Predictors of one-year outcome after cardiac re-transplantation: Machine learning analysis.

Overview

abstract

BACKGROUND: As cardiac re-transplantation is associated with inferior outcomes compared with primary transplantation, allocating scarce resources to appropriate re-transplant candidates is important. The aim of this study is to elucidate the factors associated with 1-year mortality in cardiac re-transplantation using the random forests algorithm for survival analysis. METHODS: We retrospectively reviewed the United Network for Organ Sharing registry and identified all adult (>17 years old) recipients who underwent cardiac re-transplantation between January 2000 and March 2020. The random forest algorithm on Cox modeling was used to calculate the variable importance (VIMP) of independent variables for contributing to one-year mortality. RESULTS: A total of 1294 patients underwent cardiac re-transplantation. Of these, 137 patients were re-transplanted within one year of their first transplant, while 1157 patients were re-transplanted more than one year after their first transplant. One-year mortality was significantly higher for patients receiving early transplantation compared with those receiving late transplantation (Early 40.6% vs. Late 13.6%, log-rank P<0.001). Machine learning analysis showed that total bilirubin (>2 mg/dl) (VIMP, 2.99%) was an independent predictor of one-year mortality after early re-transplant. High BMI (>30.0 kg/m2) (VIMP, 1.43%) and ventilator dependence (VIMP, 1.47%) were independent predictors of one-year mortality for the late re-transplantation group. CONCLUSION: Machine learning showed that optimal one-year survival following cardiac re-transplantation was significantly related to liver function in early re-transplantation, and to obesity and preoperative ventilator dependence in late re-transplantation. This article is protected by copyright. All rights reserved.