Artificial Intelligence-Driven Prediction of Acute Kidney Injury Following Acute Type A Aortic Dissection Surgery in a Chinese Population

Zheyuan Chen; Xuran Lu; Maomao Liu; Yan Yu; Li Yu; Sihao Cheng; Zhihui Zhu; Yongqiang Lai; Nan Liu

doi:10.1053/j.jvca.2025.05.047

Artificial Intelligence-Driven Prediction of Acute Kidney Injury Following Acute Type A Aortic Dissection Surgery in a Chinese Population

J Cardiothorac Vasc Anesth. 2025 Jun 3:S1053-0770(25)00443-4. doi: 10.1053/j.jvca.2025.05.047. Online ahead of print.

Authors

Zheyuan Chen¹, Xuran Lu², Maomao Liu¹, Yan Yu¹, Li Yu¹, Sihao Cheng¹, Zhihui Zhu³, Yongqiang Lai³, Nan Liu⁴

Affiliations

¹ Center for Cardiac Intensive Care, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
² Center for Cardiac Intensive Care, Beijing Anzhen Hospital, Capital Medical University, Beijing, China; Department of Cardiac Surgery, The Seventh Hospital of Xinjiang Medical University, Urumqi, China.
³ Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
⁴ Center for Cardiac Intensive Care, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. Electronic address: ln9102@126.com.

PMID: 40581537
DOI: 10.1053/j.jvca.2025.05.047

Abstract

Objectives: To develop and validate machine learning (ML) models for predicting acute kidney injury (AKI) following acute type A aortic dissection (ATAAD) surgery.

Design: A retrospective single-center study.

Setting: Beijing Anzhen Hospital (November 2018 to October 2023).

Participants: 1350 patients with ATAAD.

Interventions: Predictive models have been developed using various ML algorithms to estimate the risk of postoperative AKI.

Measurements and main results: Patients were randomly divided into training (85%) and testing (15%) sets. Seven ML algorithms-Gradient Boosting Machine (GBM), LightGBM, Random Forest (RF), K-Nearest Neighbors (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Naive Bayes (NB), and Logistic Regression (LR)-were evaluated. Model performance was assessed using SHapley Additive exPlanations (SHAP) analysis. A web-based application was developed using the optimal model. Postoperative AKI occurred in 586 patients (43.4%). The constructed models-GBM, LightGBM, RF, KNN, MLP-NN, NB, and LR-achieved areas under the receiver operating characteristic curves of 0.849 (95% CI 0.798-0.902), 0.874 (95% CI 0.831-0.918), 0.800 (95% CI 0.737-0.855), 0.672 (95% CI 0.598-0.739), 0.529 (95% CI 0.486-0.574), 0.833 (95% CI 0.775-0.886), and 0.866 (95% CI 0.810-0.912), respectively, in the testing set. Among these, the LightGBM model surpassed others in predictive accuracy, calibration, and clinical utility.

Conclusion: ML models, particularly LightGBM, accurately predict postoperative AKI risk in ATAAD patients, offering a promising tool to enhance perioperative management and patient outcomes.

Keywords: acute kidney injury; aortic dissection; artificial intelligence; machine learning; risk prediction.