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

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.