Abstract: A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

Abstract: This disclosure is directed to systems and techniques configured to apply at least one criterion to health event data stored in a record for a patient for determining whether to remove at least a portion of the health event data from the record or retain that portion as an accurate reflection of patient health for that point-in-time. The health event data includes adjudicated health events and non-adjudicated health events over a first time period. Based on a determination that the health event data satisfies the at least one criterion, the example technique may direct the example system to remove the health event data corresponding to the adjudicated health events and the non-adjudicated health events from the record and then, adjust longitudinal diagnostic information of a second time period that includes the first time period.

Abstract: Techniques are described for initiating a change to rules used by a medical device to identify a plurality of episodes based on a determination that an amount of the plurality of episodes classified as a classification for which transmission of the episode data from the medical device to the computing device was unnecessary satisfies at least one criterion.

Abstract: A method comprises applying, by processing circuitry of a system comprising a medical device, an ensemble of classifiers to episode data for a ventricular tachyarrhythmia episode detected by the medical device based on electrocardiogram sensed by the medical device. The method further comprises classifying, by the processing circuitry, the ventricular tachyarrhythmia episode as one of a plurality of classifications based on the application of the ensemble of classifiers to the episode data, wherein the plurality of classifications include two or more of noise, oversensing, supraventricular tachycardia, polymorphic ventricular tachycardia, monomorphic ventricular tachycardia, and ventricular fibrillation.

Abstract: Devices, systems, and techniques are disclosed for determining the likelihood that a cardiac event will self-terminate. An example technique includes determining, by processing circuitry and based on current sensed physiological parameters of a patient, that a cardiac event is occurring in the patient. The example technique includes determining, by the processing circuitry- and based on the current sensed physiological parameters of the patient, that the cardiac event is unlikely to self-terminate within a predetermined period of time. The example technique includes, in response to determining that the cardiac event is unlikely- to self-terminate, deliver therapy to the patient or issue an alert.

Abstract: A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

Abstract: Devices, systems, and techniques for detecting a sudden cardiac event based on respiratory parameter information. A method includes receiving periodic respiratory parameter information, where the respiratory parameter information includes respiratory effort of a patient; and determining, by the processing circuitry and based on the respiratory parameter information, whether a sudden cardiac arrest of the patient is detected.

Abstract: A device comprising a computer-readable medium having executable instructions stored thereon, configured to be executable by processing circuitry for causing the processing circuitry to: determine that a patient is experiencing or has experienced an acute health event; cause a motor to move a robotic device to a location proximate the patient; cause a sensor of the robotic device to gather physiological data from the patient; confirm that the patient is experiencing or has experienced the acute health event based on the physiological data; and generate an output in response to confirming that the patient is experiencing or has experienced the acute health event.

Abstract: Example devices, systems, and techniques are disclosed for providing guidance of a treatment of a patient. An example device includes processing circuitry and memory comprising instructions that, when executed by the processing circuitry, cause the processing circuitry to determine that a device detected an acute health event of a patient or delivery of cardiopulmonary resuscitation to the patient, and analyze sensed patient data in response to the determination. The instructions cause the processing circuitry to provide information for guidance of a treatment of the patient based on the analysis.

Abstract: Devices, systems, and techniques are disclosed for verifying the occurrence of an acute health event. An example device includes communication circuitry configured to receive a communication indicative of an acute health event of a patient and memory communicatively coupled to the communication circuitry and being configured to store the indication of the acute health event. The device includes processing circuitry communicatively coupled to the communication circuitry and the memory. The processing circuitry is configured to, in response to the communication, verify the acute health event and based on the verification of the acute health event, send an alert regarding the acute health event.

Abstract: An example device of a patient includes an antenna configured to wirelessly receive communication from a medical device; and processing circuitry coupled to the antenna and configured to: determine that the received communication indicates that a patient is experiencing an acute health event; in response to the determination, determine one or more physical states of the patient based on sensed data from one or more sensors; confirm that the patient is not experiencing the acute health event based on the determined one or more physical states; and output information based on the confirmation that the patient is not experiencing the acute health event.

Abstract: Techniques for transmitting diagnostic information stored in an implantable medical device (IMD) based on patient hospitalization are described. For example, the IMD may transmit higher resolution diagnostic information to a clinician and/or an external device during a hospitalization period to aid the clinician in evaluating heart failure treatment and when discharge is proper. This higher resolution diagnostic information may include one or more patient metrics automatically generated and transmitted by the IMD at least once every two hours. During a post-hospitalization period, the IMD may transmit lower resolution diagnostic information to a clinician that indicates a risk level of re-hospitalization. The lower resolution diagnostic information may include the risk level and/or patient metrics once a day, for example. In this manner, the IMD transmitted diagnostic information may be tailored to the specific heart failure monitoring needed by the patient.

Abstract: An example device of a patient includes an antenna configured to wirelessly receive communication from a medical device; and processing circuitry coupled to the antenna and configured to: determine that the received communication indicates that a patient is experiencing an acute health event; in response to the determination, determine one or more physical states of the patient based on sensed data from one or more sensors; confirm that the patient is not experiencing the acute health event based on the determined one or more physical states; and output information based on the confirmation that the patient is not experiencing the acute health event.

Abstract: This disclosure is directed to systems and techniques for detecting change in patient health and if a change in patient health is detected, direct a medical device to generate for display output indicating the detection of the change in patient health. An example medical system or technique applies a model to values of configurable settings that are programmed into detection logic of a medical device; based on the application, determine whether modified values of the configurable settings, when implemented by the detection logic, would change a determination, by the medical device, regarding whether sensed physiological activity is indicative of cardiac episode for a patient; and in response to a determination that the modified values would change the determination regarding whether the sensed physiological activity is indicative of the cardiac episode for the patient, generate output data indicative of the modified values for the configurable settings for the medical device.

Abstract: This disclosure is directed to a medical system and technique for a filter-based approach to arrhythmia detection. In one example, the medical system comprises one or more sensors configured to sense physiological parameter(s); sensing circuitry configured to generate patient data based on the sensed physiological parameter(s), the patient data comprising signal data to represent cardiac activity of the patient; and processing circuitry configured to: detect a cardiac arrhythmia for the patient based on a classification of the signal data in accordance with a machine learning model, wherein the machine learning model comprises filter(s) for at least one portion of the signal data, wherein the at least one filter corresponds to a feature set that maps to the cardiac activity represented by the portion(s) of the signal data; and generate for display output data indicative of a positive detection of the cardiac arrhythmia.

Abstract: This disclosure describes techniques for bypassing an algorithm configured to determine a likelihood of episode data being a false indication of a cardiac episode. A medical device system includes processing circuitry configured to receive episode data and determine, based on satisfaction of one or more bypass conditions of a set of bypass conditions, whether to bypass the algorithm. Responsive to bypassing the algorithm, the processing circuitry stores the episode data as a true indication of the cardiac episode.

Abstract: A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

Abstract: A system comprising processing circuitry configured to receive a wirelessly-transmitted message from a medical device, the message indicating that the medical device detected an acute health event of the patient. In response to the message, the processing circuitry is configured to determine a location of the patient, determine an alert area based on the location of the patient, and control transmission of an alert of the acute heath event of the patient to any one or more computing devices of one or more potential responders within the alert area.

Abstract: An example device of a patient includes an antenna configured to wirelessly receive communication from a medical device; and processing circuitry coupled to the antenna and configured to: determine that the received communication indicates that a patient is experiencing an acute health event; in response to the determination, determine one or more physical states of the patient based on sensed data from one or more sensors; confirm that the patient is not experiencing the acute health event based on the determined one or more physical states; and output information based on the confirmation that the patient is not experiencing the acute health event.

Abstract: Techniques for transmitting diagnostic information stored in an implantable medical device (IMD) based on patient hospitalization are described. For example, the IMD may transmit higher resolution diagnostic information to a clinician and/or an external device during a hospitalization period to aid the clinician in evaluating heart failure treatment and when discharge is proper. This higher resolution diagnostic information may include one or more patient metrics automatically generated and transmitted by the IMD at least once every two hours. During a post-hospitalization period, the IMD may transmit lower resolution diagnostic information to a clinician that indicates a risk level of re-hospitalization. The lower resolution diagnostic information may include the risk level and/or patient metrics once a day, for example. In this manner, the IMD transmitted diagnostic information may be tailored to the specific heart failure monitoring needed by the patient.