Machine Learning for 1-Year Graft Failure Prediction in Lung Transplant Recipients: The Korean Organ Transplantation Registry.

BACKGROUND

In regions with limited donor availability, optimizing efficiency in lung transplant decision-making is crucial. Preoperative prediction of 1-year graft failure can enhance candidate selection and clinical decision-making.

METHODS

We utilized data from the Korean Organ Transplantation Registry to develop and validate a deep learning-based model for predicting 1-year graft failure after lung transplantation. A total of 240 cases were analyzed using 5-fold cross-validation. Among 25 preoperative factors associated with 1-year graft failure, we selected the top 9 variables with coefficients ≥ 0.25 for model development.

RESULTS

Of the 240 lung transplant recipients, 55 (22.92%) developed graft failure within 1 year, while 185 survived. The final predictive model incorporated nine key pretransplant factors: age, bronchiolitis obliterans syndrome after hematopoietic cell transplantation, pretransplant bacteremia, bronchiectasis, creatinine, diabetes, positive human leukocyte antigen crossmatch, panel reactive antibody 1 peak mean fluorescence intensity, and pretransplant steroid use. The multilayer perceptron model demonstrated strong predictive performance, achieving an area under the curve of 0.780 and an accuracy of 0.733.

CONCLUSIONS

Our machine learning-based model effectively predicts 1-year graft failure in lung transplant recipients using a minimal set of pretransplant variables. Further validation is needed to confirm its clinical applicability.