Cardiac Pacemakers, Inc.

Pacesetter, Inc.

Robert J Sweeney

Krzysztof Z Siejko

Eric G Lovett

Marina V Brockway

Imad Libbus

Bruce H KenKnight

Yi Zhang

Pramodsingh Hirasingh Thakur

Kevin John Stalsberg

William Hsu

Shelley Marie Cazares

Haresh G Sachanandani

Jon Peterson

Scott Thomas Mazar

Jeffrey E Stahmann

Qi An

Ronald W Heil, Jr

Allan Charles Shuros

Avram Scheiner

Yatheendhar D Manicka

Frank W Ingle

Carlos Alberto Ricci

David C Olson

Gerrard Merrill Carlson

Viktoria A Averina

Julie A Thompson

Julio Cesar Spinelli

Rodney W Salo

Apurv Ullas Kamath

Mark W Kroll

Yinghong Yu

Jiang Ding

Ramesh Wariar

Abhilash Patangay

Douglas R Daum

Kelly Schatz

Matthias Woellenstein

Aaron R McCabe

Ed J Mikolajczyk

Howard Simms

Kent Lee

Joseph M Bocek

Joseph M Pastore

Gabriel A Mouchawar

Dan Li

Scott A Meyer

Xuan K Wei

Steven D Girouard

Craig Michael Stolen

Darrell Orvin Wagner

Adam W Cates

Howard D Simms, Jr

Jonathan Walter Krueger

Ron Heil

Brian Brandell

Donald I Hopper

Various system embodiments comprise a stimulator adapted to deliver a stimulation signal for a heart failure therapy, a number of sensors adapted to provide at least a first measurement of a heart failure status and a second measurement of the heart failure status, and a controller. The controller is connected to the stimulator and to the number of sensors. The controller is adapted to use the first and second measurements to create a heart failure status index, and control the stimulator to modulate the signal using the index. Other aspects and embodiments are provided herein.

Heart failure management

Devices and methods for detecting events indicative of heart failure (HF) decompensation status are described. An ambulatory medical device can determine the present physiologic state as being either a drift state or a stable state, and applies an algorithm to detect HF decompensation event according to the physiologic state. In some embodiments, the ambulatory medical device uses the present physiologic state to estimate one ore more expected future signal characteristics, and to detect HF decompensation event using the one or more expected futures signal characteristics.

Method and apparatus for multi-state heart failure decompensation detection

This document discusses, among other things, systems and methods to enable physician labels on a remote server and use labels to verify and improve algorithm results. A method comprises using patient data in an automated analysis to obtain a result; receiving a message from the user, wherein the message is related to the result; and using at least a portion of the message to automatically modify the analysis.

Method and system to enable physician labels on a remote server and use labels to verify and improve algorithm results

Devices and methods for detecting physiological target event such as events indicative of HF decompensation status are described. A medical device is configured to receive at least a first and a second chronic condition indictors of a patient, receive one or more physiologic signals from the patient, and generate a plurality of signal metrics when the first and the second chronic condition indicators meet their respective criterion. The medical device can detect the target event or condition using one or more patient-specific signal metrics selected from a group including both the first and the second set of the signal metrics. The medical device and the methods can be configured to detect an event indicative of HF decompensation.

Methods and apparatus for detecting heart failure event using patient chronic conditions

Systems and methods for detecting a present, or predicting a future, target physiologic event such as worsening heart failure (HF) are described. A system can comprise a patient information receiver circuit, at least two categorical risk analyzer circuits, and a categorical fusion circuit. The patient information receiver circuit receives physiologic signals and generates signal trends. The categorical risk analyzer circuit receives a category-specific input selected from the signal trends according to an associative physiologic condition indicative or correlative of the target event. The categorical risk analyzer circuit produces a signal trend metric indicating relative change in signal strength over time. The categorical risk analyzer circuit calculates a categorical risk index that indicates likelihood of the patient developing or presenting the associative physiologic condition. The categorical fusion circuit uses the categorical risk indices to generate a composite risk indicator indicating likelihood of the patient developing the target physiologic event.

Heart failure event detection using multi-level categorical fusion

Devices and methods for identifying patient at elevated risk of developing future heart failure (HF) events, such as events indicative of HF decompensation status, are described. The devices and methods can stratify the risk using sensor signals or signal metrics selected in accordance with patient chronic conditions. A medical device can receive a patient status input including at least a first and a second chronic condition indicators, sense one or more physiologic signals from the patient, and generate a plurality of signal metrics from the physiologic signals when the first chronic condition indicator and the second chronic condition indicator meets respective criterion. One or more patient-specific signal metrics can be selected from a group including the signal metrics selected for both the first and the second chronic conditions. A risk stratification algorithm can use the selected one or more patient-specific signal metrics to compute a composite risk index indicative of the probability of the patient later developing an event indicative of worsening of HF.

Methods and apparatus for stratifying risk of heart failure decompensation

Systems and methods to monitor an improvement of a subject's physiological condition using a goal improvement profile are described. In an example, a physiological condition of a subject can be compared to a goal improvement profile, where the profile includes at least one target condition indicative of an improved physiological condition detectable using a physiological sensor signal, and a target time when the subject is expected to reach the target condition, where the comparison can be used to indicate whether the subject has met the target condition by the target time.

Patient health improvement monitor

Systems and methods to indicate heart failure co-morbidity are described. In an example, one or more physiological signals can be analyzed for a specified time period to determine at least one of an indication of a heart failure event or a characteristic of heart failure co-morbidity, such as a characteristic of a non-heart failure physiological event. At least one of the different physiological signals can be compared to a specified criterion indicative of the heart failure event to provide a comparison value, where the comparison value is indicative of whether the at least one of the different physiological signals contributed towards the indication of the heart failure event. An indication of a non-heart failure physiological event can be determined using the heart failure event indication, the non-heart failure physiological event characteristic, and the comparison value indicative of whether the one or more physiological signals contributed towards the heart failure event.

Systems and methods to indicate heart failure co-morbidity

A system comprises a risk analysis module and a worsening heart failure (WHF) detection module. The risk analysis module measures at least one first physiological parameter of a subject using a physiological sensor of an ambulatory medical device, and determines a heart failure (HF) risk score for the subject according to the at least one measured first physiological parameter. The HF risk score indicates susceptibility of the subject to experiencing a HF event. The WHF detection module measures at least one second physiological parameter of the subject using the same or different physiological sensor, and generates an indication of prediction that the subject will experience a WHF event when the at least one second physiological parameter satisfies a WHF detection algorithm. The risk analysis module adjusts generation of the indication by the WHF detection algorithm according to the determined HF risk score.

Risk stratification based heart failure detection algorithm

Systems and related methods for analyzing data sensed from a device implanted in a patient, such as a cardiac pacing system. The system detects and evaluates electric signals within the patient that share a common event marker. By using algorithms and graphical presentation of the collected signals having common event markers, deviations in signals over time can be identified and evaluated in consideration of taking further action related to the patient and the implanted device. The system can also be used in conjunction with an advanced patient management system that includes a programmer or repeater capable of gathering information from the implanted device and transmitting the data to a host via a communications network for evaluation at a remote location.

Methodology for automated signal morphology analysis in implantable electrotherapy and diagnostic systems

Systems and methods are provided for using stored physiologic information about a subject to detect a previous treatment event. Physiologic information can be sensed from a subject using one or more sensors. Using a detection circuit, a change in the sensed physiologic information, such as a change from reference physiologic information, can be used to identify a candidate previous treatment event. An alert or other information about the candidate treatment event can be provided to a patient or clinician. In an example, a candidate treatment event can include a heart failure or diuresis treatment that is identified using information about a change in one or more of a subject's circadian pattern, a subject's thoracic impedance, or a subject's respiration status.

Systems and methods for detecting medical treatment

This document discusses, among other things, systems and methods for predicting heart failure decompensation using within-patient diagnostics. A method comprises detecting an alert status of each of one or more sensors; calculating an alert score by combining the detected alerts; and calculating a composite alert score, the composite alert score being indicative of a physiological condition and comprising a combination of two or more alert scores.

Within-patient algorithm to predict heart failure decompensation

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Woodbury, MN, United States of America

Robert J Sweeney