Remote heart failure management using the HeartLogic algorithm. RE-HEART registry

Javier de Juan Bagudá1, Juan J Gavira Gómez2, Marta Pachón Iglesias3

  • 1Servicio de Cardiología, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Spain; Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain.

Insights

The HeartLogic algorithm in implantable cardioverter-defibrillators (ICDs) effectively predicts heart failure (HF) decompensations. A standardized remote management protocol safely utilized these alerts, reducing HF hospitalizations.

Area of Science:

  • Cardiology
  • Medical Devices
  • Artificial Intelligence in Medicine

Background:

  • Heart failure (HF) decompensation is a leading cause of hospitalization.
  • Implantable cardioverter-defibrillators (ICDs) offer remote monitoring capabilities.
  • Predictive algorithms can potentially improve HF management.

Purpose of the Study:

  • To analyze the association between HeartLogic alerts and clinical events.
  • To describe the implementation of a remote management protocol for HeartLogic alerts.
  • To evaluate the efficacy of a standardized protocol in managing HF decompensations.

Main Methods:

  • Multicenter registry study evaluating HeartLogic alerts in ICDs.
  • Phased approach: Phase 1 (blinded), Phases 2 & 3 (activated, managed per local practice and standardized protocol).
  • Analysis of alert frequency, association with HF events, and remote management rates.

Main Results:

  • HeartLogic alerts were frequently associated with HF events and other clinical situations (0.89 alerts/patient-y).
  • No HF hospitalizations occurred outside the alert periods in Phase 1.
  • A low rate of unexplained alerts (0.39 alerts/patient-y) was observed.
  • Most alerts (81-82%) were remotely managed, with higher NT-proBNP levels during alert states.

Conclusions:

  • The HeartLogic index is a valuable tool for predicting HF decompensations.
  • A standardized protocol enables safe and effective remote detection and management of alerts.
  • This approach can potentially reduce HF hospitalizations and improve patient outcomes.
Abstract

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