PURPOSE: Orthotopic heart transplantation (OHT) is a life-saving procedure for advanced end-stage heart failure patients. Given the conservative allocation policy and mismatch of donors and wait-listed patients, the prediction of post OHT outcomes including survival and graft failure (GF) can help optimize organ allocation. We aimed to develop a risk prediction model using machine learning algorithm to predict survival and GF 5 years after OHT. METHODS: Using International society of heart and lung transplant (ISHLT) registry data, we retrospectively analyzed 15236 patients who underwent OHT from January 2005 through December 2009. 342 variables including pre/ post OHT, discharge and follow up information were extracted and used to develop a risk prediction model utilizing a gradient boosted classification tree algorithm (GBM) to predict the risk of graft failure and mortality 5 yrs. after hospital discharge. Variables with missing observations were handled internally by the GBM algorithm. A 10 fold cross-validation repeated 5 times was used to estimate the model's external performance. Receiver operator curve (ROC) for GBM model was calculated to predict survival and GF 5 years post OHT. RESULTS: The mean duration of follow up of 4.7 years (median 5 years). The mortality and graft failure 5 years post- OHT was 27.3% (n = 4161) & 28 %( n=4276) respectively. The area under ROC (AUC) to predict 5 yr. mortality and GF are 0.717 (95% CI 0.696- 0.737) & 0.661 (95% CI 0.640-0.683) respectively. Length of stay, recipient and donor age, recipient body mass index and ischemic time had the highest weight in predicting 5-year mortality and graft failure. The subgroup of patients with age>60yrs had a higher AUC for both the models. CONCLUSION: The GBM model has an excellent accuracy in predicting 5 yr.-mortality and graft failure in patients undergoing OHT.
ASJC Scopus subject areas
- Pulmonary and Respiratory Medicine
- Cardiology and Cardiovascular Medicine