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

作者：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 · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/62932 · 被引用次数：35 · 研究领域：Acute Kidney Injury Research、Sepsis Diagnosis and Treatment、Chronic Kidney Disease and Diabetes

BACKGROUND: Persistent sepsis-associated acute kidney injury (SA-AKI) shows poor clinical outcomes and remains a therapeutic challenge for clinicians. Early identification and prediction of persistent SA-AKI are crucial. OBJECTIVE: The aim of this study was to develop and validate an interpretable machine learning (ML) model that predicts persistent SA-AKI and to compare its diagnostic performance with that of C-C motif chemokine ligand 14 (CCL14) in a prospective cohort. METHODS: The study used 4 retrospective cohorts and 1 prospective cohort for model derivation and validation. The derivation cohort used the MIMIC-IV database, which was randomly split into 2 parts (80% for model construction and 20% for internal validation). External validation was conducted using subsets of the MIMIC-III dataset and e-ICU dataset, and retrospective cohorts from the intensive care unit (ICU) of Northern Jiangsu People's Hospital. Prospective data from the same ICU were used for validation and comparison with urinary CCL14 biomarker measurements. Acute kidney injury (AKI) was defined based on serum creatinine and urine output, using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Routine clinical data within the first 24 hours of ICU admission were collected, and 8 ML algorithms were used to construct the prediction model. Multiple evaluation metrics, including area under the receiver operating characteristic curve (AUC), were used to compare predictive performance. Feature i...