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