Abstract: Systems and methods to improve patient monitoring and device operation during a near-implant time period are disclosed, including determining a representative value of physiologic information of a patient sensed in a near-implant time period after implant of an implantable medical device in the patient, determining an absolute or hybrid baseline corresponding to the near-implant time period using information from at least one of an imputed baseline corresponding to a pre-implant time period preceding implant of the implantable medical device in the patient or an initial value of the received physiologic information of the patient in an initial portion of the near-implant time period, and determining an indication of patient condition as a function of the determined representative value and the determined absolute or hybrid baseline.

Abstract: Systems and methods to improve patient monitoring and device operation during a near-implant time period are disclosed, including determining a representative value of received physiologic information of a patient sensed by an implantable medical device implanted in the patient, determining a hybrid or relative baseline for the patient using the received physiologic information, and determining an indication of patient condition as a weighted function of (1) the representative value of the received physiologic information, (2) an absolute baseline, and (3) the hybrid or relative determined relative baseline, with a weight of at least one the determined absolute baseline or the determined relative baseline changing with time relative to a time of implant of the implantable medical device.

Abstract: Systems and methods for assessing a cardiac arrhythmia risk of a patient, such as a risk for developing atrial fibrillation, are disclosed. An exemplary medical-device system includes a risk stratifier circuit configured to, in an absence of prior and present atrial arrhythmia, determine a composite risk of the patient developing a future atrial arrhythmia using a trained machine-learning model and a plurality of features of physiological information sensed from the patient and an arrhythmia monitor circuit configured to adjust an arrhythmia monitoring parameter based at least in part on the composite risk and to detect an atrial arrhythmia event using the adjusted arrhythmia monitoring parameter.

Abstract: Systems and methods are disclosed to a medical device system comprising a cardiac signal sensing circuit, a heart sound sensor, a signal processing circuit, and a control circuit. The cardiac signal sensing circuit produces a sensed cardiac signal representative of cardiac depolarization signals when connected to electrodes. The heart sound sensor produces a sensed heart sound signal representative of vibrational sounds of the heart. The signal processing circuit identifies premature ventricular contractions (PVCs) using sensed cardiac signal information. The control circuit is configured to calculate a PVC burden of the patient using PVC information, determine that the calculated PVC burden exceeds a threshold PVC burden and that the patient is asymptomatic for heart failure, determine that sensed heart sound signal information indicates heart failure of the patient, and present a recommendation of an echocardiogram for the patient to a user.

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 heart failure (HF) status in a patient are discussed. An exemplary system includes a gait analyzer circuit than can receive gait or balance information, and generate a gait feature such as gait speed or a gait pattern. The system includes a HF detector circuit that can detect patient HF status, or to predict patient risk of a future worsening heart failure (WHF) event, using the gait feature. In some examples, the system may trigger sensing physiologic information according to the detected gait, and detect patient HF status using the sensed physiologic information. The system can initiate or adjust a heart failure therapy according to the HF status or the WHF risk.

Abstract: Systems and methods are disclosed to an ambulatory medical device. The ambulatory medical device comprising an activity sensor configured to produce an activity signal representative of activity level of a patient, a heart sound sensor configured to produce a heart sound signal, and a control circuit. The control circuit is configured to detect a change in activity level of the patient using the activity signal, enable monitoring of the heart sound signal in response to detecting the change in activity level, measure intensity of an S3 heart sound in the heart sound signal, and produce an indication of pulmonary hypertension (PH) when the measured intensity of the S3 heart sound exceeds a PH detection threshold intensity.

Abstract: Systems and methods for monitoring heart failure (HF) status in a patient are discussed. An exemplary system includes a gait analyzer circuit than can receive gait or balance information, and generate a gait feature such as gait speed or a gait pattern. The system includes a HF detector circuit that can detect patient HF status, or to predict patient risk of a future worsening heart failure (WHF) event, using the gait feature. In some examples, the system may trigger sensing physiologic information according to the detected gait, and detect patient HF status using the sensed physiologic information. The system can initiate or adjust a heart failure therapy according to the HF status or the WHF risk.

Abstract: A medical device system includes an ambulatory medical device and a second device. The ambulatory medical device includes one or more physiologic sensors configured to output sensor data and a communication circuit to transmit the sensor data to another device. The second device includes another communication circuit configured to receive the sensor data from ambulatory medical device, and processing circuitry. The processing circuitry is configured to determine patterns of the sensor data consistent with hemodialysis treatments of the patient, identify a change in the patterns of the sensor data indicative of a change in the hemodialysis treatments, and produce an alert associated with the hemodialysis treatments in response to the determining the change in the patterns of the sensor data.

Abstract: This document discusses, among other things, systems and methods for managing pain in a patient. A system may include sensors to sense physiological or functional signals, and a pain analyzer that generates a pain score using the sensed physiological or functional signals and a fusion model. The system includes a calibration module that calibrates the fusion model based on measurements from the sensed physiological or functional signals and a reference pain quantification corresponding to multiple pain intensities. A pain score may be generated using the calibrated fusion model. The system can additionally include a neurostimulator that controls the delivery of pain therapy by adjusting one or more stimulation parameters based on the pain score.

Abstract: This document discusses, among other things, systems and methods to receive physiologic information from a patient and to generate a heart failure status using the received physiologic information. Based on the received physiologic information, the generated heart failure status can be classified into at least one of a set of predetermined phenotype clusters.

Abstract: An ambulatory medical device includes a multi-axis posture sensor and processing circuitry. The multi-axis posture sensor is configured to provide an electrical posture sensor output representative of alignment of respective first, second, and third non-parallel axes of the ambulatory medical device with the gravitational field of the earth. The processing circuitry is configured to determine that the subject avoids lying on their left side using the posture sensor output, and compute a metric predictive of one or both of orthopnea and trepopnea in response to determining that the subject avoids lying on their left side.

Abstract: This document discusses, among other things, systems and methods for managing pain in a subject. A system may include one or more sensors configured to sense from the subject information corresponding to emotional reaction to pain, such as emotional expression. The emotional expression includes facial or vocal expression. A pain analyzer circuit may generate a pain score using signal metrics of facial or vocal expression extracted from the sensed information. The pain score may be output to a user or a process. The system may additionally include a neurostimulator that can adaptively control the delivery of pain therapy by automatically adjusting stimulation parameters based on the pain score.

Abstract: Systems and methods are described for subject rehospitalization management. In an example, multiple physiologic signals can be obtained from a subject using multiple sensors. In response to a hospitalization event, pre-hospitalization characteristics of the multiple physiologic signals can be identified. Post-hospitalization characteristics of the multiple physiologic signals can be identified, including characteristics that differ from their corresponding pre-hospitalization characteristics. Later subsequent physiologic signals can be further monitored after the hospitalization event, such as using the same multiple sensors, and subsequent physiologic signal characteristics can be identified. In an example, a heart failure diagnostic indication can be determined using information about the pre-hospitalization characteristics, the post-hospitalization characteristics, and the subsequent characteristics.

Abstract: Systems and methods for monitoring and staging sleep using cardiac and respiration information are disclosed. An exemplary system comprises a storage device to store a trained hybrid sleep detection and classification model that comprise a plurality of trained machine-learning models each trained to map input cardiac and respiratory data into one of multiple distinct model-specific awake or sleep classes, and a trained regression model to combine the model-specific awake or sleep classes to a composite awake or sleep classification. A sleep detector applies patient cardiac and respiratory information to the trained hybrid sleep detection and classification model to determine a composite awake or sleep classification for the patient. The composite awake or sleep classification is used to diagnose medical conditions including sleep apnea.

Abstract: Systems and methods for monitoring and managing aortic stenosis are disclosed. An exemplary medical-device system comprises a data receiver to receive heart sound information sensed by ambulatory heart sound sensors, and a stenosis detector to detect a change in aortic valve surface area from a baseline stenosis-free state using heart sound components such as S1 and S2 sounds. Based on the change in aortic valve surface area, the stenosis detector can generate an aortic stenosis indicator indicating a presence and severity of aortic stenosis. Such indicator can be provided to a user, trigger symptom monitoring and evaluation of functional deterioration in the patient, facilitate assessment of patient candidacy for aortic valve replacement, or triage heart failure management strategies.

Abstract: Systems and methods are disclosed to determine a composite health index for the patient as a function of a plurality of features, including determining validity of a first feature of the plurality of features and adjusting the function used to determine the composite health index in response to a determination that the first feature is not valid.

Abstract: Systems and methods for monitoring heart failure status in a patient are discussed. A medical-device system includes a storage device to store a correspondence between one or more heart failure comorbidities and corresponding one or more heart failure detection settings, and a heart failure detector circuit to detect a heart failure status of the patient. The heart failure detector circuit receives physiological information and heart failure comorbidity information of the patient, determines a detection setting for the patient based on the received comorbidity information and the stored correspondence, and detect a heart failure status using the received physiological information and the identified detection setting. A therapy circuit can deliver or adjust a heart failure therapy in response to the detected heart failure status.

Abstract: Systems and methods for detecting and managing heart failure are discussed. A medical-device system receives heart sound information sensed from the patient, generates a heart sound metric using the received heart sound information, and generate a heart failure indicator indicating whether the patient has a heart failure with preserved ejection fraction (HFpEF) or a heart failure with reduced ejection fraction (HFrEF) based at least in part on the heart sound metric. The medical-device system can detect a transition from HFpEF to HFrEF. A therapy circuit can deliver or adjust a heart failure therapy in response to the detected transition from HFpEF to HFrEF.

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.