The Potential of the HeartLogic^TM Algorithm in Patients with a Left Ventricular Assist Device, an Initial Report

Abstract

Background: Survival and quality-of-life of left ventricular assist device (LVAD) recipients improved significantly because of growing experience and technological advances. However, LVAD-related complication rates, including recurrent episodes of congestion, remain high. Early detection of fluid retention to provide a time-window for medical intervention is the pillar in preventing hospitalizations. The multisensory HeartLogicTM algorithm accurately detected impending congestion in ambulant heart failure patients. The aim of the current study is to investigate the feasibility of HeartLogicTM-driven care in LVAD patients. Methods: Consecutive LVAD destination therapy patients were followed-up according the structured HeartLogicTM-based heart failure carepath. An alert triggered a device check-up, and the heart failure team contacted the patient to evaluate for signs and symptoms of impending congestion. An alert was adjudicated as true positive or unexplained. An episode of congestion not preceded by an alert was deemed as a false negative. Results: Data from 7 patients were included: the median age was 67 years [IQR 61–71], 71% were male and 71% had a non-ischemic aetiology. Total follow-up entailed 12 patient-years. All patients experienced at least one alert. In total, 33 alerts were observed. Majority of alerts (70%, n = 23) were driven by congestion and one alerts (15%) were clinically meaningful but not primarily fluid-retention-related (e.g., altered hemodynamic triggered by a pump thrombosis). Of all the alerts, five (15%) were classified as an unexplained alert, and during follow-up, four false negative episodes were documented. Conclusions: HeartLogicTM-driven care with continuous monitoring to detect impending fluid retention in LVAD patients was feasible and deserves further prospective validation.

Keywords

• HeartLogic^TM
• left ventricular assist device
• heart failure
• CIED
• multisensory remote monitoring