HeartLogic Multisensor Algorithm Identifies Patients During Periods of Significantly Increased Risk of Heart Failure Events: Results From the MultiSENSE Study

Abstract

Background: Care of heart failure (HF) patients results in a high burden on healthcare resources, and estimating prognosis is becoming increasingly important to triage resources wisely. Natriuretic peptides are recommended prognosticators in chronic HF. Our objective was to evaluate whether a multisensor HF index and alert algorithm (HeartLogic) replaces or augments current HF risk stratification.

Methods and results: MultiSENSE (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients) enrolled 900 patients with cardiac resynchronization therapy defibrillators enabled for collection of heart sounds, respiration, thoracic impedance, heart rate, and activity data. The HeartLogic algorithm automatically calculated a daily HF index and identified periods IN or OUT of an active alert state relative to a configurable threshold. Patients experienced 192 independently adjudicated HF events (average rate, 0.20/patient-year [pt-yr]) during 1 year of follow-up. HF event rates while IN alert was 10-fold higher than OUT of alert (0.80 versus 0.08 events/pt-yr). Combined with NT-proBNP (N-terminal pro-B-type natriuretic peptide) at enrollment (relative to 1000 pg/mL threshold, event rate was 0.42 [HIGH] versus 0.07 [LOW] events/pt-yr), substratification found the lowest risk group (LOW NT-proBNP and OUT of alert) experienced 0.02 events/pt-yr, whereas the highest risk group (HIGH NT-proBNP and IN alert) was associated with a 50-fold increased risk of an HF event (1.00 events/pt-yr) relative to the lowest risk group.

Conclusions: Dynamic assessment using implantable device sensors within HeartLogic by itself or in conjunction with NT-proBNP measurements can identify time-intervals when patients are at significantly increased risk of worsening HF and potentially better triage resources to this vulnerable patient population.

Clinical trial registration: https://www.clinicaltrials.gov. Unique identifier: NCT01128166.

Keywords: algorithm; heart failure; hospitalization; prognosis; risk.