Prediction of Outcomes after Heart Transplantation Using Machine Learning Techniques

M. A. Villela, C. A. Bravo, M. Shah, S. Patel, U. P. Jorde, J. Stehlik, A. Castellanos

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


PURPOSE: Machine learning (ML) techniques can improve predictive modeling over more traditional methods by identifying higher dimensionality and non-linear relationships between variables. We hypothesized that an AutoML algorithm would be superior to a logistic regression (LR) model for prediction of outcomes in HT. METHODS: The UNOS database was queried for patients receiving single-organ heart transplantation between 2006-2016. Pre-transplant variables for the donor and recipient were used for the prediction of one-year mortality or re-transplant. Auto machine-learning (AutoML) with stacking of Gradient Boosting Machine (GBM) derived algorithms was used to create a ML predictive meta-model. These results were compared to a traditional LR model using receiving operating characteristic (ROC) values. RESULTS: During this time period 18,612 patients with HT were identified. Observed one-year mortality or re-transplant was 11.5%. The AutoML derived model performed modestly (ROC=0.66, Figure 1) but showed improvement in outcome prediction over the LR model (ROC=0.62, Figure 2). Strongest predictive variables in the LR model were recipient bilirubin, creatinine, mechanical ventilation, donor age and ischemic time. The meta-model structure of AutoML precludes direct assessment of individual variable weight. CONCLUSION: Using contemporary input from the UNOS database, AutoML meta-modeling outperformed LR for prediction of one-year outcomes in HT. Automation of predictive modeling using ML in HT is powerful, albeit limited by the "black box" effect on individual variables. AutoML in HT warrants further investigation.

ASJC Scopus subject areas

  • Surgery
  • Pulmonary and Respiratory Medicine
  • Cardiology and Cardiovascular Medicine
  • Transplantation

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