Zhang Yuhui, Xu Damin, Gao Jianwei, Wang Ruiguo, Yan Kun, Liang Hong, Xu Juan, Zhao Youlu, Zheng Xizi, Xu Lingyi, Wang Jinwei, Zhou Fude, Zhou Guopeng, Zhou Qingqing, Yang Zhao, Chen Xiaoli, Shen Yulan, Ji Tianrong, Feng Yunlin, Wang Ping, Jiao Jundong, Wang Li, Lv Jicheng, Yang Li
Renal Division, Peking University First Hospital, Beijing, China.
Institute of Nephrology, Peking University, Beijing, China.
Nat Commun. 2025 Jan 2;16(1):68. doi: 10.1038/s41467-024-55629-5.
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.
急性肾损伤（AKI）的早期预测可为预防AKI提供关键契机。迄今为止，尚未有针对发展中国家普通住院患者AKI的预测模型发表。在此，我们展示了一种针对普通住院患者的简单、实时、可解释的AKI预测模型，该模型来自中国一家大型三级医院，已在五家独立的、地理位置不同、层级各异的医院得到验证。该模型包含20个易于获取的变量，在验证队列中显示出一致且高水平的预测辨别力，基于血清肌酐的AKI和48小时内严重AKI的受试者工作特征曲线下面积（AUC），对于迁移模型分别为0.74 - 0.85和0.83 - 0.90，对于重新拟合模型分别为0.81 - 0.90和0.88 - 0.95。通过最佳概率截断值，重新拟合模型在内部验证中可提前中位数72（24 - 198）小时预测AKI，在外部验证中可提前54 - 90小时预测。该模型未来的广泛应用可能为预防AKI提供一种有效、便捷且具有成本效益的方法。
