JMIR Publications

ABSTRACT

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:

We conducted a retrospective cohort analysis of noncardiac surgeries from 2009 to 2019 at seven university hospitals in South Korea. We evaluated six machine learning models: deep neural networks, logistic regression, decision tree, random forest, light gradient boosting machine, and naïve Bayes for predicting postoperative AKI, defined as a significant increase in serum creatinine or initiation of renal replacement therapy within 30 days post-surgery.

Results:

Performance of the models were analyzed using area under the curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, precision, sensitivity (recall), specificity and F1 score. The models, utilizing 38 preoperative predictors, showed that deep neural networks (AUC = 0.832), light gradient boosting machines (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 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.