Development and validation of a risk prediction model for perioperative acute kidney injury in non-cardiac and non-urological surgery patients: a retrospective cohort study

Development and validation of a risk prediction model for perioperative acute kidney injury in non-cardiac and non-urological surgery patients: a retrospective cohort study

BackgroundThis study presents a predictive model designed to fill the gap in tools for predicting perioperative acute kidney injury (AKI) in patients undergoing non-cardiac, non-urological surgeries, with the goal of improving clinical decision-making and patient outcomes.MethodsA retrospective coho...

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Bibliographic Details
Main Authors: Xuhui Cong, Xuli Zou, Ruilou Zhu, Yubao Li, Lu Liu, Jiaqiang Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Physiology
Subjects:
prediction model
risk assessment
perioperative acute kidney injury (AKI)
non-cardiac
nonurological surgeries
clinical decision-making
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2025.1628450/full
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Summary:BackgroundThis study presents a predictive model designed to fill the gap in tools for predicting perioperative acute kidney injury (AKI) in patients undergoing non-cardiac, non-urological surgeries, with the goal of improving clinical decision-making and patient outcomes.MethodsA retrospective cohort of 40,520 patients aged 65 and older who underwent non-cardiac, non-urological surgeries was analyzed. Key risk factors were identified using univariable logistic regression and LASSO, while multivariate logistic regression was applied to develop and validate the model.ResultsThe prediction model, based on 18 key variables including demographic data, comorbidities, and intraoperative factors, demonstrated strong discriminatory power for predicting perioperative AKI (AUC = 0.803; 95% CI, 0.783–0.823). It also showed a good fit in the validation cohort (Hosmer–Lemeshow test, χ2 = 5.895, P = 0.750). Decision curve analysis further confirmed the model’s significant clinical utility.ConclusionThis model effectively predicts perioperative AKI, providing a valuable tool for personalized risk assessment and prevention strategies in non-cardiac, non-urological surgeries. Further validation in diverse populations is recommended to optimize its clinical application.
ISSN:1664-042X

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