TY  - JOUR
AU  - Min, Ji Won
AU  - Min, Jae-Hong
AU  - Chang, Se-Hyun
AU  - Chung, Byung Ha
AU  - Koh, Eun Sil
AU  - Kim, Young Soo
AU  - Kim, Hyung Wook
AU  - Ban, Tae Hyun
AU  - Shin, Seok Joon
AU  - Choi, In Young
AU  - Yoon, Hye Eun
PY  - 2025
DA  - 2025/4/9
TI  - A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation
JO  - J Med Internet Res
SP  - e62853
VL  - 27
KW  - acute kidney injury
KW  - general surgery
KW  - deep neural networks
KW  - machine learning
KW  - prediction model
KW  - postoperative care
KW  - surgery
KW  - anesthesia
KW  - mortality
KW  - morbidity
KW  - retrospective study
KW  - cohort analysis
KW  - hospital
KW  - South Korea
KW  - logistic regression
KW  - user-friendly
KW  - patient care
KW  - risk management
KW  - artificial intelligence
KW  - digital health
AB  - 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 network, 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 the initiation of renal replacement therapy within 30 days after surgery. The performance of the models was analyzed using the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, precision, sensitivity (recall), specificity, and F1-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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2025/1/e62853
UR  - https://doi.org/10.2196/62853
DO  - 10.2196/62853
ID  - info:doi/10.2196/62853
ER  -