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Given the limited pool of donor organs, accurate predictions of survival onthe wait list and post transplantation are crucial for cardiac transplantationdecisions and policy. However, current clinical risk scores do not yieldaccurate predictions. We develop a new methodology (ToPs, Trees of Predictors)built on the principle that specific predictors should be used for specificclusters within the target population. ToPs discovers these specific clustersof patients and the specific predictor that perform best for each cluster. Incomparison with current clinical risk scoring systems, our method providessignificant improvements in the prediction of survival time on the wait listand post transplantation. For example, in terms of 3 month survival forpatients who were on the US patient wait list in the period 1985 to 2015, ourmethod achieves AUC of 0.847, the best commonly used clinical risk score(MAGGIC) achieves 0.630. In terms of 3 month survival/mortality predictions (incomparison to MAGGIC), holding specificity at 80.0 percents, our algorithmcorrectly predicts survival for 1,228 (26.0 percents more patients out of 4,723who actually survived, holding sensitivity at 80.0 percents, our algorithmcorrectly predicts mortality for 839 (33.0 percents) more patients out of 2,542who did not survive. Our method achieves similar improvements for other timehorizons and for predictions post transplantation. Therefore, we offer a moreaccurate, personalized approach to survival analysis that can benefit patients,clinicians and policymakers in making clinical decisions and setting clinicalpolicy. Because risk prediction is widely used in diagnostic and prognosticclinical decision making across diseases and clinical specialties, theimplications of our methods are far reaching.

Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors