A machine learning-driven app for predicting the need for post-operative respiratory support in liver transplant recipients.

BACKGROUNDS: Liver transplantation (LT) is a life-saving procedure for patients with end-stage liver disease, yet post-operative complications, particularly the need for respiratory support, remain a significant challenge. We aimed to develop and validate a machine learning (ML)-based predictive tool for postoperative respiratory support requirement in liver transplant recipients. METHODS: This single-center retrospective study was conducted at Zhongshan Hospital, Fudan University (Shanghai, China) from January 2018 to October 2023. Following data preprocessing, key variables were selected through univariate analysis, recursive feature elimination (RFE), Chi-square test, and correlation analysis. Nine ML models were initially constructed and optimized via grid search with 5-fold cross-validation. The final model was selected based on area under the curve (AUC), accuracy, sensitivity, specificity, and F1-score, followed by comparative analysis with conventional scoring systems. Model interpretability was achieved using shapley additive explanations (SHAP) analysis, providing both global and local explanations. For clinical implementation, we developed an online application platform for real-time prediction. RESULTS: The study included 1121 liver transplant recipients, divided into a discovery cohort (n = 749) and validation cohort (n = 372). Significant differences (P < 0.05) were observed between patients requiring versus not requiring respiratory support across multiple preoperative, intraoperative, and postoperative parameters. After hyperparameter optimization, the random forest (RF), stochastic gradient boosting (SGB), and logistic regression (LR) models were applied to the validation cohort, with RF ultimately being selected as the final predictive tool, achieving an AUC of 0.790 (95 % CI: 0.723-0.857) in the test set and 0.713 (95 % CI: 0.658-0.767) in the validation cohort, significantly outperforming both model for end-stage liver disease (MELD) and acute physiology and chronic health evaluation II (APACHE II) scores. SHAP analysis revealed complex bidirectional relationships between predictors and outcomes, with certain variables showing both protective and risk-enhancing effects depending on clinical context. CONCLUSIONS: Based on large-scale clinical data, we developed a robust predictive model that can effectively assess the need for postoperative respiratory support in liver transplant recipients, thereby facilitating clinical decision-making and potentially improving patient outcomes. However, future multi-center validation was warranted to confirm generalizability.