Cong Xuhui, Zou Xuli, Zhu Ruilou, Li Yubao, Liu Lu, Zhang Jiaqiang
Department of Anesthesia and Perioperative Medicine, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Xinxiang Medical University, Xinxiang, Henan, China.
Front Physiol. 2025 Jul 17;16:1628450. doi: 10.3389/fphys.2025.1628450. eCollection 2025.
This 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.
A 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.
The 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, χ = 5.895, = 0.750). Decision curve analysis further confirmed the model's significant clinical utility.
This 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.
本研究提出了一种预测模型，旨在填补非心脏、非泌尿外科手术患者围手术期急性肾损伤（AKI）预测工具的空白，以改善临床决策和患者预后。
分析了40520例65岁及以上接受非心脏、非泌尿外科手术患者的回顾性队列。使用单变量逻辑回归和LASSO确定关键风险因素，同时应用多变量逻辑回归来开发和验证模型。
基于包括人口统计学数据、合并症和术中因素在内的18个关键变量的预测模型，在预测围手术期AKI方面具有很强的区分能力（AUC = 0.803；95% CI，0.783 - 0.823）。它在验证队列中也显示出良好的拟合度（Hosmer-Lemeshow检验，χ = 5.895， = 0.750）。决策曲线分析进一步证实了该模型具有显著的临床实用性。
该模型能有效预测围手术期AKI，为非心脏、非泌尿外科手术中的个性化风险评估和预防策略提供了有价值的工具。建议在不同人群中进一步验证以优化其临床应用。
