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Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study

Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study

When preadmission s Cr was not available, baseline creatinine was estimated using the MDRD (Modification of Diet in Renal Disease) equation by back calculation from an estimated glomerular filtration rate (GFR) of 75 m L/min/1.73 m2 [38]. Urine-based or creatinine-based criteria, or a combination of both criteria were used to determine if a patient meets the KDIGO AKI criteria. After determining sepsis and AKI separately, we applied the definition of SA-AKI from the ADQI 28 Working Group.

Wei Jiang, Yaosheng Zhang, Jiayi Weng, Lin Song, Siqi Liu, Xianghui Li, Shiqi Xu, Keran Shi, Luanluan Li, Chuanqing Zhang, Jing Wang, Quan Yuan, Yongwei Zhang, Jun Shao, Jiangquan Yu, Ruiqiang Zheng

J Med Internet Res 2025;27:e62932

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