Abstract:
Background: This study investigates the use of modern machine
learning (ML) techniques to improve prediction of survival after
orthotopic heart transplantation (OHT).
Methods: Retrospective study of adult patients undergoing
primary, isolated OHT between 2000-2019 as identified in the United
Network for Organ Sharing (UNOS) registry. The primary outcome was
one-year post-transplant survival. Patients were randomly divided into
training (80%) and validation (20%) sets. Dimensionality reduction and
data re-sampling were employed during training. Multiple machine
learning algorithms were combined into a final ensemble ML model.
Discriminatory capability was assessed using area under
receiver-operating-characteristic curve (AUROC), net reclassification
index (NRI), and decision curve analysis (DCA).
Results: A total of 33,657 OHT patients were evaluated.
One-year mortality was 11% (n=3,738). In the validation cohort, the
AUROC of singular logistic regression was 0.649 (95% CI 0.628-0.670)
compared to 0.691 (95% CI 0.671-0.711) with random forest, 0.691 (95%
CI 0.671-0.712) with deep neural network, and 0.653 (95% CI
0.632-0.674) with Adaboost. A final ensemble ML model was created that
demonstrated the greatest improvement in AUROC: 0.764 (95% CI
0.745-0.782) (p<0.001). The ensemble ML model improved
predictive performance by 72.9% ±3.8% (p<0.001) as assessed
by NRI compared to logistic regression. DCA showed the final ensemble
method improved risk prediction across the entire spectrum of predicted
risk as compared to all other models (p<0.001).
Conclusions: Modern ML techniques can improve risk prediction
in OHT compared to traditional approaches. This may have important
implications in patient selection, programmatic evaluation, allocation
policy, and patient counseling and prognostication.