Can machine learning models using preoperative data accurately predict postoperative acute kidney injury in patients undergoing surgeries under general anesthesia?
Machine learning models using preoperative data can accurately predict the risk of postoperative acute kidney injury, potentially aiding in personalized risk management.
BACKGROUND: Postoperative acute kidney injury (AKI) is a significant risk associated with surgeries under general anesthesia, often leading to increased mortality and morbidity. Existing predictive models for postoperative AKI are usually limited to specific surgical areas or require external validation. OBJECTIVE: We proposed to build a prediction model for postoperative AKI using several machine learning methods. METHODS: -score. RESULTS: Among the 239,267 surgeries analyzed, 7935 cases of postoperative AKI were identified. The models, using 38 preoperative predictors, showed that deep neural network (AUC=0.832), light gradient boosting machine (AUC=0.836), and logistic regression (AUC=0.825) demonstrated superior performance in predicting AKI risk. The deep neural network model was then developed into a user-friendly website for clinical use. CONCLUSIONS: Our study introduces a robust, high-performance AKI risk prediction system that is applicable in clinical settings using preoperative data. This model's integration into a user-friendly website enhances its clinical utility, offering a significant step forward in personalized patient care and risk management.
Min et al. (Thu,) studied this question.
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