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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE

John P Boehmer1, Ramesh Hariharan2, Fausto G Devecchi3

  • 1Penn State Hershey Medical Center, Hershey, Pennsylvania.

JACC. Heart Failure
|March 4, 2017
PubMed
Summary

A new algorithm using implanted device sensors can predict heart failure events with 70% sensitivity, offering a median 34-day warning. This HeartLogic system aims to reduce hospitalizations for heart failure (HF).

Keywords:
cardiac devicescardiac resynchronization therapydecompensationdiagnosticsheart failureremote monitoringsensors

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Medical Device Technology

Background:

  • Heart failure (HF) leads to costly hospitalizations and poor patient outcomes.
  • Implanted devices offer potential for continuous monitoring of HF pathophysiology.
  • Predictive algorithms may enable early intervention to prevent decompensation.

Purpose of the Study:

  • To develop and validate a device-based diagnostic algorithm for predicting heart failure events.
  • To assess the feasibility of using multisensor data from implanted devices for HF monitoring.
  • To establish the sensitivity and alert rate of a novel HF prediction algorithm.

Main Methods:

  • The MultiSENSE study enrolled 900 patients with implanted cardiac resynchronization therapy defibrillators.
  • A composite index and alert algorithm (HeartLogic) was developed using heart sounds, respiration, thoracic impedance, heart rate, and activity.
  • Independent validation was performed in a sequestered test cohort, evaluating sensitivity and unexplained alert rates.

Main Results:

  • The HeartLogic algorithm achieved 70% sensitivity in detecting heart failure events (HFEs) in the test cohort.
  • An unexplained alert rate of 1.47 per patient-year was observed, below the target of <2.
  • A median lead time of 34 days was observed before HFEs, enabling early intervention.

Conclusions:

  • The HeartLogic multisensor index and alert algorithm is a sensitive and timely predictor of impending heart failure decompensation.
  • Device-based monitoring and algorithms can significantly improve the management of chronic heart failure.
  • This technology holds promise for reducing hospitalizations and improving patient outcomes in heart failure.