Purpose We sought to develop and validate machine learning (ML) models
to increase the predictive accuracy of mortality after heart
transplantation (HT). Methods and results We included adult HT
recipients from the United Network for Organ Sharing (UNOS) database
between 2010 and 2018 using solely pre-transplant variables. The study
cohort comprised 18 625 patients (53 +/- 13 years, 73% males) and was
randomly split into a derivation and a validation cohort with a 3:1
ratio. At 1-year after HT, there were 2334 (12.5%) deaths. Out of a
total of 134 pre-transplant variables, 39 were selected as highly
predictive of 1-year mortality via feature selection algorithm and were
used to train five ML models. AUC for the prediction of 1-year survival
was .689, .642, .649, .637, .526 for the Adaboost, Logistic Regression,
Decision Tree, Support Vector Machine, and K-nearest neighbor models,
respectively, whereas the Index for Mortality Prediction after Cardiac
Transplantation (IMPACT) score had an AUC of .569. Local interpretable
model-agnostic explanations (LIME) analysis was used in the best
performing model to identify the relative impact of key predictors. ML
models for 3- and 5-year survival as well as acute rejection were also
developed in a secondary analysis and yielded AUCs of .629, .609, and
.610 using 27, 31, and 91 selected variables respectively. Conclusion
Machine learning models showed good predictive accuracy of outcomes
after heart transplantation
