Monitoring left ventricular assist device parameters to detect flow- and power-impacting complications: a proof of concept

Abstract

AIMS: The number of patients on left ventricular assist device (LVAD) support increases due to the growing number of patients with end-stage heart failure and the limited number of donor hearts. Despite improving survival rates, patients frequently suffer from adverse events such as cardiac arrhythmia and major bleeding. Telemonitoring is a potentially powerful tool to early detect deteriorations and may further improve outcome after LVAD implantation. Hence, we developed a personalized algorithm to remotely monitor HeartMate3 (HM3) pump parameters aiming to early detect unscheduled admissions due to cardiac arrhythmia or major bleeding. METHODS AND RESULTS: The source code of the algorithm is published in an open repository. The algorithm was optimized and tested retrospectively using HeartMate 3 (HM3) power and flow data of 120 patients, including 29 admissions due to cardiac arrhythmia and 14 admissions due to major bleeding. Using a true alarm window of 14 days prior to the admission date, the algorithm detected 59 and 79% of unscheduled admissions due to cardiac arrhythmia and major bleeding, respectively, with a false alarm rate of 2%. CONCLUSION: The proposed algorithm showed that the personalized algorithm is a viable approach to early identify cardiac arrhythmia and major bleeding by monitoring HM3 pump parameters. External validation is needed and integration with other clinical parameters could potentially improve the predictive value. In addition, the algorithm can be further enhanced using continuous data.