Development and Validation of a Hybrid Machine Learning Model to Predict Lung Transplant Outcomes.

Importance: Long-term survival after a lung transplant remains highly variable, and existing risk stratification tools have limited accuracy, clinical utility, and interpretability. Objective: To develop, validate, and assess the clinical utility of an interpretable hybrid machine learning model using United Network for Organ Sharing data to predict time to death or retransplant at 1, 5, and 10 years after a lung transplant. Design, Setting, and Participants: This prognostic study used data from a United Network for Organ Sharing-Organ Procurement and Transplantation Network cohort that underwent lung transplants between October 16, 1987, and March 26, 2025. The study included 51 933 adult patients (aged ≥18 years) undergoing their first lung transplant in the US. The data were temporally split into a development cohort (1987-2014; n = 26 682) and a testing cohort (2015-2025; n = 25 251). The development cohort was divided into a training set (n = 24 014) and validation set (n = 2668) for model selection and hyperparameter tuning. Main Outcomes and Measures: The outcome was the time to death or retransplant. The model was developed using the AutoScore-Survival framework, which uses a random survival forest for variable selection and Cox proportional hazards regression for scoring. Performance was assessed by discrimination (a time-dependent area under the curve [AUC], the Harrell C-index, and integrated AUC [iAUC]), calibration (plots, slope, observed-to-expected event ratio, and Brier score), and clinical utility (decision curve analysis). Results: Among 51 933 recipients (median age, 59 years [5th-95th percentile range, 27-71 years]; 57.6% men), the median follow-up was 8.97 years (95% CI, 8.93-8.99 years), and 31 865 (61.4%) experienced an event. Nine predictors were selected for the final model: length of hospital stay, recipient age, single vs double transplant, posttransplant ventilation support, prior cardiac surgery, creatinine level at transplant, functional status, total bilirubin level, and donor age. In the unseen testing set, the model showed moderate discrimination with an iAUC of 0.61 (95% CI, 0.60-0.63) and a C-index of 0.64 (95% CI, 0.63-0.64). The time-dependent AUC was 0.61 (95% CI, 0.52-0.70) at 1 year, 0.59 (95% CI, 0.53-0.65) at 5 years, and 0.72 (95% CI, 0.55-0.85) at 10 years. The model was well calibrated, and the decision curve analysis demonstrated a consistent net benefit across threshold probabilities. Conclusions and Relevance: In this large prognostic study, the interpretable hybrid model provided practical, personalized risk stratification for lung transplant outcomes. With moderate discrimination, good calibration, and clear clinical utility, the model supports shared decision-making and is accessible via a web-based calculator.