Preliminary experience with the multisensor HeartLogic algorithm for heart failure monitoring: a retrospective case series report.

AIMS

In the Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients study, a novel algorithm for heart failure (HF) monitoring was implemented. The HeartLogic (Boston Scientific) index combines data from multiple implantable cardioverter defibrillator (ICD)-based sensors and has proved to be a sensitive and timely predictor of impending HF decompensation. The remote monitoring of HF patients by means of HeartLogic has never been described in clinical practice. We report post-implantation data collected from sensors, the combined index, and their association with clinical events during follow-up in a group of patients who received a HeartLogic-enabled device in clinical practice.

METHODS AND RESULTS

Patients with ICD and cardiac resynchronization therapy ICD were remotely monitored. In December 2017, the HeartLogic feature was activated on the remote monitoring platform, and multiple ICD-based sensor data collected since device implantation were made available: HeartLogic index, heart rate, heart sounds, thoracic impedance, respiration, and activity. Their association with clinical events was retrospectively analysed. Data from 58 patients were analysed. During a mean follow-up of 5 ± 3 months, the HeartLogic index crossed the threshold value (set by default to 16) 24 times (over 24 person-years, 0.99 alerts/patient-year) in 16 patients. HeartLogic alerts preceded five HF hospitalizations and five unplanned in-office visits for HF. Symptoms or signs of HF were also reported at the time of five scheduled visits. The median early warning time and the time spent in alert were longer in the case of hospitalizations than in the case of minor events of clinical deterioration of HF. HeartLogic contributing sensors detected changes in heart sound amplitude (increased third sound and decreased first sound) in all cases of alerts. Patients with HeartLogic alerts during the observation period had higher New York Heart Association class (P = 0.025) and lower ejection fraction (P = 0.016) at the time of activation.

CONCLUSIONS

Our retrospective analysis indicates that the HeartLogic algorithm might be useful to detect gradual worsening of HF and to stratify risk of HF decompensation.