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Published on: February 28, 2012
Predicting all-cause mortality by means of a multisensor implantable defibrillator algorithm for heart failure
Antonio D'Onofrio1, Gennaro Vitulano2, Leonardo Calò3
1Unità Operativa di Elettrofisiologia, Studio e Terapia delle Aritmie", Monaldi Hospital, Naples, Italy.
Insights
The HeartLogic algorithm effectively predicts heart failure decompensation. Its alert states significantly identify patients at higher risk for mortality, aiding proactive care.
Area of Science:
- Cardiology
- Medical Devices
- Predictive Analytics
Background:
- The HeartLogic algorithm is a sensitive predictor of impending heart failure decompensation.
- It integrates multiple physiological parameters into a single index for monitoring.
Purpose of the Study:
- To evaluate if remotely monitored HeartLogic data can identify patients at high risk for mortality.
- To assess the algorithm's utility in predicting all-cause mortality in heart failure patients.
Main Methods:
- The HeartLogic algorithm, combining heart sounds, impedance, respiration, and activity, was activated in 568 patients with implantable cardioverter-defibrillators (ICDs).
- Data from 26 centers were analyzed over a median follow-up of 26 months, focusing on "IN-alert" and "OUT of alert" states.
- Multivariate analysis controlled for baseline confounders to assess mortality risk.
Main Results:
- 1200 alerts were recorded in 370 patients (65%), with the "IN-alert" state comprising 13% of the observation period.
- Patients in the "IN-alert" state had a significantly higher mortality rate (0.25 deaths/patient-year) compared to those "OUT of alert" (0.02 deaths/patient-year).
- The "IN-alert" state remained a strong predictor of death (hazard ratio 9.18) after adjusting for baseline factors.
Conclusions:
- The HeartLogic algorithm's index can identify patients at elevated risk for all-cause mortality.
- The "IN-alert" state signifies periods of substantially increased risk of death, enabling timely clinical intervention.
Background:
The HeartLogic algorithm (Boston Scientific) has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation.
Objective:
The purpose of this study was to determine whether remotely monitored data from this algorithm could be used to identify patients at high risk for mortality.
Methods:
The algorithm combines implantable cardioverter-defibrillator (ICD)-measured accelerometer-based heart sounds, intrathoracic impedance, respiration rate, ratio of respiration rate to tidal volume, night heart rate, and patient activity into a single index. An alert is issued when the index crosses a programmable threshold. The feature was activated in 568 ICD patients from 26 centers.
Results:
During median follow-up of 26 months [25th-75th percentile 16-37], 1200 alerts were recorded in 370 patients (65%). Overall, the time IN-alert state was 13% of the total observation period (151/1159 years) and 20% of the follow-up period of the 370 patients with alerts. During follow-up, 55 patients died (46 in the group with alerts). The rate of death was 0.25 per patient-year (95% confidence interval [CI] 0.17-0.34) IN-alert state and 0.02 per patient-year (95% CI 0.01-0.03) OUT of the alert state, with an incidence rate ratio of 13.72 (95% CI 7.62-25.60; P <.001). After multivariate correction for baseline confounders (age, ischemic cardiomyopathy, kidney disease, atrial fibrillation), the IN-alert state remained significantly associated with the occurrence of death (hazard ratio 9.18; 95% CI 5.27-15.99; P <.001).
Conclusion:
The HeartLogic algorithm provides an index that can be used to identify patients at higher risk for all-cause mortality. The index state identifies periods of significantly increased risk of death.
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