BMC Nephrology · Published 2026-07-28 · DOI 10.1186/s12882-026-05234-z
Kai Xu, Jiang Liu, Yumeng Ji, Jian Song, Shiqi Gao, Chenyu Zhou, Juntao Qiu, Cuntao Yu
Abstract Background Postoperative continuous renal replacement therapy (CRRT) initiation for severe acute kidney injury is a clinically consequential complication after open thoracoabdominal aortic repair. We aimed to develop an interpretable machine-learning (ML) model for predicting postoperative CRRT in this high-risk population. Methods This single-center retrospective cohort study included consecutive adult patients who underwent open thoracoabdominal aortic repair between January 2010 and December 2025. After exclusions, 372 patients were analyzed, including 66 (17.7%) who initiated postoperative CRRT. The cohort was randomly divided into a training set (75%, n = 278) and an internal test set (25%, n = 94). After multistep feature selection, six routinely available perioperative predictors were retained. Eight ML models and a baseline logistic regression model were developed. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results In bootstrap internal validation, Light Gradient Boosting Machine showed the highest optimism-corrected AUC of 0.814 (95% CI, 0.773–0.856) and the lowest optimism-corrected Brier score of 0.131 (95% CI, 0.112–0.150). However, performance differences among several models were modest. LightGBM was therefore selected for interpretation in this internally validated dataset. SHAP analysis identified surgery duration, D-dimer, serum creatinine, intraoperative red blood cell transfusion, maximum intraoperative lactate, and Crawford extent II as important contributors to predicted CRRT risk. Conclusions A parsimonious LightGBM model showed favorable internally validated performance for predicting postoperative CRRT after open thoracoabdominal aortic repair. Given the single-center design, limited event number, and lack of external validation, the model should be considered exploratory and requires external validation and potential recalibration before clinical implementation. Clinical trial number Not applicable.
Abstract from DOAJ. Public domain (CC0 1.0).
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Xu, K., Liu, J., Ji, Y., et al. (2026). Machine learning–based prediction of postoperative continuous renal replacement therapy initiation after open thoracoabdominal aortic repair. BMC Nephrology. https://doi.org/10.1186/s12882-026-05234-z