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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月2日

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  • 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,
  • Li Yang

摘要

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.