• Vardhmaan Jain, Agam Bansal, Nathan Radakovich, Vikram Sharma, Muhammad Zarrar Khan, Kevin Harris, Salam Bachour, Cerise Kleb, Jacek Cywinski, Maged Argalious, Cristiano Quintini, MenonK V NarayananKVNDivision of Gastroenterology and Hepatology, Cleveland Clinic, Cleveland, Ohio., Ravi Nair, Michael Tong, Samir Kapadia, and Maan Fares.
• Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio.
• J. Cardiothorac. Vasc. Anesth. 2021 Jul 1; 35 (7): 2063-2069.

ObjectiveTo develop machine learning models that can predict post-transplantation major adverse cardiovascular events (MACE), all-cause mortality, and cardiovascular mortality in patients undergoing liver transplantation (LT).DesignRetrospective cohort study.SettingHigh-volume tertiary care center.ParticipantsThe study comprised 1,459 consecutive patients undergoing LT between January 2008 and December 2019.InterventionsNone.Measurements And Main ResultsMACE, all-cause mortality, and cardiovascular mortality were modeled using logistic regression, least absolute shrinkage and selection surgery regression, random forests, support vector machine, and gradient-boosted modeling (GBM). All models were built by splitting data into training and testing cohorts, and performance was assessed using five-fold cross-validation based on the area under the receiver operating characteristic curve and Harrell's C statistic. A total of 1,459 patients were included in the final cohort; 1,425 (97.7%) underwent index transplantation, 963 (66.0%) were female, the median age at transplantation was 57 (11-70) years, and the median Model for End-Stage Liver Disease score was 20 (6-40). Across all outcomes, the GBM model XGBoost achieved the highest performance, with an area under the receiver operating curve of 0.71 (95% confidence interval [CI] 0.63-0.79) for MACE, a Harrell's C statistic of 0.64 (95% CI 0.57-0.73) for overall survival, and 0.72 (95% CI 0.59-0.85) for cardiovascular mortality over a mean follow-up of 4.4 years. Examination of Shapley values for the GBM model revealed that on the cohort-wide level, the top influential factors for postoperative MACE were age at transplantation, diabetes, serum creatinine, cirrhosis caused by nonalcoholic steatohepatitis, right ventricular systolic pressure, and left ventricular ejection fraction.ConclusionMachine learning models developed using data from a tertiary care transplantation center achieved good discriminant function in predicting post-LT MACE, all-cause mortality, and cardiovascular mortality. These models can support clinicians in recipient selection and help screen individuals who may be at elevated risk for post-transplantation MACE.Copyright © 2021 Elsevier Inc. All rights reserved.

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