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© 2025. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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 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.

Details

Title
A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation
Author
Ji Won Min  VIAFID ORCID Logo  ; Jae-Hong, Min  VIAFID ORCID Logo  ; Se-Hyun Chang  VIAFID ORCID Logo  ; Byung Ha Chung  VIAFID ORCID Logo  ; Koh, Eun Sil  VIAFID ORCID Logo  ; Kim, Young Soo  VIAFID ORCID Logo  ; Kim, Hyung Wook  VIAFID ORCID Logo  ; Ban, Tae Hyun  VIAFID ORCID Logo  ; Shin, Seok Joon  VIAFID ORCID Logo  ; In Young Choi  VIAFID ORCID Logo  ; Yoon, Hye Eun  VIAFID ORCID Logo 
First page
e62853
Section
Artificial Intelligence
Publication year
2025
Publication date
2025
Publisher
Gunther Eysenbach MD MPH, Associate Professor
e-ISSN
1438-8871
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3222367853
Copyright
© 2025. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.