Machine Learning Based Prediction of 1-year Survival after Isolated Heart Transplant

Abstract

<h3>Purpose</h3> Given limitations of traditional scores of 1-year survival after isolated heart transplantation (HT), we sought to create machine learning algorithms (ML) and compare with traditional scores. <h3>Methods</h3> We included adults HT recipients from the UNOS database between 2010-2019. The study cohort was randomly split in a derivation and a validation dataset (3:1 ratio) that were used to train and test the following ML algorithms after feature selection. Additionally, a logistic regression model was implemented and the IMPACT score was calculated. Solely pre-transplant clinical and laboratory variables of the UNOS database were used for all risk scores. <h3>Results</h3> The study cohort comprised of 18,625 patients (53 ± 13 years, 73% males). At 1-year after cardiac transplant, there were 2334 (12.53%) deaths. A total of 134 pre-transplant variables were analyzed. Receiver-operator curves for all ML models and the IMPACT score are shown in Figure. Areas under the curve were 0.694, 0.650, 0.649, 0.647, 0.512 for the Adaboost, LR, DT, SVM, KNN models respectively, whereas the IMPACT score had an AUC of 0.569. <h3>Conclusion</h3> ML models created and validated by using a contemporary cohort of the UNOS database showed high accuracy in predicting 1-year survival after HT.