A Machine Learning-derived Risk Score Improves Prediction of Outcomes After LVAD Implantation: An Analysis of the INTERMACS Database

Jin Joo Park; Sonya John; Claudio Campagnari; Avi Yagil; Barry Greenberg; Eric Adler

doi:10.1016/j.cardfail.2024.09.013

A Machine Learning-derived Risk Score Improves Prediction of Outcomes After LVAD Implantation: An Analysis of the INTERMACS Database

J Card Fail. 2024 Oct 31:S1071-9164(24)00881-9. doi: 10.1016/j.cardfail.2024.09.013. Online ahead of print.

Authors

Jin Joo Park¹, Sonya John², Claudio Campagnari³, Avi Yagil⁴, Barry Greenberg⁵, Eric Adler²

Affiliations

¹ Cardiology Department, University of California San Diego, La Jolla, California; Cardiovascular Center, Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seoul, Korea.
² Cardiology Department, University of California San Diego, La Jolla, California.
³ Physics Department, University of California, Santa Barbara, California.
⁴ Cardiology Department, University of California San Diego, La Jolla, California; Physics Department, University of California, La Jolla, California.
⁵ Cardiology Department, University of California San Diego, La Jolla, California. Electronic address: bgreenberg@health.ucsd.edu.

PMID: 39486760
DOI: 10.1016/j.cardfail.2024.09.013

Abstract

Background: Significant variability in outcomes after left ventricular assist device (LVAD) implantation emphasize the importance of accurately assessing patients' risk before surgery. This study assesses the Machine Learning Assessment of Risk and Early Mortality in Heart Failure (MARKER-HF) mortality risk model, a machine learning-based tool using 8 clinical variables, to predict post-LVAD implantation mortality and its prognostic enhancement over the Interagency Registry of Mechanically Assisted Circulatory Support (INTERMACS) profile.

Methods: Analyzing 25,365 INTERMACS database patients (mean age 56.8 years, 78% male), 5,663 (22.3%) and 19,702 (77.7%) received HeartMate 3 and other types of LVAD, respectively. They were categorized into low, moderate, high, and very high-risk groups based on MARKER-HF score. The outcomes of interest were in-hospital and 1-year postdischarge mortality.

Results: In patients receiving HeartMate 3 devices, 6.2% died during the index hospitalization. In-hospital mortality progressively increased from 4.4% in low-risk to 15.2% in very high-risk groups with MARKER-HF score. MARKER-HF provided additional risk discrimination within each INTERMACS profile. Combining MARKER-HF score and INTERMACS profile identified patients with the lowest (3.5%) and highest in-hospital mortality rates (19.8%). The postdischarge mortality rate at 1 year was 5.8% in this population. In a Cox proportional hazard regression analysis adjusting for both MARKER-HF and INTERMACS profile, only MARKER-HF score (hazard ratio 1.27, 95% confidence interval 1.11-1.46, P < .001) was associated with postdischarge mortality. Similar findings were observed for patients receiving other types of LVADs.

Conclusions: The MARKER-HF score is a valuable tool for assessing mortality risk in patients with HF undergoing HeartMate 3 and other LVAD implantation. It offers prognostic information beyond that of the INTERMACS profile alone and its use should help in the shared decision-making process for LVAD implantation.

Keywords: HeartMate 3; MARKER-HF; machine-learning; risk prediction.