Nomogram model to predict in-hospital mortality in lung transplant recipients: A retrospective cohort study

Lung transplantation (LTx) is a vital treatment for advanced lung disease. However, in-hospital mortality remains a significant challenge. Identifying perioperative risk factors is crucial for improving outcomes. This study aimed to develop a predictive nomogram for in-hospital mortality in lung transplant recipients.

We retrospectively analyzed 1355 LTx recipients at Wuxi People's Hospital (2015-2024). Least absolute shrinkage and selection operator (LASSO) regression identified predictors of in-hospital mortality. A nomogram was constructed and validated using calibration curves, decision curve analysis, and receiver operating characteristic (ROC) curves.

The overall in-hospital mortality rate was 14.5 %, with deaths occurring within 20 days post-surgery. Independent predictors included age, ICU stay duration, cold ischemia time, blood transfusion, C-reactive protein, serum sodium, primary graft dysfunction, renal replacement therapy, and extracorporeal membrane oxygenation support. The nomogram showed superior predictive accuracy (AUROC: 0.874, 95 % CI 0.843-0.905) compared to SOFA (AUROC: 0.772, 95 % CI 0.732-0.812) and APACHE II scores (AUROC: 0.718, 95 % CI 0.673-0.764). Calibration and decision curve analyses confirmed its accuracy and clinical utility.

This study highlights key perioperative risk factors for in-hospital mortality in LTx recipients. The developed nomogram provides a reliable tool for predicting early mortality, aiding clinicians in optimizing patient management.