Development and validation of a real-time prediction model for acute kidney injury in hospitalized patients

Yuhui Zhang, Damin Xu, Jianwei Gao, Ruiguo Wang, Kun Yan, Hong Liang, Juan Xu, Youlu Zhao, Xizi Zheng, Lingyi Xu, Jinwei Wang, Fude Zhou, Guopeng Zhou, Qingqing Zhou, Zhao Yang, Xiaoli Chen, Yulan Shen, Tianrong Ji, Yunlin Feng, Ping Wang, Jundong Jiao (), Li Wang (), Jicheng Lv () and Li Yang ()
Additional contact information
Yuhui Zhang: Peking University First Hospital
Damin Xu: Peking University First Hospital
Jianwei Gao: Digital Health China Technologies Co. Ltd
Ruiguo Wang: Digital Health China Technologies Co. Ltd
Kun Yan: Peking University
Hong Liang: Peking University
Juan Xu: Digital Health China Technologies Co. Ltd
Youlu Zhao: Peking University First Hospital
Xizi Zheng: Peking University First Hospital
Lingyi Xu: Peking University First Hospital
Jinwei Wang: Peking University First Hospital
Fude Zhou: Peking University First Hospital
Guopeng Zhou: Peking University First Hospital
Qingqing Zhou: Peking University First Hospital
Zhao Yang: Peking University First Hospital
Xiaoli Chen: Taiyuan Central Hospital
Yulan Shen: Beijing Miyun District Hospital
Tianrong Ji: The Second Affiliated Hospital of Harbin Medical University
Yunlin Feng: Sichuan Provincial People’s Hospital
Ping Wang: Peking University
Jundong Jiao: The Second Affiliated Hospital of Harbin Medical University
Li Wang: Sichuan Provincial People’s Hospital
Jicheng Lv: Peking University First Hospital
Li Yang: Peking University First Hospital

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. Here we show a simple, real-time, interpretable AKI prediction model for general hospitalized patients developed from a large tertiary hospital in China, which has been validated across five independent, geographically distinct, different tiered hospitals. The model containing 20 readily available variables demonstrates consistent, high levels of predictive discrimination in validation cohort, with AUCs for serum creatinine-based AKI and severe AKI within 48 h ranging from 0.74–0.85 and 0.83–0.90 for transported models and from 0.81–0.90 and 0.88–0.95 for refitted models, respectively. With optimal probability cutoffs, the refitted model could predict AKI at a median of 72 (24–198) hours in advance in internal validation, and 54–90 h in advance in external validation. Broad application of the model in the future may provide an effective, convenient and cost-effective approach for AKI prevention.

Date: 2025
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DOI: 10.1038/s41467-024-55629-5

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