Machine Learning Approaches to Analyzing Adverse Events Following Durable LVAD Implantation.

The Annals of Thoracic Surgery
Arman KilicRema Padman

Abstract

This study employed machine learning approaches to analyze sequences of adverse events (AEs) after left ventricular assist device (LVAD) implantation. Data on patients implanted with the HeartWare HVAD durable LVAD were extracted from the ENDURANCE and ENDURANCE Supplemental clinical trials, with follow-up through 5 years. Major AEs included device malfunction, major bleeding, major infection, neurological dysfunction, renal dysfunction, respiratory dysfunction, and right heart failure (RHF). Time interval and transition probability analyses were performed. We created a Sankey diagram to visualize transitions between AEs. Hierarchical clustering was applied to dissimilarity matrices based on the longest common subsequence to identify clusters of patients with similar AE profiles. A total of 568 patients underwent HVAD implantation with 3590 AEs. Bleeding and RHF comprised the highest proportion of early AEs after surgery whereas infection and bleeding accounted for most AEs occurring after 3 months. The highest transition probabilities were observed with infection to infection (0.34), bleeding to bleeding (0.31), RHF to bleeding (0.31), RHF to infection (0.28), and bleeding to infection (0.26). Five distinct clusters of patient...Continue Reading

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Citations

Mar 30, 2021·The Journal of Heart and Lung Transplantation : the Official Publication of the International Society for Heart Transplantation·Manreet K KanwarMandeep R Mehra
Aug 17, 2021·Current Opinion in Cardiology·Rashmi NedadurBobby Yanagawa

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