AUTHOR=Fessler Julien , Gouy-Pailler Cédric , Ma Wenting , Devaquet Jerôme , Messika Jonathan , Glorion Matthieu , Sage Edouard , Roux Antoine , Brugière Olivier , Vallée Alexandre , Fischler Marc , Le Guen Morgan , Komorowski Matthieu 

TITLE=Machine Learning for Predicting Pulmonary Graft Dysfunction After Double-Lung Transplantation: A Single-Center Study Using Donor, Recipient, and Intraoperative Variables

JOURNAL=Transplant International

VOLUME=Volume 38 - 2025

YEAR=2025

URL=https://www.frontierspartnerships.org/journals/transplant-international/articles/10.3389/ti.2025.14965

DOI=10.3389/ti.2025.14965

ISSN=1432-2277

ABSTRACT=Grade 3 primary graft dysfunction at 72 h (PGD3-T72) is a severe complication following lung transplantation. We aimed to develop an intraoperative machine-learning tool to predict PGD3-T72. We retrospectively analyzed perioperative data from 477 patients who underwent double-lung transplantation at a single center between 2012 and 2019. Data were structured into nine chronological steps, and supervised machine-learning models (XGBoost and logistic regression) were trained to predict PGD3-T72, with hyperparameters optimized via grid search and cross-validation. PGD3-T72 occurred in 83 patients (17.3%). XGBoost outperformed logistic regression, achieving peak performance at second graft implantation with an AUROC of 0.84 IQR: 0.065, p < 0.001, with a sensitivity of 0.81 and a specificity of 0.68. The top predictors included extracorporeal membrane oxygenation (ECMO) use, blood lactate levels, PaO2/FiO2 ratio, and total lung capacity mismatch. Subgroup analyses confirmed robustness across ECMO and non-ECMO cohorts. PGD3-T72 can be reliably predicted intraoperatively, offering potential for early intervention.