Cardiac Pacemakers, Inc.

Boston Scientific Neuromodulation Corporation

Cameron Health, Inc.

Cardiac Pacamakers, Inc.

Qi An

Pramodsingh Hirasingh Thakur

Viktoria A Averina

Yinghong Yu

Yi Zhang

Julie A Thompson

Barun Maskara

Keith R Maile

Krzysztof Z Siejko

Jeffrey E Stahmann

Bin Mi

Ramesh Wariar

Michael John Kane

David J Ternes

John D Hatlestad

Jonathan Bennett Shute

Robert J Sweeney

Brendan Early Koop

Deepa Mahajan

Stephen John Hahn

Gezheng Wen

Amy Jean Brisben

Howard D Simms, Jr

Rezwan Ahmed

Jason Humphrey

Adam MacEwen

Allan Charles Shuros

David L Perschbacher

Stephen B Ruble

Sunipa Saha

Brian D Soltis

Michael James Dufresne

Bryan Allen Clark

Rosana Esteller

Lili Liu

Tianhe Zhang

Arjun D Sharma

Jonathan Walter Krueger

Zhe Shen

Juan G Hincapie

JoAnna Trapp Simpson

Pratik K Pandya

Martin McDaniel

Kimberly Anne Eridon

Arwinder Pal Singh

Ranganathan Balasubramanian Iyer

Jianjun Yuan

Amy Jean Brisden

Ashley Moriah Jensen

Melanie Anne Zile

Howard D Simms Jr

Viktoria A Avernia

Brian A Clark

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.

Systems and methods for detecting worsening heart failure

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.

Systems and methods for heart failure management

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 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.

Heart failure monitor using gait information

Embodiments of the present disclosure relate to imaging a body part using sounds. In embodiments, a system comprises a motion sensor and a processing device communicatively coupled to the motion sensor. The motion sensor is configured to sense an acceleration wave produced by a sound emitted by a source and generate acceleration measurements in response to sensing the acceleration wave, wherein the source is associated with the body part of a subject. The processing device is configured to receive the acceleration measurements and determine a location of the source using a location of the motion sensor and the acceleration measurements. In addition, the processing device is configured to image the body part of the subject using the determined location of the source and the acceleration measurements.

Imaging of a body part using sounds

Systems and methods for monitoring and treating patients with heart failure are discussed. The system can store in a memory stimulation parameters, including stimulation timing parameters for a plurality of heart rate ranges. The system includes a plurality of timers with respective durations for the plurality of heart rate ranges. A stimulation control circuit can identify a target heart range in which a detected heart rate falls, and measure an atrioventricular (AV) conduction characteristic value in response to the timer for the target heart range being expired at the detected heart rate. The stimulation control circuit can update a stimulation parameter corresponding to the target heart rate range using the measured AV conduction characteristic. The updated stimulation parameter can be used in cardiac stimulation.

Heart rate indicated atrioventricular delay optimization

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.

Phenotyping sensor patterns to identify and treat heart failure

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 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.

Systems and methods for patient monitoring

Systems and methods for monitoring and treating patients with heart failure are discussed. The system may receive patient atrioventricular (AV) conduction characteristic under different heart rates or patient conditions. Stimulation parameters including stimulation timing parameters may be stored in a memory. The system may include a stimulation control circuit configured to determine a parameter update schedule indicating a timing at which to update stimulation parameter using patient AV conduction characteristic, and dynamically update at least a portion of the stored set of stimulation parameters at the determined parameter update schedule. For a specified heart rate or heart rate range, a stimulation parameter may be selected from the set of the stimulation parameters for use during cardiac stimulation.

Dynamic atrioventricular delay optimization

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

Methods and systems for providing closed loop control of stimulation provided by an implantable stimulator device are disclosed herein. The disclosed methods and systems use a neural feature prediction model to predict a neural feature, which is used as a feedback control variable for adjusting stimulation. The predicted neural feature is determined based on one or more stimulation artifact features. The disclosed methods and systems can be used to provide closed loop feedback in situations, such as sub-perception therapy, when neural features cannot be readily directly measured.

Systems and methods for using electrospinogram signals for closed loop control in Spinal Cord Stimulation therapy

Systems and methods to are disclosed to determine a sleep disordered breathing parameter of a patient, including receiving respiration information of the patient and temperature information of the patient and to determine the sleep disordered breathing parameter of the patient using the received respiration information and temperature information of the patient.

Temperature monitoring for sleep disordered breathing

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.

Pneumonia detection in cardiovascular patients

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Blaine, MN, United States of America

Qi An