Abstract: Systems and methods for detecting a physiological event or estimating a physiological parameter using ambulatory electrograms of a subject are discussed. An exemplary system includes a computing device that can receive ambulatory electrograms collected by an ambulatory medical device (AMD) associated with a subject, and apply the ambulatory electrograms to a trained machine learning model to estimate a physiological parameter or to detect a physiological event in the subject. The same or a different machine learning model can be trained to detect an operating status of the AMD using the ambulatory electrograms. The system comprises an output device to output the estimated physiological parameter, the detected physiological event, or the detected device operating status a user or a process such as to initiate or titrate a therapy.

Abstract: Systems and methods to predict a patient mortality risk are disclosed, including determining a risk value for a plurality of physiologic measures and determining a mortality risk metric indicative of a risk of patient mortality as a weighted combination of the plurality of physiologic measures having risk values satisfying a pre-determined condition.

Abstract: Systems and methods to determine an indication of patient dehydration are disclosed, including receiving first and second physiologic information of a patient, the first physiologic information including heart sound information of the patient and the second physiologic information different than the first physiologic information, and determining the indication of patient dehydration using the received first and second physiologic information.

Abstract: An example of a system for monitoring and treating respiratory distress in a patient may include signal inputs, a signal processing circuit, and a respiratory distress analyzer. The signal inputs may be configured to receive patient condition signals indicative of autonomic balance of the patient. The signal processing circuit may be configured to process the patient condition signals and to generate patient condition parameters indicative of the autonomic balance using the processed patient condition signals. The respiratory distress analyzer may be configured to determine a state of the respiratory distress using the patient condition parameters, and may include a parameter analysis circuit configured to analyze the autonomic balance of the patient and to determine the state of the respiratory distress using an outcome of the analysis.

Abstract: Systems and methods for monitoring patient vasoactivity are discussed. An exemplary patient monitor system includes a sensor circuit configured to generate a heart sound (HS) metric using a HS signal sensed from a patient, and a vasoactivity monitor configured to monitor vasoactivity, such as degree of vasoconstriction or vasodilation, using the HS metric. The system can provide the monitored vasoactivity to a user to alert patient hemodynamic responses to vasoactive drugs, or initiate or adjust a vasoactive therapy according to the vasoactivity. The system may use the monitored vasoactivity to detect a medical condition such as worsening heart failure, pulmonary edema, or syncope.

Abstract: Systems and methods for detecting worsening cardiac conditions such as worsening heart failure events are described. A system may include sensor circuits to sense physiological signals and signal processors to generate from the physiological signals first and second signal metrics. The system may include a risk stratifier circuit to produce a cardiac risk indication. The system may use at least the first signal metric to generate a primary detection indication, and use at least the second signal metric and the risk indication to generate a secondary detection indication. The risk indication may be used to modulate the second signal metric. A detector circuit may detect the worsening cardiac event using the primary and secondary detection indications.

Abstract: Systems and methods for monitoring patients with multiple chronic diseases are described. A system may include a health status monitor that receives diagnostic data including physiological signals sensed from a patient. The system may produce at least a first risk indication of the patient developing a first disease and a second risk indication of the patient developing a different second disease. The system may detect the first and second diseases from the physiological signals, and generate a composite health status indicator using the detections of the first and second diseases and the first and second risk indications. An alert of worsening health status may be generated if the composite detection score exceeds an alert threshold.

Abstract: Systems and methods for treating a medical condition such as worsening heart failure (WHF) are described. A medical system may sense one or more physiological signals, and generate from the sensed physiological signals a signal metric trend indicating a progression of heart failure. A detector may detect a physiological event leading to WHF. A therapy control circuit may generate a therapy titration protocol using the generated signal metric trend. The therapy titration protocol includes a temporal profile of therapy dosage relative to a target dosage. The therapy control circuit may adjust the target dosage based on patient response. Therapies may be administered by a clinician or automatically delivered to the patient according to the therapy titration protocol.

Abstract: Systems and methods for detecting worsening cardiac conditions such as worsening heart failure events are described. A system may include sensor circuits to sense physiological signals and signal processors to generate from the physiological signals first and second signal metrics. The system may include a risk stratifier circuit to produce a cardiac risk indication. The system may use at least the first signal metric to generate a primary detection indication, and use at least the second signal metric and the risk indication to generate a secondary detection indication. The risk indication may be used to modulate the second signal metric. A detector circuit may detect the worsening cardiac event using the primary and secondary detection indications.

Abstract: Systems and methods for monitoring patients with a chronic disease are described. A patient management system may sense physiological signals from a patient using one or more implantable or other ambulatory sensors, and generate from the physiological signals a chronobiological rhythm indicator (CRI) such as indicating a circadian rhythm. A reference CRI associated with a prior hospital admission event of the patient may be provided to the patient management system, which compares the CRI to the reference CRI and generates a readmission risk score indicating the patient's risk of subsequent hospital readmission due to a worsened condition of the chronic disease. The readmission risk score may be provided to a user or a process, or used to initiate or adjust a therapy delivered to the patient.

Abstract: Systems and methods for assessing a patient's risk of renal dysfunction are described. A system may include sensor circuits to sense physiological signals and processors to generate signal metrics from the physiological signals. The system may generate a primary renal risk indication using a first signal metric, and a secondary renal risk indication using at least a second signal metric. The system may generate a composite renal risk indication and estimate a glomerular filtration rate or a chronic kidney disease stage using at least the primary and secondary risk indications. The composite renal risk indication, which indicative of a degree of renal dysfunction, may be presented to a clinician, or provided to a detector for detecting worsening heart failure.

Abstract: This document discusses, among other things, systems and methods to determine an indication of discharge readiness for a patient using received physiologic information of the patient corresponding to hospitalization of the patient and received physiologic information of the patient corresponding to a time after hospitalization.

Abstract: Systems and methods for monitoring patients with multiple chronic diseases are described. A system may include a health status monitor that receives diagnostic data including physiological signals sensed from a patient. The system may produce at least a first risk indication of the patient developing a first disease and a second risk indication of the patient developing a different second disease. The system may detect the first and second diseases from the physiological signals, and generate a composite health status indicator using the detections of the first and second diseases and the first and second risk indications. An alert of worsening health status may be generated if the composite detection score exceeds an alert threshold.

Abstract: Systems and methods for monitoring patients with a chronic disease are described. A patient management system may sense physiological signals from a patient using one or more implantable or other ambulatory sensors, and generate from the physiological signals a chronobiological rhythm indicator (CRI) such as indicating a circadian rhythm. A reference CRI associated with a prior hospital admission event of the patient may be provided to the patient management system, which compares the CRI to the reference CRI and generates a readmission risk score indicating the patient's risk of subsequent hospital readmission due to a worsened condition of the chronic disease. The readmission risk score may be provided to a user or a process, or used to initiate or adjust a therapy delivered to the patient.

Abstract: Systems and methods for treating a medical condition such as worsening heart failure (WHF) are described. A medical system may sense one or more physiological signals, and generate from the sensed physiological signals a signal metric trend indicating a progression of heart failure. A detector may detect a physiological event leading to WHF. A therapy control circuit may generate a therapy titration protocol using the generated signal metric trend. The therapy titration protocol includes a temporal profile of therapy dosage relative to a target dosage. The therapy control circuit may adjust the target dosage based on patient response. Therapies may be administered by a clinician or automatically delivered to the patient according to the therapy titration protocol.

Abstract: Systems and methods for detecting worsening cardiac conditions such as worsening heart failure events are described. A system may include sensor circuits to sense physiological signals and signal processors to generate from the physiological signals first and second signal metrics. The system may include a risk stratifier circuit to produce a cardiac risk indication. The system may use at least the first signal metric to generate a primary detection indication, and use at least the second signal metric and the risk indication to generate a secondary detection indication. The risk indication may be used to modulate the second signal metric. A detector circuit may detect the worsening cardiac event using the primary and secondary detection indications.

Abstract: This document discusses, among other things, systems and methods to generate a first pacing waveform during a first pacing period and a second pacing waveform during a second pacing period, to alternate first and second pacing periods to provide pacing-based hypertension therapy to a heart of a patient to reduce patient blood pressure, to receive information indicative of patient metabolic demand, and to determine an adjusted pacing-based hypertension therapy parameter using the received information indicative of patient metabolic demand, wherein the first pacing waveform has a first atrioventricular (AV) delay and the second pacing waveform has a second AV delay longer than the first AV delay.

Abstract: Systems and methods are disclosed herein, comprising a risk analysis module configured to determine a heart failure (HF) risk score for a subject using an S3 heart sound parameter of the subject and a control module configured to calculate a worsening heart failure (WHF) score for the subject using a HF parameter, wherein the control module is configured to enable a logistic regression detection of the WHF score if the determined HF risk score is in a first HF risk score range and to enable a neural network detection of the WHF score if the determined HF risk score is in a second HF risk score range.

Abstract: Systems and methods for monitoring patients with a chronic disease such as heart failure are disclosed. The system may include a physiological sensor circuit to sense physiological signals and generate signal metrics from the physiological signals. The system may include a health status analyzer circuit to use the signal metrics to generate one or more stability indicators of patient health status, such as stability of heart failure status. The system may additionally generate one or more health status indicators indicating patient health status such as heart failure progression. A patient disposition decision may be generated using the health status indicators and the stability indicators to provide an indication of readiness for patient discharge from or a risk of admission to a hospital.

Abstract: Systems and methods provide for assessing the heart failure status of a patient and, more particularly, to generating a trend parameter based on a distribution of the patient's respiration rate. Systems and methods provide for detecting, using an implantable device or a patient-external device, patient respiration and computing a respiration rate based on the detected patient respiration. A distribution of the respiration rate is calculated, and a trend parameter based on the respiration rate distribution is generated. The trend parameter is indicative of a patient's heart failure status. An output signal indicative of the patient's heart failure status may be generated based on the trend parameter.