Journal of cardiothoracic and vascular anesthesia · 2025
Artificial Intelligence-Driven Prediction of Acute Kidney Injury Following Acute Type A Aortic Dissection Surgery in a Chinese Population.
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Chen Zheyuan, Lu Xuran, Liu Maomao, Yu Yan, Yu Li, Cheng Sihao, Zhu Zhihui, Lai Yongqiang, Liu Nan
Аннотация
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.
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.