Cardiac Pacemakers, Inc.

Julie A Thompson

Pramodsingh Hirasingh Thakur

Qi An

Yi Zhang

Ramesh Wariar

Barun Maskara

Jeffrey E Stahmann

Viktoria A Averina

Helen L Reeve-Stoffer

Krzysztof Z Siejko

John D Hatlestad

Eric G Lovett

Kenneth C Beck

Jaeho Kim

Robert J Sweeney

Aaron R McCabe

David J Yonce

Yousufali H Dalal

Yinghong Yu

James O Gilkerson

Joseph M Bocek

Abhilash Patangay

Yongxing Zhang

Yunlong Zhang

Helen L Reeve

Janice Jenkins

Bruce H KenKnight

Scott Thomas Mazar

Dan Li

Yatheendhar D Manicka

Tamara Colette Baynham

Shelley Marie Cazares

Yayun Lin

Arjun D Sharma

Richard Fogoros

Richard Milon Dujmovic, Jr

Aaron Lewicke

Jonathan Walter Krueger

Michael J Pederson

Joseph Kovac

Valerie Smith

Aaron Mccabe

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.

Multi-disease patient management

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. Information about relative changes in signal characteristics from multiple sensors can be used to identify particular subject physiologic signals to monitor during subsequent periods.

Heart failure management to avoid rehospitalization

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.

Readmission risk assessment based on chronobiological rhythms

A hospitalization management system including a heart failure analyzer that receives diagnostic data including at least sensor data representative of one or more physiological signals sensed from a hospitalized patient using one or more sensors and assesses risk of rehospitalization for the patient using the diagnostic data. The outcome of the risk assessment is used during and following the patient's hospitalization for reducing the risk of rehospitalization.

Method and apparatus for management of heart failure hospitalization

Devices and methods for improving device therapy such as cardiac resynchronization therapy by determining a value for a device parameter are described. An ambulatory medical device (AMD) can include a sensor circuit to sense a physiological signal and generate two or more signal metrics, and detect an event of worsening cardiac condition using the two or more signal metrics. In response to the detection of worsening cardiac condition, the AMD can determine, for a stimulator, a value of at least one stimulation parameter based on temporal responses of two or more signal metrics. The temporal responses include near-term and long-term responses to the stimulation. The AMD can program the stimulator with the determined parameter value, and generate stimulation according to the determined parameter value to stimulate target tissue.

Multi-sensor based cardiac stimulation

In one example, a method of monitoring a sustained activity of a human or animal subject for the purpose of determining a risk group includes detecting a physical activity signal from the subject, determining a magnitude of the detected physical activity signal, initiating a timer in response to determining the magnitude of the physical activity signal exceeding an activity level threshold, triggering storing at least one value associated with the physical activity signal only when the magnitude of the physical activity signal exceeds the activity level threshold and the timer is greater than a duration threshold, and determining, using the processor circuit, an indication of the subject's cardiovascular disease based on the stored value.

Activity level determination for risk stratification

Assessing decongestive therapy delivered to a heart failure patient involves use of an implantable sensor configured to sense a physiologic parameter indicative of the patient's diuresis status and a processor coupled to the implantable sensor. The sensor may comprise a thoracic fluid sensor, a heart sounds sensor, a cardiac chamber or arterial pressure sensor, a respiration sensor, or a blood chemistry sensor, for example. The processor is configured to determine if a target level of patient diuresis has been achieved based on a relationship between the sensed physiologic parameter and a threshold developed for the patient, and to produce an output in response to determining that the target level of patient diuresis has been achieved. The processor may be disposed in an implantable housing, in a patient-external housing, or in a network server system.

Decongestive therapy titration for heart failure patients using implantable sensor

An apparatus comprises plurality of sensors and a processor. Each sensor provides a sensor signal that includes physiological information and at least one sensor is implantable. The processor includes a physiological change event detection module that detects a physiological change event from a sensor signal and produces an indication of occurrence of one or more detected physiological change events, and a heart failure (HF) detection module. The HF detection module determines, using a first rule, whether the detected physiological change event indicative of a change in HF status of a subject, determines whether to override the first rule HF determination using a second rules, and declares whether the change in HF status occurred according to the first and second rules.

Multi-sensor strategy for heart failure patient management

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Circle Pines, MN, United States of America

Julie A Thompson