. The processing circuitry is configured to predict a risk of at least one of an arrhythmia event or a sudden cardiac arrest (SCA) event based on the one or more first parameters. The processing circuitry is configured to determine that the risk satisfies a threshold, and based on the risk satisfying the threshold, send an alert indicative of the risk.

Description

MEDICAL SYSTEM FOR DETERMINATION OF RISK SCORE FOR PREDICTING OCCURRENCE OF ARRHYTHMIAS AND/OR SUDDEN CARDIAC ARREST

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63/573,768, filed April 3, 2024, the entire content of which is incorporated herein by reference.

TECHNICAL FIELD

[0002] This disclosure generally relates to systems including medical devices and, more particularly, to monitoring of patient health using such systems.

BACKGROUND

[0003] Cardiac signal analysis may be performed by a variety of devices, such as implantable medical devices (IMDs), insertable cardiac monitors (ICMs) and external devices (e.g., smart watches, fitness monitors, mobile devices, Holter monitors, wearable defibrillators, or the like). For example, devices may be configured to process cardiac signals (e.g., electrocardiograms (ECGs)) sensed by one or more electrodes. Features of cardiac signals may include the P-wave, Q-wave, R-wave, S-wave, QRS-complex, and T- wave. A QT interval may be the time from the beginning of the QRS complex to the end of the T-wave. A QTc interval is a QT interval that has been normalized or corrected with respect to a heart rate using a formula. Accurate detection and delineation of features in cardiac signals, such as QT intervals or QTc intervals, may be of importance for monitoring patient health, such as risk of sudden cardiac death.

SUMMARY

[0004] In general, the disclosure describes techniques for continuous, long-term monitoring of a patient including predicting a risk of an occurrence of arrhythmias and/or sudden cardiac arrest (SCA). Such continuous monitoring may include continuously monitoring ECG data of the patient on a beat-by-beat basis. The risk(s) may be based on features derived from one or more parameters of the continuous ECG data sensed by an IMD, such as an ICM. The risk(s) may take the form of risk score(s) which may be determined based on weighting the one or more parameters with respective weights. When a risk score satisfies a threshold (e.g., is greater than, or greater than or equal to the threshold), the techniques may include sending an alert indicative of the risk. In some examples, upon determining the occurrence of an arrhythmia event and/or an SCA event, the techniques may include determining whether there is an association between any of the one or more features and the arrhythmia event or SCA event. If there is such an association, the techniques may include modifying the criteria for sending the alert, such as either modifying a weight assigned to the parameter for risk score calculation, or simply sending an alert if the same propert(ies) (or similar properties) of the associated parameter appear again in the future. The modified criteria may be patient specific, such as where the associated parameter is an associated parameter for a given patient, or may be for a larger population of patients, such as patients of a specific demographic group or all patients, where the associated parameter may be an associated parameter for more than one specific patient. It should be noted that while the ECG may be monitored on a beat-by-beat basis in the long term (e.g., such as when a patient is ambulatory taking part in activities of daily living), parameters may not necessarily be derived for each beat. In some examples, a system may determine whether a given beat is noisy, and if the given beat is noisy, either eschew determining the parameters associated with the noisy beat or not consider any determined parameters associated with the noisy beat.

[0005] Occurrence of arrhythmias such as ventricle tachycardia, ventricle fibrillation, atrial fibrillation, asystole, bradycardia, or the like, can increase the risk of sudden cardiac arrest (SCA). The survival rate from SCA decreases for every minute that the patient does not receive therapy. Thus, there is need for continuous monitoring to predict and/or prevent the occurrence of arrhythmias and sudden cardiac arrest so that patients can receive appropriate therapy in a timely manner. An implantable device which can monitor an ECG of a patient over long-term can be a useful tool to continuously evaluate and predict the risk of occurrence of SCA and other arrhythmias based on ECG-derived features such as absolute QTc interval values, magnitudes of change in QTc values over time, heart rate variability values, changes in PVC burden, other PVC characteristics such as monomorphic vs polymorphic occurrences, PVC burdens of couplets and/or triplets, changes in ST characteristics over time, QRS widths, changes in QRS width, and/or the like.

[0006] An ICM capable of continuously monitoring such features over a long period may be a useful tool. For example, such an ICM may monitor the features over an extended period of time and when the patient is ambulatory, participating in activities of daily living. Continuous monitoring may include triggered, episodic, and/or periodic sensing of patient signals, without requiring human intervention. In some examples, such an ICM may continuously determine features. In some examples, the continuous determination of features may include the exclusion of features for heart beats determined to be noisy. As such, the use of “continuous” is not intended to indicate that there will necessarily be a feature value associated with every heartbeat.

[0007] In one example, a system includes: an insertable cardiac monitoring device, the insertable cardiac monitoring device comprising: a housing configured to be subcutaneously inserted into a patient, the housing comprising a cover; a plurality of electrodes, at least one of the plurality of electrodes being disposed on a proximal portion of the cover and at least another one of the plurality of electrodes being disposed on a distal portion of the cover; sensing circuitry configured to sense continuous electrocardiogram (ECG) data based on electrical activity of a heart of the patient via the plurality of electrodes; one or more memories configured to store the continuous ECG data, the continuous ECG data being sensed on a beat-by-beat basis; and processing circuitry coupled to the one or more memories and configured to: determine one or more first parameters based on the continuous ECG data, the one or more first parameters comprising at least one of an absolute corrected QT (QTc) interval value or a magnitude of change in QTc interval values over time; predict a risk of at least one of an arrhythmia event or a sudden cardiac arrest (SCA) event based on the one or more first parameters; determine that the risk satisfies a threshold; and based on the risk satisfying the threshold, send an alert indicative of the risk.

[0008] In another example, a method includes: determining, by processing circuitry, one or more first parameters based on continuous ECG data, the continuous ECG data being sensed by an insertable cardiac monitoring device on a beat-by-beat basis, and the one or more first parameters comprising at least one of an absolute corrected QT (QTc) interval value or a magnitude of change in QTc interval values over time; predicting, by the processing circuitry, a risk of at least one of an arrhythmia event or a sudden cardiac arrest (SCA) event based on the one or more first parameters; determining, by the processing circuitry, that the risk satisfies a threshold; and sending, by the processing circuitry and based on the risk satisfying the threshold, an alert indicative of the risk.

[0009] In another example, a non-transitory, computer-readable storage medium stores instructions, which when executed, cause processing circuitry to: determine one or more first parameters based on continuous ECG data, the continuous ECG data being sensed by an insertable cardiac monitoring device on a beat-by-beat basis, and the one or more first parameters comprising at least one of an absolute corrected QT (QTc) interval value or a magnitude of change in QTc interval values over time; predict a risk of at least one of an arrhythmia event or a sudden cardiac arrest (SCA) event based on the one or more first parameters; determine that the risk satisfies a threshold; and send, based on the risk satisfying the threshold, an alert indicative of the risk.

[0010] The summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the systems, device, and methods described in detail within the accompanying drawings and description below. Further details of one or more examples of this disclosure are set forth in the accompanying drawings and in the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

[0011] FIG. 1 illustrates the environment of an example medical system in conjunction with a patient.

[0012] FIG. 2 is a functional block diagram illustrating an example configuration of the IMD of the medical system of FIG. 1.

[0013] FIG. 3 A is a perspective drawing illustrating an insertable cardiac monitor.

[0014] FIG. 3B is a perspective drawing illustrating another insertable cardiac monitor. [0015] FIG. 4 is a functional block diagram illustrating an example configuration of the external device of FIG. 1.

[0016] FIG. 5 is a block diagram illustrating an example system that includes an access point, a network, external computing devices, such as a server, and one or more other computing devices, which may be coupled to the medical device and external device of FIGS. 1-4, in accordance with one or more examples of the present disclosure.

[0017] FIG. 6 is a flow diagram illustrating example risk score techniques according to one or more aspects of this disclosure.

[0018] FIG. 7 is a flow diagram illustrating other example risk score techniques according to one or more aspects of this disclosure. DETAILED DESCRIPTION

[0019] Continuous monitoring of ECGs of a patient may allow prediction of a risk of SCA and/or arrhythmias of the patient based on certain features which may be determinable from the ECGs. A relatively high risk of SCA may indicate a need for prompt or immediate medical intervention, in order to improve a patient health situation because SCA may lead to death of the patient. A relatively high risk of arrhythmias may also indicate a need for medical intervention.

[0020] This disclosure describes example devices and systems which may continuously monitor, on a beat-by-beat basis, a patient ECG and determine an SCA risk score and/or an arrhythmia risk score based on ECG-derived features. For example, an IMD, such as an ICM, may perform long term monitoring of a continuously sensed ECG signal. The ICM may calculate a risk score for predicting sudden cardiac arrest based on the continuously sensed ECG signal. In some examples, the ICM may also calculate a risk score for predicting the occurrence of arrhythmias, such as ventricular tachycardia (VT) and/or ventricular fibrillation (VF). In some examples, an SCA risk score may be indicative of a risk of an SCA event occurring in the near future, such as within the next 24 hours or less. In some examples, an arrhythmia risk score may be indicative of a risk that an arrhythmia event occurs within a longer period of time in the future, such as within the next several weeks. In some examples, the ICM may take the form of a Reveal LINQ™ or LINQ II™ available from Medtronic, Inc., of Minneapolis, Minnesota, which may be inserted subcutaneously in the patient. In some examples, rather than, or in addition to, the ICM calculating risk score(s), an external device, such as a cloud-based computing system, may calculate the risk score(s).

[0021] A variety of types of medical devices sense ECGs. Some medical devices that sense ECGs are non-invasive, e.g., using a plurality of electrodes placed in contact with external portions of the patient, such as at various locations on the skin of the patient. The electrodes used to monitor the ECG in these non-invasive processes may be attached to the patient using an adhesive, strap, belt, or vest, as examples, and electrically coupled to a monitoring device, such as an electrocardiograph, Holter monitor, or other electronic device. The electrodes are configured to sense electrical signals associated with the electrical activity of the heart or other cardiac tissue of the patient, and to provide these sensed electrical signals to the electronic device for further processing and/or display of the electrical signals. The non-invasive devices and methods may be utilized on a temporary basis, for example to monitor a patient during a clinical visit, such as during a doctor’s appointment, or for example for a predetermined period of time, for example for one day (twenty-four hours), or for a period of several days.

[0022] Some IMDs also sense and monitor ECGs. The electrodes used by IMDs to sense ECGs are typically integrated with a housing of the IMD and/or coupled to the IMD via one or more elongated leads. Example IMDs that monitor ECGs include pacemakers and implantable cardioverter-defibrillators, which may be coupled to intravascular or extravascular leads, as well as pacemakers with housings configured for implantation within the heart, which may be leadless. An example of pacemaker configured for intracardiac implantation is the Micra™ Transcatheter Pacing System, available from Medtronic pic. Some IMDs that do not provide therapy, e.g., implantable patient monitors, sense ECGs. Examples of such an IMD include Reveal LINQ™ and LINQ II™. Such IMDs may facilitate relatively longer-term monitoring of patients during normal daily activities, and may periodically transmit collected data to a network service, such as the Medtronic Carelink™ Network.

[0023] An ICM according to the techniques of this disclosure that may monitor an ECG continuously, in a beat-by-beat manner, and determine parameters therefrom, may more accurately predict a SCA or arrhythmia than a device that may monitor an ECG less than continuously. This is because more beats are considered (e.g., all non-noisy beats) when determining the parameters used to predict the SCA or arrhythmia, than with other devices. The ICM may be part of a system that may include an external device and/or server (such as a cloud-based server) that may cooperate in continuously monitoring the ECG. For example, the ICM may continuously sample the ECG, transmit ECG data to the external device and/or server on a periodic basis, and the external device and/or server may predict an SCA or arrhythmia. In examples where noisy beats are not considered, any of a number of different techniques for determining whether a beat is noisy or not noisy may be employed. In some examples, such techniques may be different at different times, to take into account different factors, such as whether the patient is active or at rest, the time of day, seasonal changes, weather or other environmental changes, etc. [0024] While this disclosure discusses techniques for predicting an SCA risk and for predicting an arrhythmia risk based on features of continuously sensed ECGs with an example ICM, any medical device configured to continuously sense an ECG, via implanted or external electrodes, including the examples identified herein, may implement the techniques of this disclosure for predicting an SCA risk of a patient and/or for predicting an arrhythmia risk of a patient.

[0025] FIG. 1 illustrates the environment of an example medical system 2 in conjunction with a patient 4, in accordance with one or more techniques of this disclosure. The example techniques may be used with an IMD 10, which may include an ICM and which may be in wireless communication with at least one of external device 12 and other devices not pictured in FIG. 1. In some examples, IMD 10 is implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1). IMD 10 may be positioned near the sternum near or just below the level of the heart of patient 4, e.g., at least partially within the cardiac silhouette. IMD 10 includes a plurality of electrodes (not shown in FIG. 1), and is configured to sense an ECG via the plurality of electrodes. In some examples, IMD 10 takes the form of the Reveal LINQ™ or LINQ II™ ICM, or another ICM similar to, e.g., a version or modification of, the Reveal LINQ™ or LINQ II™ ICM.

[0026] External device 12 may be a computing device with a display viewable by the user and an interface for providing input to external device 12 (e.g., a user input mechanism). In some examples, external device 12 may be a notebook computer, tablet computer, workstation, one or more servers, cellular phone, personal digital assistant, or another computing device that may run an application that enables the computing device to interact with IMD 10. External device 12 is configured to communicate with IMD 10 and, optionally, another computing device (not illustrated in FIG. 1), via wireless communication. External device 12, for example, may communicate via near-field communication technologies (e.g., inductive coupling, NFC or other communication technologies operable at ranges less than 10-20 cm) and far-field communication technologies (e.g., RF telemetry according to the 802.11 or Bluetooth® specification sets, or other communication technologies operable at ranges greater than near-field communication technologies).

[0027] External device 12 may be used to configure operational parameters for IMD 10. External device 12 may be used to retrieve data from IMD 10, such as sensed ECGs, features of sensed ECGs, SCA risk scores, and/or arrhythmia risk scores. The retrieved data may include values of physiological parameters measured by IMD 10, indications of episodes of arrhythmia, indications of episodes of SCA, or other maladies detected by IMD 10, and physiological signals recorded by IMD 10. For example, external device 12 may retrieve information from IMD 10 related to sensed ECGs, features of sensed ECGs, SCA risk scores, and/or arrhythmia risk scores over a time period. The time period may be predetermined, for example, hourly, daily or weekly, or may be otherwise based on the timing of the last retrieval of information by external device 12, or may be determined by a user of external device 12, such as by entering a command on external device 12 requesting the information from IMD 10. In some examples, the time period may be 2 hours. External device 12 may also retrieve ECG segments recorded by IMD 10, e.g., due to IMD 10 determining that an episode of arrhythmia or another malady occurred during the segment, or in response to a request to record the segment from patient 4 or another user. In some examples, external device 12 may receive the information from IMD 10 at a time associated with an event determined by IMD 10. For example, IMD 10 may determine that an SCA score is higher than a threshold and may, at a next opportunity, transmit the information to external device 12.

[0028] Processing circuitry of medical system 2, e.g., of IMD 10, external device 12, and/or of one or more other computing devices, may be configured to perform the example techniques of this disclosure for predicting a risk of SCA and/or arrhythmia, such as when patient 4 is ambulatory. For example, the processing circuitry of medical system 2 may analyze features in ECG(s) continuously sensed by IMD 10 to predict a risk of SCA and/or arrhythmia in patient 4. Although described in the context of examples in which IMD 10 that senses the ECGs includes an ICM, example systems including one or more implantable or external devices of any type configured to sense an ECG may be configured to implement the techniques of this disclosure.

[0029] For example, processing circuitry of medical system 2 may determine an SCA risk score and/or an arrhythmia risk score. The SCA risk score may be computed using a different time window (e.g., a shorter time window) of parameters than may be used for the arrhythmia risk score. For example, parameters used to determine a risk score for SCA may include those occurring within a period of hours (e.g., less than one day) rather than days or weeks. [0030] The SCA risk score may be based on one or more of an absolute QTc interval value, a magnitude of change in QTc interval values over time, a heart rate variability value, a change in PVC burden over time, other PVC characteristic(s) such as monomorphic vs polymorphic occurrence, a PVC burden of couplets and/or triplets, a change in ST characteristics over time, QRS widths, a change in QRS width over time, etc. In some examples, other parameters, such as sleep apnea burden, activity, blood pressure, fluid level (which may be determined through an impedance measurement, autonomic tone activity (which may be measured in the skin), irregularity/regularity of changes over time of length of RR intervals (e.g., as measured by a Lorenz plot, and/or the like, may be included as additional parameters used by the processing circuitry of system 2 to compute the SCA risk score. In some examples, the processing circuitry of system 2 may compute an arrhythmia risk score based on the same or similar parameters, but over a longer period of time. For example, the arrhythmia risk score may be based on parameters sensed over a period of days or weeks, rather than over a period of hours.

[0031] Processing circuitry of system 2 may calculate the SCA risk score and/or the arrhythmia risk score in any of a number of manners. For example, processing circuitry of system 2 may calculate a simple weighted sum of parameters which may be derived from regression techniques, or may employ machine learning data fusion algorithms like Bayesian belief networks, random forests, or support vector machines, deep learning convolution, long short-term memory (LSTM) networks, or transformers which specialize on time series analysis. Each of the different features included in a risk score calculation, may be given a respective weight for calculating the risk score. For example, absolute QT interval values may be more of an indicator of an increased risk for SCA than QRS width. In the example where both absolute QT interval value and QRS width are used to determine an SCA risk score, absolute QT interval value may be given a greater weight than QRS width in determining the SCA risk score.

[0032] The processing circuitry of system 2 may determine whether the SCA risk score satisfies an SCA threshold. For example, in the example where a higher SCA risk score means patient 4 has a higher risk of an SCA episode, the processing circuitry of system 2 may determine whether the SCA risk score is greater than (or greater than or equal to) the SCA threshold. In the example where a lower SCA risk score means has a higher risk of an SCA episode, the processing circuitry of system 2 may determine whether the SCA risk score is less than (or less than or equal to) the SCA threshold. If the SCA risk score satisfies the SCA threshold, processing circuitry of system 2 may send an alert to a clinician, caretaker of patient 4, and/or patient 4, so that patient 4 may seek out medical assistance and/or so that a clinician may provide the appropriate medical attention to patient 4, so as to prevent, or reduce the risk of, an occurrence of an actual SCA episode. The alert may include the SCA risk score, parameters used to determine the SCA risk score, an ECG segment, and/or other parameters sensed or determined by system 2 that may be of use to a clinician, caretaker, and/or patient 4. In some examples, the alert may include a command to activate or control a wearable cardioverter defibrillator to deliver therapy. In some examples, the alert may include a command to change a sensitivity of one or more detection algorithms, such as a ventricular tachycardia/ventricular fibrillation detection algorithm, that may be employed by processing circuitry of system 2 to be more sensitive.

[0033] Similarly, the processing circuitry of system 2 may determine whether the arrhythmia risk score satisfies an arrhythmia threshold. For example, in the example where a higher arrhythmia risk score means patient 4 has a higher risk of an arrhythmia episode, the processing circuitry of system 2 may determine whether the arrhythmia risk score is greater than (or greater than or equal to) the arrhythmia threshold. In the example where a lower arrhythmia risk score means has a higher risk of an arrhythmia episode, the processing circuitry of system 2 may determine whether the arrhythmia risk score is less than (or less than or equal to) the arrhythmia threshold. If the arrhythmia risk score satisfies the arrhythmia threshold, processing circuitry of system 2 may send an alert to a clinician, caretaker of patient 4, and/or patient 4, so that patient 4 can seek out medical assistance and/or so that a clinician may provide the appropriate medical attention to patient 4, so as to prevent, or reduce the risk of an actual arrhythmia. The alert may include the arrhythmia risk score, parameters used to determine the arrhythmia risk score, an ECG segment, and/or other parameters sensed or determined by system 2 that may be of use to a clinician, caretaker, and/or patient 4. In some examples, the alert may include a command to change a sensitivity of one or more detection algorithms, such as a ventricular tachycardia/ventricular fibrillation detection algorithm, that may be employed by processing circuitry of system 2 to be more sensitive.

[0034] In some examples, the techniques of this disclosure may be tuned for patient 4. For example, if patient 4 is deemed to be at higher risk for an SCA event than another patient, then the SCA risk score calculation may be different for patient 4 than the other patient and/or the SCA threshold may be different than for the other patient, so as to make the use of the alert more sensitive for patient 4 than the other patient or to reduce the time to when an alert is issued. In some examples, one or more detection algorithms employed by processing circuitry of system 2, such as a ventricular tachycardia/ventricular fibrillation detection algorithm, may be tuned for patient 4 to be more sensitive based on patient 4 being at higher risk or based on a determined SCA risk score (and/or arrhythmia risk score).

[0035] In some examples, if patient 4 experiences an occurrence of arrhythmias or an occurrence of an SCA event, then IMD 10 may use the parameters computed X minutes before the SCA event to compute an SCA risk score and/or to predict future events. For example, if an increase in QTc interval was detected within X minutes before the SCA event, then IMD 10 may issue an alert if the same increase in QTc is detected by IMD 10 in the future. In some examples, if an increase in QTc interval was detected within X minutes before the SCA event, then IMD 10 may more heavily weight the increased QTc interval when determining an SCA risk score in the future. In some examples, X minutes may be on the order of 5 - 30 minutes, such as 15 minutes.

[0036] In some examples, if patient 4 experiences an occurrence of arrhythmias or an occurrence of an SCA event and processing circuitry of system 2 determines a PVC burden increase Y days before the event, the increase in PVC burden may be incorporated into the calculation of an arrhythmia risk score and/or an SCA risk score so that high risk alert can be provided in the future if similar patterns of PVC changes are detected in the future for patient 4. This may aid processing circuitry of system 2 to predict future events based on previous occurrences. In some examples, if an event such as arrhythmias (VT/VF) or cardiac arrest occurs, processing circuitry of system 2 may store the calculated features X minutes before the event in memory of system 2. Processing circuitry of system 2 may modify the arrhythmia risk score calculation and/or the SCA risk score calculation in order to provide high risk alerts in the future if similar ECG parameters are observed in the future. In this way, if similar features occur in the future, an alert can be provided for increased risk of recurrence of the event. For example, if QTc interval values of >500 milliseconds (ms) were noticed before the occurrence of a VF episode, then an alert for increased risk of VF can be provided in the future if similar QTc values occur in the future. In some examples, processing circuitry of system 2 may modify the arrhythmia risk score calculation and/or the SCA risk score calculation in a patient-specific manner or for a group of patients. For example, processing circuitry of system 2 may modify the arrhythmia risk score calculation and/or the SCA risk score calculation specifically for patient 4, such as when particular patterns PVC burden occur with patient 4 before an occurrence of an arrhythmia or SCA event. Alternatively, or additionally, processing circuitry of system 2 may modify the arrhythmia risk score calculation and/or the SCA risk score calculation for patient 4 as part of a modification of the arrhythmia risk score calculation and/or the SCA risk score calculation for a larger group of patients than just patient 4. For example, processing circuitry of system 2 may modify the arrhythmia risk score calculation and/or the SCA risk score calculation for a larger group of patients, such as patients within a particular demographic, patients having same particular comorbidities, all patients, etc., when the particular patterns are occurring within the larger group of patients.

[0037] For example, IMD 10 may detect a T-wave of an ECG. IMD 10 may use the T- wave to determine a QT interval value. IMD 10 may determine the QT interval value for every beat (e.g., every non-noisy beat) thus providing long term QT interval value trends based on sensed ECGs. From this QT interval feature, processing circuitry of system 2 may determine daily QTc interval value trends. Processing circuitry of system 2 may determine magnitude of changes in QTc, which may be used for risk score calculations.

[0038] For example, IMD 10 may detect R- waves of the ECG and determine an R-R interval. IMD 10 may determine a T-wave within the R-R interval. For example, IMD 10 may determine a maximum amplitude sample within the R-R interval to be the T-wave. IMD 10 may determine a QT interval value based on the determined T-wave and the determined R-wave. For example, IMD 10 may determine a time between an R-wave peak and the determined T-wave to be the QT interval value. In other examples, IMD 10 may determine the QT interval value to be a number of samples between the R-wave peak and the determined T-wave. IMD 10 may determine a QTc interval value, for example, by applying one or more formulas to the QT interval value to determine the QTc interval value. Example formulas include a Bazett formula, a Fridericia formula, and a Framingham formula, which are set forth below:

Bazett formula: QTc = QT / RR Fridericia formula: QTc = QT / RR^1/3 Framingham formula: QTc = QT + 0.154 (1 - RR) where QTc is the corrected QT interval value, QT is the QT interval value and RR is the R-R interval value.

[0039] Further details and examples of determining QT interval values and/or QTc interval values are described in U.S. Patent 11,576,606, “CARDIAC SIGNAL QT INTERVAL DETECTION,” issued on February 14, 2023; U.S. Patent 11,589,794, entitled “CARDIAC SIGNAL QT INTERVAL DETECTION,” issued on February 28, 2023; and U.S. Patent Publication No. US 2023-0181083A1, published on June 15, 2023, each of which is hereby incorporated by reference.

[0040] IMD 10 may also detect PVC beats and compute a daily PVC burden. Processing circuitry of system 2 may monitor the daily PVC burden trends and changes in PVC burden from day-to-day which may be used for risk score calculation. Processing circuitry of system 2 may determine other PVC characteristics, such as monomorphic and polymorphic PVCs, by analyzing ECGs sensed by IMD 10. Processing circuitry of system 2 may monitor occurrences of couplets and triplets. Processing circuitry of system 2 may monitor other ECG parameters, such as heart rate variability values, QRS widths, changes in QRS width overtime, changes in T-wave morphology overtime, and changes in ST characteristics over time, and use any of, or any combination of, such parameters for computing a risk score. Processing circuitry of system 2 may monitor other parameters, which may not be derived or determined from ECG data, such as sleep apnea burden, activity, and blood pressure, which may be sensed by IMD 10, and may use any of, or any combination of, such parameters for risk score computation.

[0041] FIG. 2 is a functional block diagram illustrating an example configuration of IMD 10 of FIG. 1 in accordance with one or more techniques described herein. In the illustrated example, IMD 10 may include an ICM that includes electrodes 16A and 16B (collectively “electrodes 16”), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, storage device 56, switching circuitry 58, and sensors 62. Although the illustrated example includes two electrodes 16, IMDs including or coupled to more than two electrodes 16 may implement the techniques of this disclosure in some examples.

[0042] Processing circuitry 50 may include fixed function circuitry and/or programmable processing circuitry. Processing circuitry 50 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 50 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry 50 herein may be embodied as software, firmware, hardware or any combination thereof.

[0043] Sensing circuitry 52 may be selectively coupled to electrodes 16 via switching circuitry 58, e.g., to select the electrodes 16 and polarity, referred to as the sensing vector, used to continuously sense ECG data (e.g., on a beat-by-beat basis), as controlled by processing circuitry 50. Sensing circuitry 52 may sense signals from electrodes 16, e.g., to produce continuous ECG data, in order to facilitate continuously monitoring the electrical activity of the heart. Sensing circuitry 52 also may monitor signals from sensors 62, which may include one or more accelerometers, pressure sensors, optical sensors, impedance sensors, microphones, etc., as examples. Sensors 62 may produce signals indicative of a sleep apnea burden, an activity level, and/or blood pressure of patient 4. In some examples, sensing circuitry 52 may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 16 and/or sensors 62.

[0044] Sensing circuitry 52 and/or processing circuitry 50 may be configured to detect R-waves and T-waves. Sensing circuitry 52 may include one or more rectifiers, filters, amplifiers, comparators, and/or analog-to-digital converters, in some examples. In some examples, sensing circuitry 52 may output an indication to processing circuitry 50 in response to sensing an R-wave or a T-wave. In some examples, processing circuitry 50 may determine an R-wave or a T-wave in an indication from sensing circuitry 52. Processing circuitry 50 may use the indications of detected R-waves and T-waves for determining QT intervals or corrected QT intervals (QTc).

[0045] Sensing circuitry 52 may also provide one or more digitized ECG signals to processing circuitry 50 for analysis, e.g., for use in cardiac rhythm discrimination, and/or determination of absolute QTc interval values, magnitudes of changes in QTc values over time, heart rate variability values, changes in PVC burden over time, other PVC characteristics such as monomorphic vs polymorphic occurrences, PVC burdens of couplets and/or triplets, changes in ST characteristics over time, QRS widths, changes in QRS width over time, and/or the like according to the techniques of this disclosure. In some examples, processing circuitry 50 may store the digitized ECG in storage device 56. Processing circuitry 50 of IMD 10, and/or processing circuitry of another device that retrieves data from IMD 10, may analyze the ECG to determine QT intervals or QTc intervals according to the techniques of this disclosure.

[0046] Communication circuitry 54 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. Under the control of processing circuitry 50, communication circuitry 54 may receive downlink telemetry from, as well as send uplink telemetry to external device 12 or another device with the aid of an internal or external antenna, e.g., antenna 26. In addition, processing circuitry 50 may communicate with a networked computing device via an external device (e.g., external device 12) and a computer network, such as the Medtronic pic CareLink® Network. Antenna 26 and communication circuitry 54 may be configured to transmit and/or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth®, WiFi, or other proprietary or non-proprietary wireless communication schemes.

[0047] In some examples, storage device 56 includes computer-readable instructions that, when executed by processing circuitry 50, cause IMD 10 and processing circuitry 50 to perform various functions attributed to IMD 10 and processing circuitry 50 herein. Storage device 56 may include one or more of volatile, non-volatile, magnetic, optical, or electrical media, such as random access memory (RAM), read-only memory (ROM), nonvolatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media. Storage device 56 may store, as examples, programmed values for one or more operational parameters of IMD 10 and/or data collected by IMD 10 for transmission to another device using communication circuitry 54. Data stored by storage device 56 and transmitted by communication circuitry 54 to one or more other devices may include ECG data, parameters associated with the ECG data, and/or other parameters. The parameters associated with the ECG data may include one or more of absolute QTc interval values, magnitudes of changes in QTc values over time, heart rate variability values, changes in PVC burden over time, other PVC characteristics such as monomorphic vs polymorphic occurrences, PVC burdens of couplets and/or triplets, changes in ST characteristics overtime, QRS widths, changes in QRS width overtime, and/or the like. The other parameters may include one or more of sleep apnea burden, activity level of the patient, and/or blood pressure.

[0048] Processing circuity 50 may monitor the one or more ECG parameters continuously based on the continuous ECG data. For example, for each beat (or for each beat not determined to be noisy), processing circuitry 50 may determine one or more parameters of the ECG data.

[0049] Processing circuitry 50 may determine an SCA risk score and/or an arrhythmia risk score, for example, based on the one or more ECG based parameters. In some examples, processing circuitry 50 may further base the SCA risk score and/or the arrhythmia risk score on additional parameters, such as parameter not based on ECG data, but based on other sensor data such as accelerometer, activity, sound, and/or pressure sensors. Such additional parameters may include sleep apnea burden (e.g., a number of sleep apnea events during a predetermined period of time), an activity level, blood pressure, or the like.

[0050] Processing circuitry 50 may compare the determined SCA risk score and/or arrhythmia risk score to an SCA threshold and/or an arrhythmia threshold, respectively. If processing circuitry 50 determines that the SCA risk score satisfies the SCA threshold, processing circuitry 50 may control communication circuitry 54 to send an SCA alert, for example, to external device 12 or another device. If processing circuitry 50 determines that the arrhythmia risk score satisfies the arrhythmia threshold, processing circuitry 50 may control communication circuitry 54 to send an arrhythmia alert, for example, to external device 12 or another device.

[0051] In some examples, processing circuitry 50 may control communication circuitry 54 to send indications of absolute QTc interval values, magnitudes of changes in QTc values over time, heart rate variability values, changes in PVC burden over time, other PVC characteristics such as monomorphic vs polymorphic occurrences, PVC burdens of couplets and/or triplets, changes in ST characteristics over time, QRS widths, changes in QRS width over time, and/or the like. In some examples, processing circuitry 50 may control communication circuitry 54 to send indications of sleep apnea burden, activity level of the patient, and/or blood pressure.

[0052] FIG. 3 A is a perspective drawing illustrating an IMD 10A, which may be an example configuration of IMD 10 of FIGS. 1 and 2 as an ICM. In the example shown in FIG. 3A, IMD 10A may be embodied as a monitoring device having housing 812, proximal electrode 816A and distal electrode 816B. Housing 812 may further comprise first major surface 814, second major surface 818, proximal end 820, and distal end 822. Housing 812 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids. Housing 812 may be hermetically sealed and configured for subcutaneous implantation. Electrical feedthroughs provide electrical connection of electrodes 816A and 816B.

[0053] In the example shown in FIG. 3 A, IMD 10A is defined by a length /., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D. In one example, the geometry of the IMD 10A - in particular a width W greater than the depth D - is selected to allow IMD 10A to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during insertion. For example, the device shown in FIG. 3A includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion. For example, the spacing between proximal electrode 816A and distal electrode 816B may range from 5 millimeters (mm) to 55 mm, 30 mm to 55 mm, 35 mm to 55 mm, and from 40 mm to 55 mm and may be any range or individual spacing from 5 mm to 60 mm. In addition, IMD 10A may have a length L that ranges from 30 mm to about 70 mm. In other examples, the length L may range from 5 mm to 60 mm, 40 mm to 60 mm, 45 mm to 60 mm and may be any length or range of lengths between about 30 mm and about 70 mm. In addition, the width of major surface 814 may range from 3 mm to 15 mm, from 3 mm to 10 mm, or from 5 mm to 15 mm, and may be any single or range of widths between 3 mm and 15 mm. The thickness of depth D of IMD 10A may range from 2 mm to 15 mm, from 2 mm to 9 mm, from 2 mm to 5 mm, from 5 mm to 15 mm, and may be any single or range of depths between 2 mm and 15 mm. In addition, IMD 10A according to an example of the present disclosure is has a geometry and size designed for ease of implant and patient comfort. Examples of IMD 10A described in this disclosure may have a volume of three cubic centimeters (cm) or less, 1.5 cubic cm or less or any volume between three and 1.5 cubic centimeters.

[0054] In the example shown in FIG. 3 A, once inserted within the patient, the first major surface 814 faces outward, toward the skin of the patient while the second major surface 818 is located opposite the first major surface 814. In addition, in the example shown in FIG. 3 A, proximal end 820 and distal end 822 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient. IMD 10A, including instrument and method for inserting IMD 10A is described, for example, in U.S. Patent Publication No. 2014/0276928, incorporated herein by reference in its entirety.

[0055] Proximal electrode 816A is at or proximate to proximal end 820, and distal electrode 816B is at or proximate to distal end 822. Proximal electrode 816A and distal electrode 816B are used to sense ECG signals, e.g., ECG signals, thoracically outside the ribcage, which may be sub-muscularly or subcutaneously. Cardiac signals may be stored in a memory of IMD 10 A, and data may be transmitted via integrated antenna 830 A to another device, which may be another implantable device or an external device, such as external device 12. In some example, electrodes 816A and 816B may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, an ECG, EEG, EMG, or a nerve signal, or for measuring impedance, from any implanted location.

[0056] In the example shown in FIG. 3 A, proximal electrode 816A is at or in close proximity to the proximal end 820 and distal electrode 816B is at or in close proximity to distal end 822. In this example, distal electrode 816B is not limited to a flattened, outward facing surface, but may extend from first major surface 814 around rounded edges 824 and/or end surface 826 and onto the second major surface 818 so that the electrode 816B has a three-dimensional curved configuration. In some examples, electrode 816B is an uninsulated portion of a metallic, e.g., titanium, part of housing 812.

[0057] In the example shown in FIG. 3 A, proximal electrode 816A is located on first major surface 814 and is substantially flat, and outward facing. However, in other examples proximal electrode 816A may utilize the three-dimensional curved configuration of distal electrode 816B, providing a three-dimensional proximal electrode (not shown in this example). Similarly, in other examples distal electrode 816B may utilize a substantially flat, outward facing electrode located on first major surface 814 similar to that shown with respect to proximal electrode 816A. The various electrode configurations allow for configurations in which proximal electrode 816A and distal electrode 816B are located on both first major surface 814 and second major surface 818. In other configurations, only one of proximal electrode 816A and distal electrode 816B is located on both major surfaces 814 and 818, and in still other configurations both proximal electrode 816A and distal electrode 816B are located on one of the first major surface 814 or the second major surface 818 (e.g., proximal electrode 816A located on first major surface 814 while distal electrode 816B is located on second major surface 818). In another example, IMD 10A may include electrodes on both major surface 814 and 818 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10 A. Electrodes 816A and 816B may be formed of a plurality of different types of biocompatible conductive material, e.g., stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.

[0058] In the example shown in FIG. 3 A, proximal end 820 includes a header assembly 828 that includes one or more of proximal electrode 816A, integrated antenna 830 A, antimigration projections 832, and/or suture hole 834. Integrated antenna 830A is located on the same major surface (i.e., first major surface 814) as proximal electrode 816A and is also included as part of header assembly 828. Integrated antenna 830 A allows IMD 10A to transmit and/or receive data. In other examples, integrated antenna 830A may be formed on the opposite major surface as proximal electrode 816A or may be incorporated within the housing 812 of IMD 10A. In the example shown in FIG. 3A, anti-migration projections 832 are located adjacent to integrated antenna 830A and protrude away from first major surface 814 to prevent longitudinal movement of the device. In the example shown in FIG. 3 A, anti-migration projections 832 include a plurality (e.g., nine) small bumps or protrusions extending away from first major surface 814. As discussed above, in other examples antimigration projections 832 may be located on the opposite major surface as proximal electrode 816A and/or integrated antenna 830A. In addition, in the example shown in FIG. 3A, header assembly 828 includes suture hole 834, which provides another means of securing IMD 10A to the patient to prevent movement following insertion. In the example shown, suture hole 834 is located adjacent to proximal electrode 816A. In one example, header assembly 828 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD 10 A.

[0059] FIG. 3B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of IMD 10 from FIGS. 1 and 2 as an ICM. IMD 10B of FIG. 16B may be configured substantially similarly to IMD 10A of FIG. 3 A, with differences between them discussed herein.

[0060] IMD 10B may include a leadless, subcutaneously-implantable monitoring device, e.g., an ICM. IMD 10B includes housing having a base 840 and an insulative cover 842. Proximal electrode 816C and distal electrode 816D may be formed or placed on an outer surface of cover 842. Various circuitries and components of IMD 10B, e.g., described above with respect to FIG. 2, may be formed or placed on an inner surface of cover 842, or within base 840. In some examples, a battery or other power source of IMD 10B may be included within base 840. In the illustrated example, antenna 830B is formed or placed on the outer surface of cover 842 but may be formed or placed on the inner surface in some examples. In some examples, insulative cover 842 may be positioned over an open base 840 such that base 840 and cover 842 enclose the circuitries and other components and protect them from fluids such as body fluids. The housing including base 840 and insulative cover 842 may be hermetically sealed and configured for subcutaneous implantation.

[0061] Circuitries and components may be formed on the inner side of insulative cover 842, such as by using flip-chip technology. Insulative cover 842 may be flipped onto a base 840. When flipped and placed onto base 840, the components of IMD 10B formed on the inner side of insulative cover 842 may be positioned in a gap 844 defined by base 840. Electrodes 816C and 816D and antenna 830B may be electrically connected to circuitry formed on the inner side of insulative cover 842 through one or more vias (not shown) formed through insulative cover 842. Insulative cover 842 may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material. Base 840 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 816C and 846D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 816C and 816D may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.

[0062] In the example shown in FIG. 3B, the housing of IMD 10B defines a length /., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D, similar to IMD 10A of FIG. 3 A. For example, the spacing between proximal electrode 816C and distal electrode 816D may range from 5 mm to 50 mm, from 30 mm to 50 mm, from 35 mm to 45 mm, and may be any single spacing or range of spacings from 5 mm to 50 mm, such as approximately 40 mm. In addition, IMD 10B may have a length L that ranges from 5 mm to about 70 mm. In other examples, the length L may range from 30 mm to 70 mm, 40 mm to 60 mm, 45 mm to 55 mm, and may be any single length or range of lengths from 5 mm to 50 mm, such as approximately 45 mm. In addition, the width W may range from 3 mm to 15 mm, 5 mm to 15 mm, 5 mm to 10 mm, and may be any single width or range of widths from 3 mm to 15 mm, such as approximately 8 mm. The thickness or depth D of IMD 10B may range from 2 mm to 15 mm, from 5 mm to 15 mm, or from 3 mm to 5 mm, and may be any single depth or range of depths between 2 mm and 15 mm, such as approximately 4 mm. IMD 10B may have a volume of three cubic centimeters (cm) or less, or 1.5 cubic cm or less, such as approximately 1.4 cubic cm.

[0063] In the example shown in FIG. 3B, once inserted subcutaneously within the patient, outer surface of cover 842 faces outward, toward the skin of the patient. In addition, as shown in FIG. 3B, proximal end 846 and distal end 848 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient. In addition, edges of IMD 10B may be rounded.

[0064] FIG. 4 is a block diagram illustrating an example configuration of components of external device 12. In the example of FIG. 4, external device 12 includes processing circuitry 80, communication circuitry 82, storage device 84, and user interface 86.

[0065] Processing circuitry 80 may include one or more processors that are configured to implement functionality and/or process instructions for execution within external device 12. For example, processing circuitry 80 may be capable of processing instructions stored in storage device 84. Processing circuitry 80 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 80 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 80.

[0066] Communication circuitry 82 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as IMD 10. Under the control of processing circuitry 80, communication circuitry 82 may receive downlink telemetry from, as well as send uplink telemetry to, IMD 10, or another device. Communication circuitry 82 may be configured to transmit or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth®, WiFi, or other proprietary or non-proprietary wireless communication schemes. Communication circuitry 82 may also be configured to communicate with devices other than IMD 10 via any of a variety of forms of wired and/or wireless communication and/or network protocols.

[0067] Storage device 84 may be configured to store information within external device 12 during operation. Storage device 84 may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device 84 includes one or more of a short-term memory or a long-term memory. Storage device 84 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 84 is used to store data indicative of instructions for execution by processing circuitry 80. Storage device 84 may be used by software or applications running on external device 12 to temporarily store information during program execution.

[0068] Data exchanged between external device 12 and IMD 10 may include operational parameters, alerts, ECG-derived parameters, other parameters, and/or the like. External device 12 may transmit data including computer readable instructions which, when implemented by IMD 10, may control IMD 10 to change one or more operational parameters and/or export collected data, such as ECG-derived parameters, other sensed parameters, and/or the like. For example, processing circuitry 80 may transmit an instruction to IMD 10 which requests IMD 10 to export collected data (e.g., ECG-derived parameter, other sensed parameters, and/or the like) to external device 12. In turn, external device 12 may receive the collected data from IMD 10 and store the collected data in storage device 84. Processing circuitry 80 may implement any of the techniques described herein to analyze ECG data received from IMD 10, e.g., to determine one or more parameters, an SCA risk score, an arrhythmia risk score, whether the SCA risk score satisfies an SCA threshold, whether the arrhythmia risk score satisfies an arrhythmia risk score, and/or the like.

[0069] A user, such as a clinician or patient 4, may interact with external device 12 through user interface 86. In some examples, a clinician or patient 4 may interact with external device 12 to view ECG data, one or more parameters, risk score(s), any alerts, and/or the like. User interface 86 includes a display (not shown), such as a liquid crystal display (LCD) or a light emitting diode (LED) display or other type of screen, with which processing circuitry 80 may present information related to IMD 10, e.g., ECG data, one or more parameters, risk score(s), any alerts, and/or the like. In addition, user interface 86 may include an input mechanism to receive input from the user. The input mechanisms may include, for example, any one or more of buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touch screen, or another input mechanism that allows the user to navigate through user interfaces presented by processing circuitry 80 of external device 12 and provide input. In other examples, user interface 86 also includes audio circuitry for providing audible notifications, instructions or other sounds to the user, receiving voice commands from the user, or both.

[0070] FIG. 5 is a block diagram illustrating an example system that includes an access point 91, a network 93, external computing devices, such as a server 95, and one or more other computing devices 101A-101N (collectively, “computing devices 101”), which may be coupled to IMD 10 and external device 12 via network 93, in accordance with one or more techniques described herein. In this example, IMD 10 may use communication circuitry 54 to communicate with external device 12 via a first wireless connection, and to communicate with an access point 91 via a second wireless connection. In the example of FIG. 5, access point 91, external device 12, server 95, and computing devices 101 are interconnected and may communicate with each other through network 93.

[0071] Access point 91 may include a device that connects to network 93 via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 91 may be coupled to network 93 through different forms of connections, including wired or wireless connections. In some examples, access point 91 may be a user device, such as a tablet or smartphone, that may be co-located with the patient. IMD 10 may be configured to transmit data, such as patient cardiac activity data and indications of episode data, and/or indications of changes in patient health, to access point 91. Access point 91 may then communicate the retrieved data to server 95 via network 93.

[0072] In some cases, server 95 may be configured to provide a secure storage site for data that has been collected from IMD 10 and/or external device 12. In some cases, server 95 may assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, via computing devices 101. One or more aspects of the illustrated system of FIG. 5 may be implemented with general network technology and functionality, which may be similar to that provided by the Medtronic CareLink® Network.

[0073] In some examples, one or more of computing devices 101 may be a tablet or other smart device located with a clinician, by which the clinician may program, receive alerts from, and/or interrogate IMD 10. For examples, IMD 10 and/or external device 12 may send alerts as described herein to one or more of computing devices 101. In some examples, computing device(s) 12 may transmit the alert to a care provider, an emergency medical technician, or other designated persons. For example, the alert may be a communication to the emergency medical technician, or local neighborhood alert system with an automated emergency defibrillator service, to a care provider, etc. In some examples, the alert includes collected data from IMD 10. In some examples, the alert includes at least one of a telephone call, a short message service message, an email, a web alert, a security system alert, a social media alert, an audible alert, or a visual alert.

[0074] For example, IMD 10 may send an audible warning to an loT device of computing devices 101A-101N, such as a smart speaker. In some examples, IMD 10 may send an alarm to a social media group or group email, for example, where there is a geographic or therapy relevance to patient 4.

[0075] For example, server 95 may be configured to transmit alert messages to one or more computing devices 101 associated with one or more care providers via network 93. Care providers may include emergency medical systems (EMS) and hospitals, and may include particular departments within a hospital, such as cardiac care department, an emergency department, catheterization lab, or a stroke response department. Computing devices 101 may include smartphones, desktop, laptop, or tablet computers, or workstations associated with such systems or entities, or employees of such systems or entities. The alert messages may include any of the data collected by IMD 10, and/or computing device(s) 12 including sensed physiological parameters and/or results of the analysis by IMD 10, computing device(s) 12 and/or server 95. The information transmitted from server 95 to computing devices 101 may improve the timeliness and effectiveness of treatment of patient 4 to reduce a likelihood of SCA and/or arrhythmia.

[0076] In some examples, server 95 (or another device of system 2, such as computing device 12) may be configured to transmit an alert message to a computing device of a caregiver or patient 4, which may prompt the caregiver or patient 4 to make an appointment with a clinician or to otherwise seek medical treatment.

[0077] In the example illustrated by FIG. 5, server 95 includes a storage device 97, e.g., to store data retrieved from IMD 10, and processing circuitry 99. Although not illustrated in FIG. 5 computing devices 101 may similarly include a storage device and processing circuitry. Processing circuitry 99 may include one or more processors that are configured to implement functionality and/or process instructions for execution within server 95. For example, processing circuitry 99 may be capable of processing instructions stored in storage device 97. Processing circuitry 99 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 99 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 99. Processing circuitry 99 of server 95 and/or the processing circuity of computing devices 101 may implement any of the techniques described herein to analyze information or data received from IMD 10, e.g., to determine a risk score for predicting an occurrence of arrhythmias and/or a risk score for an occurrence of sudden cardiac arrest.

[0078] Storage device 97 may include computer-readable storage media or computer- readable storage device(s). In some examples, storage device 97 includes one or more of short-term memories or long-term memories. Storage device 97 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM and/or EEPROM. In some examples, storage device 97 is used to store data indicative of instructions for execution by processing circuitry 99.

[0079] FIG. 6 is a flow diagram illustrating example risk score techniques according to one or more aspects of this disclosure. While described with respect to processing circuitry 50 of FIG. 2, the following techniques may be performed by any of, or any combination of, processing circuitry 50, processing circuitry 80, processing circuitry 99, processing circuitry of one or more of devices 101, and/or processing circuitry of other devices capable of performing such techniques.

[0080] Processing circuitry 50 may determine ECG features and monitor for arrhythmia and/or SCA event occurrences (200). For example, processing circuitry 50 may determine one or more of an absolute QTc interval value, a magnitude of change in QTc interval values over time, a heart rate variability value, a change in overall PVC burden over time, a relationship between monomorphic PVC occurrence and polymorphic PVC occurrence, a PVC burden of couplets, a PVC burden of triplets, a change in ST characteristics over time, a QRS width, or a change in QRS width over time. In some examples, processing circuitry 50 may determine other parameters, which may be used as arrhythmia and/or SCA predictors, such as QT alternans, a recovery rate after a PVC, etc. [0081] Processing circuitry 50 may determine an event risk score based on the ECG features (202). For example, processing circuitry 50 may calculate an arrhythmia risk score or an SCA event score based on the ECG features. ECG features may be indicative of cardiac health of patient 4. Certain ECG features, such as those discussed herein, may be useful in determining a likelihood of, or predicting whether an arrhythmia event or an SCA event may occur.

[0082] Processing circuitry 50 may determine whether the event risk score satisfies a threshold (204). For example, if a higher event risk score indicates a higher risk, processing circuitry 50 may determine whether the event risk score is greater than the threshold, or greater than or equal to the threshold to determine whether the event risk score satisfies the threshold. For example, if a lower event risk score indicates a higher risk, processing circuitry 50 may determine whether the event risk score is less than the threshold, or less than or equal to the threshold to determine whether the event risk score satisfies the threshold.

[0083] If the event risk score satisfies the threshold (the “YES” path from box 204), processing circuitry 50 may control communication circuitry 54 to send an alert (206). If the event risk score does not satisfy the threshold (the “NO” path from box 204), processing circuitry 50 may continue monitoring for arrhythmia and/or SCA event occurrences (208). [0084] Processing circuitry 50 may determine whether an event is detected (210). For example, processing circuitry 50 may determine whether an arrhythmia and/or an SCA event does occur. For example, processing circuitry 50 may utilize any of a number of current or future techniques for detecting an arrhythmia or an SCA event when determining whether an event is detected.

[0085] If processing circuitry 50 determine that an event is not detected (the “NO” path from box 210), processing circuitry 50 may continue monitoring for arrhythmia and/or SCA event occurrences (208). If processing circuitry 50 determines that an event is detected (the “YES” path from box 210), processing circuitry 50 may store ECG features from prior to the event (212). For example, processing circuitry 50 may store ECG features for a predetermined period of time (e.g., 15 minutes) prior to the detected event. [0086] Processing circuitry 50 may modify the event risk score calculation to more likely provide alerts in the future if similar ECG parameters are observed (214). For example, if a QTc value climbed to greater than 500 in the 15 minutes prior to the detected event, processing circuitry 50 may modify the calculation of the event risk score so that a QTc value that is greater than 500 is more heavily weighed for the future. In some examples, the event risk score calculation may be implemented using a machine learning model in IMD 10 and/or in server 95. For example, if the QTc value climbed to greater than 500 in the 15 minutes prior to the detected event, processing circuitry 50 may control communication circuitry 54 to send ECG data and/or parameters to server 95 and processing circuitry 99 may modify the event risk score calculation. In some examples, the modification of the event risk score calculation may be an example of training the machine learning model. In some examples, the modification of the event risk score may be processing circuitry 50 may modify the calculation of the event risk score by sending may Processing circuitry 50 may again determine ECG features and monitor for arrhythmia and/or SCA event occurrences (200).

[0087] FIG. 7 is a flow diagram illustrating other example risk score techniques according to one or more aspects of this disclosure. While described with respect to processing circuitry 50 of FIG. 2, the following techniques may be performed by any of, or any combination of, processing circuitry 50, processing circuitry 80, processing circuitry 99, processing circuitry of one or more of devices 101, and/or processing circuitry of other devices capable of performing such techniques.

[0088] Processing circuitry 50 may determine one or more first parameters based on continuous ECG data (300). The one or more parameters may include at least one of an absolute QTc interval value or a magnitude of change in QTc interval values over time. In some examples, the one or more parameters may further include one or more of, a heart rate variability value, a change in overall PVC burden over time, a relationship between monomorphic PVC occurrence and polymorphic PVC occurrence, a PVC burden of couplets, a PVC burden of triplets, a change in ST characteristics over time, a QRS width, or a change in QRS width over time.

[0089] Processing circuitry 50 may predict a risk of at least one of an arrhythmia event or an SCA event based on the one or more first parameters (302). For example, processing circuitry 50 may determine a risk score based on the one or more first parameters. [0090] Processing circuitry 50 may determine that the risk satisfies a threshold (304). In an example where a higher risk score is indicative of a higher risk of an arrhythmia event or an SCA event, processing circuitry 50 may determine that the risk satisfies the threshold by determining the risk score is greater than or greater than or equal to the threshold. In an example where a lower risk score is indicative of a higher risk of an arrhythmia event or an SCA event, processing circuitry 50 may determine the risk satisfies the threshold by the risk score being lower than, or lower than or equal to, the threshold.

[0091] Processing circuitry 50 may, based on the risk satisfying a threshold, send an alert indicative of the risk (306). For example, processing circuitry 50 may control communication circuitry 54 to send the alert to external device 12, server 95, and/or any of computing devices 101.

[0092] In some examples, the one or more first parameters include at least one of an absolute QTc interval value, a magnitude of change in QTc interval values over time, a heart rate variability value, a change in overall PVC burden over time, a relationship between monomorphic PVC occurrence and polymorphic PVC occurrence, a PVC burden of couplets, a PVC burden of triplets, a change in ST characteristics over time, a QRS width, or a change in QRS width over time. In some examples, processing circuitry 50 may predict the risk further based on one or more second parameters. In some examples, processing circuitry 50 may determine the one or more second parameters. In some examples, the one or more second parameters include at least one of sleep apnea burden, activity, or blood pressure.

[0093] In some examples, processing circuitry 50 may predict the risk of the SCA event. In some examples, the threshold includes an SCA threshold. In some examples, as part of predicting the risk SCA event, processing circuitry 50 may determine an SCA risk score. In some examples, processing circuitry 50 may determine the SCA risk score based on the one or more first parameters within a period of time of determining the SCA risk score, the period of time being less than or equal to 24 hours. In some examples, the period of time may be programmable, for example, by a clinician. In some examples, the period of time may be different for different of the first parameters. For example, a QTc interval value may be associated with a shorter period of time, whereas a parameter such as PVC burden may be associated with a longer time period. [0094] In some examples, processing circuitry 50 may predict the risk of the arrhythmia event. In some examples, the threshold includes an arrhythmia threshold. In some examples, as part of predicting the risk arrhythmia event, processing circuitry 50 may determine an arrhythmia risk score. In some examples, processing circuitry 50 may determine both an SCA risk score and an arrhythmia risk score.

[0095] In some examples, as part of determining the at least one of the SCA risk score or the arrhythmia risk score, processing circuitry 50 may apply a respective weight to each of the one or more parameters. In some examples, processing circuitry 50 may determine an occurrence of at least one of an arrhythmia event or an SCA event. In some examples, processing circuitry 50 may determine at least one parameter of the one or more parameters associated with the arrhythmia event or the SCA event. In some examples, processing circuitry 50 may change the respective weight associated with the at least one parameter. [0096] In some examples, processing circuitry 50 may determine an occurrence of at least one of a first arrhythmia event or a first SCA event. In some examples, processing circuitry 50 may determine a parameter of the one or more parameters associated with the first arrhythmia event or the first SCA event. In some examples, processing circuitry 50 may determine a first property of the parameter prior to the first arrhythmia event or the first SCA event as a first threshold. In some examples, processing circuitry 50 may determine, at a time subsequent to the first arrhythmia event or the first SCA event, that a second property of the parameter satisfies the first threshold. In some examples, processing circuitry 50 may, based on the second property of the parameter satisfying the first threshold, send an alert indicative of a risk of at least one of a second arrhythmia event or a second SCA event.

[0097] While the techniques herein are described as being performed by various elements, such as sensing circuitry 52 and processing circuitry 50, in some examples, other elements or a combination of elements may perform the techniques. For example, sensing circuitry 52 may perform techniques described as being performed by processing circuitry 50, processing circuitry 50 may perform techniques described as being performed by sensing circuitry 52, or a combination of sensing circuitry 52 and processing circuitry 50 may perform techniques described as being performed by either.

[0098] The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the techniques may be implemented within one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic QRS circuitry, as well as any combinations of such components, embodied in external devices, such as physician or patient programmers, stimulators, or other devices. The terms “processor,” “processing circuitry,” “controller” or “control module” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry, and alone or in combination with other digital or analog circuitry.

[0099] For aspects implemented in software, at least some of the functionality ascribed to the systems and devices described in this disclosure may be embodied as instructions on a non-transitory computer-readable storage medium such as RAM, ROM, NVRAM, EEPROM, FLASH memory, magnetic media, optical media, or the like. The instructions may be executed to support one or more aspects of the functionality described in this disclosure.

[0100] This disclosure includes the following non-limiting examples.

[0101] Example 1. A system comprising: an insertable cardiac monitoring device, the insertable cardiac monitoring device comprising: a housing configured to be subcutaneously inserted into a patient, the housing comprising a cover; a plurality of electrodes, at least one of the plurality of electrodes being disposed on a proximal portion of the cover and at least another one of the plurality of electrodes being disposed on a distal portion of the cover; sensing circuitry configured to sense continuous electrocardiogram (ECG) data based on electrical activity of a heart of the patient via the plurality of electrodes; one or more memories configured to store the continuous ECG data, the continuous ECG data being sensed on a beat-by-beat basis; and processing circuitry coupled to the one or more memories and configured to: determine one or more first parameters based on the continuous ECG data, the one or more first parameters comprising at least one of an absolute corrected QT (QTc) interval value or a magnitude of change in QTc interval values over time; predict a risk of at least one of an arrhythmia event or a sudden cardiac arrest (SCA) event based on the one or more first parameters; determine that the risk satisfies a threshold; and based on the risk satisfying the threshold, send an alert indicative of the risk.

[0102] Example 2. The system of example 1, wherein the one or more first parameters further comprise at least one of a heart rate variability value, a change in overall premature ventricular contraction (PVC) burden over time, a relationship between monomorphic PVC occurrence and polymorphic PVC occurrence, a PVC burden of couplets, a PVC burden of triplets, a change in ST characteristics over time, a QRS width, or a change in QRS width over time.

[0103] Example 3. The system of example 1 or example 2, wherein predicting the risk is further based on one or more second parameters and wherein the processing circuitry is further configured to determine the one or more second parameters, the one or more second parameters comprising at least one of sleep apnea burden, activity, or blood pressure.

[0104] Example 4. The system of any of examples 1-3, wherein the processing circuitry is configured to predict the risk of the SCA event, wherein the threshold comprises an SCA threshold, and wherein as part of predicting the risk SCA event, the processing circuitry is configured to determine an SCA risk score.

[0105] Example 5. The system of example 4, wherein the processing circuitry is configured to determine the SCA risk score based on the one or more first parameters within a period of time of determining the SCA risk score, the period of time being less than or equal to 24 hours.

[0106] Example 6. The system of any of examples 1-5, wherein the processing circuitry is configured to predict the risk of the arrhythmia event, wherein the threshold comprises an arrhythmia threshold, and wherein as part of predicting the risk of the arrhythmia event, the processing circuitry is configured to determine an arrhythmia risk score.

[0107] Example 7. The system of any of examples 4-6, wherein the processing circuitry is configured to determine both an SCA risk score and an arrhythmia risk score. [0108] Example 8. The system of any of examples 4-7, wherein as part of determining the at least one of the SCA risk score or the arrhythmia risk score, the processing circuitry is configured to apply a respective weight to each of the one or more first parameters.

[0109] Example 9. The system of example 8, wherein the processing circuitry is further configured to: determine an occurrence of at least one of an arrhythmia event or an SCA event; determine at least one parameter of the one or more first parameters associated with the arrhythmia event or the SCA event; and change the respective weight associated with the at least one parameter. [0110] Example 10. The system of any of examples 1-9, wherein the processing circuitry is further configured to: determine an occurrence of at least one of a first arrhythmia event or a first SCA event; determine a parameter of the one or more first parameters associated with the first arrhythmia event or the first SCA event; determine a first property of the parameter prior to the first arrhythmia event or the first SCA event as a first threshold; determine, at a time subsequent to the first arrhythmia event or the first SCA event, that a second property of the parameter satisfies the first threshold; and based on the second property of the parameter satisfying the first threshold, send an alert indicative of a risk of at least one of a second arrhythmia event or a second SCA event.

[0111] Example 11. A method comprising: determining, by processing circuitry, one or more first parameters based on continuous ECG data, the continuous ECG data being sensed by an insertable cardiac monitoring device on a beat-by-beat basis, and the one or more first parameters comprising at least one of an absolute corrected QT (QTc) interval value or a magnitude of change in QTc interval values over time; predicting, by the processing circuitry, a risk of at least one of an arrhythmia event or a sudden cardiac arrest (SCA) event based on the one or more first parameters; determining, by the processing circuitry, that the risk satisfies a threshold; and sending, by the processing circuitry and based on the risk satisfying the threshold, an alert indicative of the risk.

[0112] Example 12. The method of example 11, wherein the one or more first parameters further comprise at least one of a heart rate variability value, a change in overall premature ventricular contraction (PVC) burden over time, a relationship between monomorphic PVC occurrence and polymorphic PVC occurrence, a PVC burden of couplets, a PVC burden of triplets, a change in ST characteristics over time, a QRS width, or a change in QRS width over time.

[0113] Example 13. The method of example 11 or example 12, wherein predicting the risk is further based on one or more second parameters and wherein the method further comprises determining, by the processing circuitry, the one or more second parameters, the one or more second parameters comprising at least one of sleep apnea burden, activity, or blood pressure.

[0114] Example 14. The method of any of examples 11-13, wherein the method includes predicting the risk of the SCA event, wherein the threshold comprises an SCA threshold, and wherein predicting the risk SCA event comprises determining an SCA risk score.

[0115] Example 15. The method of example 14, wherein the method includes determining the SCA risk score based on the one or more first parameters within a period of time of determining the SCA risk score, the period of time being less than or equal to 24 hours.

[0116] Example 16. The method of any of examples 11-15, wherein the method includes predicting the risk of the arrhythmia event, wherein the threshold comprises an arrhythmia threshold, and wherein predicting the risk of the arrhythmia event comprises determining an arrhythmia risk score.

[0117] Example 17. The method of any of examples 14-16, wherein the method comprises determining both an SCA risk score and an arrhythmia risk score.

[0118] Example 18. The method of any of examples 14-17, wherein determining the at least one of the SCA risk score or the arrhythmia risk score comprises applying a respective weight to each of the one or more first parameters.

[0119] Example 19. The method of example 18, further comprising: determining an occurrence of at least one of an arrhythmia event or an SCA event; determining at least one parameter of the first one or more parameters associated with the arrhythmia event or the SCA event; and changing the respective weight associated with the at least one parameter.

[0120] Example 20. Non-transitory computer-readable storage media storing instructions, which when executed by processing circuitry, cause the processing circuitry to: determine one or more first parameters based on continuous ECG data, the continuous ECG data being sensed by an insertable cardiac monitoring device on a beat-by-beat basis, and the one or more first parameters comprising at least one of an absolute corrected QT (QTc) interval value or a magnitude of change in QTc interval values over time; predict a risk of at least one of an arrhythmia event or a sudden cardiac arrest (SCA) event based on the one or more first parameters; determine that the risk satisfies a threshold; and send, based on the risk satisfying the threshold, an alert indicative of the risk.

[0121] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

CLAIMS: What is claimed is:

1. A system comprising: an insertable cardiac monitoring device, the insertable cardiac monitoring device comprising: a housing configured to be subcutaneously inserted into a patient, the housing comprising a cover; a plurality of electrodes, at least one of the plurality of electrodes being disposed on a proximal portion of the cover and at least another one of the plurality of electrodes being disposed on a distal portion of the cover; sensing circuitry configured to sense continuous electrocardiogram (ECG) data based on electrical activity of a heart of the patient via the plurality of electrodes; one or more memories configured to store the continuous ECG data, the continuous ECG data being sensed on a beat-by-beat basis; and processing circuitry coupled to the one or more memories and configured to: determine one or more first parameters based on the continuous ECG data, the one or more first parameters comprising at least one of an absolute corrected QT (QTc) interval value or a magnitude of change in QTc interval values over time; predict a risk of at least one of an arrhythmia event or a sudden cardiac arrest (SCA) event based on the one or more first parameters; determine that the risk satisfies a threshold; and based on the risk satisfying the threshold, send an alert indicative of the risk.

2. The system of claim 1, wherein the one or more first parameters further comprise at least one of a heart rate variability value, a change in overall premature ventricular contraction (PVC) burden over time, a relationship between monomorphic PVC occurrence and polymorphic PVC occurrence, a PVC burden of couplets, a PVC burden of triplets, a change in ST characteristics over time, a QRS width, or a change in QRS width over time.

3. The system of claim 1 or claim 2, wherein predicting the risk is further based on one or more second parameters and wherein the processing circuitry is further configured to determine the one or more second parameters, the one or more second parameters comprising at least one of sleep apnea burden, activity, or blood pressure.

4. The system of any of claims 1-3, wherein the processing circuitry is configured to predict the risk of the SCA event, wherein the threshold comprises an SCA threshold, and wherein as part of predicting the risk SCA event, the processing circuitry is configured to determine an SCA risk score.

5. The system of claim 4, wherein the processing circuitry is configured to determine the SCA risk score based on the one or more first parameters within a period of time of determining the SCA risk score, the period of time being less than or equal to 24 hours.

6. The system of any of claims 1-5, wherein the processing circuitry is configured to predict the risk of the arrhythmia event, wherein the threshold comprises an arrhythmia threshold, and wherein as part of predicting the risk of the arrhythmia event, the processing circuitry is configured to determine an arrhythmia risk score.

7. The system of any of claims 4-6, wherein the processing circuitry is configured to determine both an SCA risk score and an arrhythmia risk score.

8. The system of any of claims 4-7, wherein as part of determining the at least one of the SCA risk score or the arrhythmia risk score, the processing circuitry is configured to apply a respective weight to each of the one or more first parameters.

9. The system of claim 8, wherein the processing circuitry is further configured to: determine an occurrence of at least one of an arrhythmia event or an SCA event; determine at least one parameter of the one or more first parameters associated with the arrhythmia event or the SCA event; and change the respective weight associated with the at least one parameter.

10. The system of any of claims 1-9, wherein the processing circuitry is further configured to: determine an occurrence of at least one of a first arrhythmia event or a first SCA event; determine a parameter of the one or more first parameters associated with the first arrhythmia event or the first SCA event; determine a first property of the parameter prior to the first arrhythmia event or the first SCA event as a first threshold; determine, at a time subsequent to the first arrhythmia event or the first SCA event, that a second property of the parameter satisfies the first threshold; and based on the second property of the parameter satisfying the first threshold, send an alert indicative of a risk of at least one of a second arrhythmia event or a second SCA event.

11. A method comprising: determining, by processing circuitry, one or more first parameters based on continuous ECG data, the continuous ECG data being sensed by an insertable cardiac monitoring device on a beat-by-beat basis, and the one or more first parameters comprising at least one of an absolute corrected QT (QTc) interval value or a magnitude of change in QTc interval values over time; predicting, by the processing circuitry, a risk of at least one of an arrhythmia event or a sudden cardiac arrest (SCA) event based on the one or more first parameters; determining, by the processing circuitry, that the risk satisfies a threshold; and sending, by the processing circuitry and based on the risk satisfying the threshold, an alert indicative of the risk.

12. The method of claim 11, wherein the one or more first parameters further comprise at least one of a heart rate variability value, a change in overall premature ventricular contraction (PVC) burden over time, a relationship between monomorphic PVC occurrence and polymorphic PVC occurrence, a PVC burden of couplets, a PVC burden of triplets, a change in ST characteristics over time, a QRS width, or a change in QRS width over time.

13. The method of claim 11 or claim 12, wherein predicting the risk is further based on one or more second parameters and wherein the method further comprises determining, by the processing circuitry, the one or more second parameters, the one or more second parameters comprising at least one of sleep apnea burden, activity, or blood pressure.

14. The method of any of claims 11-13, wherein the method includes predicting the risk of the SCA event, wherein the threshold comprises an SCA threshold, and wherein predicting the risk SCA event comprises determining an SCA risk score.

15. Computer-readable storage media storing instructions, which when executed by processing circuitry, cause the processing circuitry' to perform the method of any of claims 11 -14.