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 1‐year mortality.
Results
A total of 1294 patients underwent cardiac re‐transplantation. Of these, 137 patients were re‐transplanted within 1 year of their first transplant, while 1157 patients were re‐transplanted more than 1 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 < .001). Machine learning analysis showed that total bilirubin (> 2 mg/dl) (VIMP, 2.99%) was an independent predictor of 1‐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 1‐year mortality for the late re‐transplantation group.
Conclusion
Machine learning showed that optimal 1‐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.