Machine Learning Approaches to Analyzing Adverse Events Following Durable LVAD Implantation.
Academic Article
Overview
abstract
BACKGROUND: This study employed machine learning approaches to analyze sequences of adverse events (AEs) after left ventricular assist device (LVAD) implantation. METHODS: 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. RESULTS: 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 patients were generated, each with different patterns of time intervals between AEs, transition rates between AEs, and clinical outcomes. CONCLUSIONS: Machine learning approaches allow for improved visualization and understanding of AE burden after LVAD implantation. Distinct patterns and relationships provide insights that may be important for quality improvement efforts.
