Development and validation of a prediction model for acute renal failure after lung transplantation
- VernacularTitle:肺移植后急性肾衰竭预测模型的开发与验证
- Author:
Sheng CHEN
1
;
Chen PAN
2
;
Shaoxiang LI
1
;
Bingzheng ZHANG
1
;
Wenjie JIAO
1
Author Information
1. Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao, 266071, Shandong, P. R. China
2. Medical Department of Nantong University, Nantong, 226000, Jiangsu, P. R. China
- Publication Type:Journal Article
- Keywords:
Lung transplantation;
acute renal failure;
risk factors;
nomogram
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(04):473-481
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify and analyze risk factors for acute renal failure (ARF) following lung transplantation and to develop a predictive model. Methods Data for this study were obtained from the United Network for Organ Sharing (UNOS) database, encompassing patients who underwent unilateral or bilateral lung transplantation between 2015 and 2022. We analyzed both preoperative and postoperative clinical characteristics of the patients. A combined approach utilizing random forest and least absolute shrinkage and selection operator (LASSO) regression was employed to identify key factors associated with the incidence of ARF post-transplantation, based on which a nomogram model was developed. The predictive performance of the constructed model was evaluated in both training and validation sets, using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics to verify and compare model effectiveness. Results A total of 15 110 lung transplantation patients were included in the study, consisting of6 041 males and 9 069 females, with a median age of 62.00 years (interquartile range: 54.00 to 67.00). The analysis revealed statistically significant differences between postoperative renal dialysis and non-dialysis patients regarding preoperative lung diagnosis, estimated glomerular filtration rate (eGFR), mechanical ventilation, preoperative ICU treatment, extracorporeal membrane oxygenation (ECMO) support, infections occurring within two weeks prior to transplantation, Karnofsky Performance Status (KPS) score, waitlist duration, double-lung transplantation, and ischemia time (P<0.05). Five key variables associated with ARF after lung transplantation were identified through random forest and LASSO regression: recipients’ eGFR, preoperative ICU treatment, ECMO support, bilateral lung transplantation, and ischemia time. A nomogram model was subsequently established. Model evaluation demonstrated that the constructed predictive model achieved high accuracy in both training and validation sets, with favorable AUC values, confirming its validity and reliability. Conclusion This study identifies common risk factors for ARF following lung transplantation and introduces an effective predictive model with potential clinical applications.
