Abstract: An example of a system for monitoring and treating respiratory distress in a patient may include one or more non-invasive monitoring devices to acquire patient condition signals and a respiratory distress monitoring circuit to monitor a state of the respiratory distress using the patient condition signals. The respiratory distress monitoring circuit may include a signal processing circuit to generate patient condition parameters using the patient condition signals and a respiratory distress analyzer, which may include a parameter analysis circuit and a notification circuit. The parameter analysis circuit may be configured to produce a patient condition metric being a function of the patient condition parameters and to perform prediction and/or detection of an exacerbation of the respiratory distress based on the patient condition metric. The notification circuit may be configured to produce an alert notifying a result of the performance of the prediction and/or detection.

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 to detect pneumonia in cardiovascular patients are disclosed, including receiving physiologic information of a patient from an ambulatory medical device (AMD), the physiologic information comprising respiration information of the patient, and determining a pneumonia score of the patient using the received respiration information.

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 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 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 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 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.