Translational pediatrics · 2025
A machine learning-based model for predicting the postoperative risk of acute kidney injury in neonates.
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He Liping, Gao Tianyin, Tang Yanli, Jin Saifen, Zhuang Manli
Аннотация
BACKGROUND: Acute kidney injury (AKI) is a serious postoperative complication in hospitalized neonates. We aimed to develop and evaluate a machine learning (ML) model for predicting the risk of postoperative AKI in neonates.
METHODS: The clinical records of 2,025 neonates were collected, and the patients were randomly divided into training and test sets. The outcome variable was the occurrence of postoperative AKI, and the models incorporated 25 predictive variables, including demographics, intraoperative infusions, and postoperative indicators. ML models were developed using six different algorithms on the training set, and their performance was assessed on the test set using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The model with the best AUC was selected for validation in the test set. The association between the risk factors and postoperative AKI was interpreted using the SHapley Additive exPlanations (SHAP) method.
RESULTS: A total of 110 neonatal patients (5.43%) developed AKI following surgery. Patient age, operation duration, and urine output were the three most important predictors of AKI. Among the tested models, the logistic regression (LR) algorithm was the best predictor of postoperative AKI, achieving the highest AUC [median, 0.807; 95% confidence interval (CI): 0.701-0.897] and the highest sensitivity (median, 0.733; 95% CI: 0.5-0.938). The SHAP method was used to illustrate the prediction process of the LR model for neonatal postoperative AKI at the level of individual patients.
CONCLUSIONS: The ML model that uses the LR algorithm with eight commonly measured variables could serve as a tool to predict postoperative AKI in neonates.
METHODS: The clinical records of 2,025 neonates were collected, and the patients were randomly divided into training and test sets. The outcome variable was the occurrence of postoperative AKI, and the models incorporated 25 predictive variables, including demographics, intraoperative infusions, and postoperative indicators. ML models were developed using six different algorithms on the training set, and their performance was assessed on the test set using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The model with the best AUC was selected for validation in the test set. The association between the risk factors and postoperative AKI was interpreted using the SHapley Additive exPlanations (SHAP) method.
RESULTS: A total of 110 neonatal patients (5.43%) developed AKI following surgery. Patient age, operation duration, and urine output were the three most important predictors of AKI. Among the tested models, the logistic regression (LR) algorithm was the best predictor of postoperative AKI, achieving the highest AUC [median, 0.807; 95% confidence interval (CI): 0.701-0.897] and the highest sensitivity (median, 0.733; 95% CI: 0.5-0.938). The SHAP method was used to illustrate the prediction process of the LR model for neonatal postoperative AKI at the level of individual patients.
CONCLUSIONS: The ML model that uses the LR algorithm with eight commonly measured variables could serve as a tool to predict postoperative AKI in neonates.