Abstract: Systems and methods for monitoring patients for risk of worsening heart failure (WHF) are discussed. A patient management system includes a sensor circuit to receive respiration measurements, and an input device to receive patient dyspnea descriptor indicating dyspnea symptom severity. The system includes a risk analyzer circuit to trend the respiration measurement over time, and to generate a WHF risk indicator using both the trended respiration measurement and the received dyspnea descriptor. A control circuit controls the receiver circuit to receive the dyspnea descriptor in response to the trended respiration measurement satisfying a specific condition. The system can present the WHF risk indicator to a user or a process, or deliver or adjust a HF therapy based on the WHF risk indicator.

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 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: An example of a system for managing pain may include a pain monitoring circuit, a pain relief device, and a control circuit. The pain monitoring circuit may include a parameter analyzer and a pain score generator. The parameter analyzer may be configured to receive and analyze at least two parameters selected from a physiological parameter indicative of a physiological function or state of a patient, a functional parameter indicative of a physical activity or state of the patient, or a patient parameter including subjective information provided by the patient. The pain score generator may be configured to compute a composite pain score using an outcome of the analysis. The composite pain score may indicate a degree of the pain. The pain relief device may be configured to deliver a pain-relief therapy. The control circuit may be configured to control the delivery of the pain-relief therapy using the composite 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 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: Systems and methods are disclosed to detect a potential syncope event using cardiac acceleration information of a patient and to transition a medical device from a first low-power mode to a second high-power mode in response to the detected potential syncope event.

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 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: This document discusses, among other things, systems and methods to determine an alert state for a patient using received physiologic information, determine an event rate for the alert state, and adjust determination of a composite risk for the patient using the determined event rate.

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 managing heart failure are described. The system receives physiological information including a first HS signal corresponding to paced ventricular contractions and a second HS signal corresponding to intrinsic ventricular contractions. The system detects worsening heart failure (WHF) using the received physiological information. A signal analyzer circuit can generate a paced HS metric from the first HS signal and a sensed HS metric from the second HS signal, and determine a concordance indicator between the paced and the sensed HS metrics. In response to the detected WHF, the system can use the concordance indicator to generate a therapy adjustment indicator for adjusting electrostimulation therapy, or a worsening cardiac contractility indicator indicating the detected WHF is attributed to degrading myocardial contractility.

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 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: This document discusses, among other things, systems and methods for managing pain of a subject. A system includes one or more physiological sensors configured to sense a physiological signal indicative of patient brain activity. The physiological signals may include an electroencephalography signal, a magnetoencephalography signal, or a brain-evoked potential. The system may extract from the brain activity signal one or more signal metrics indicative of strength or pattern of brain electromagnetic activity associated with pain, and generate a pain score using the one or more signal metrics. The pain score can be output to a patient or a process. The system may select an electrode configuration for pain-relief electrostimulation based on the pain score, and deliver a closed-loop pain therapy according to the selected electrode configuration.

Abstract: Systems and methods for monitoring physiologic response to Valsalva maneuver (VM) are disclosed. An exemplary patient monitor may detect a natural incidence of a VM session occurred in an ambulatory setting using a heart sound (HS) signal sensed from the patient. The patient monitor may include a physiologic response analyzer to sense patient physiologic response during the detected VM session, and generate a cardiovascular or autonomic function indicator based on the sensed physiologic response to the VM. Using the physiologic response to the VM, the system may detect a target physiologic event using the sensed physiologic response to the VM.

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 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 determine an alert state for each of a plurality of patients using received physiologic information, determine an event rate for the plurality of patients for a specific alert state, and adjust a composite HF risk determination for the plurality of patients using the determined event rate.