Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China

Background: Heart transplantation (HTx) remains the gold-standard treatment for end-stage heart failure. The aim of this study was to establish a risk-prediction model for assessing prognosis of HTx using machine-learning approach.

Methods: Consecutive recipients of orthotopic HTx at our institute between January 1st, 2015 and December 31st, 2018 were included in this study. The primary outcome was 1-year mortality. Least absolute shrinkage and selection operator method was used to select variables and seven different machine-learning approaches were employed to develop the risk-prediction model. Bootstrap method was used for model validation. Shapley Additive exPlanations (SHAP) method was used for model interpretation.

Results: 381 recipients were included with average age of 43.783 years old. Albumin, recipient age and left atrium diameter ranked top three most important variables that affected the 1-year mortality of HTx. Other important variables included red blood cell, hemoglobin, lymphocyte%, smoking history, use of lyophilized rhBNP, use of Levosimendan, hypertension, cardiac surgery history, malignancy and endotracheal intubation history. Random Forest (RF) model achieved the best area under curves (AUC) of 0.801 and gradient boosting machine (GBM) showed the best sensitivity of 0.271. SHAP method was introduced to display the RF model's predicting processes of "survival" or "death" in individual level.

Conclusions: We established the risk-prediction model for postoperative prognosis of HTx patients by using machine learning method and demonstrated that the RF model performed the highest discrimination with the largest AUC when validated. This prediction model could help to recognize high-risk HTx recipients, provide personalized therapy plan and reduce organ wastage.