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<p>Augmented renal clearance (ARC) frequently occurs in critically ill septic patients and is known to impact survival outcomes. To address this, we aimed to develop an interpretable machine learning model for early mortality prediction in this high-risk population using the MIMIC-IV database. A total of 518 septic patients with ARC were enrolled, with a 28-day mortality rate of 17.2%. From the first 24&#xa0;h of ICU admission and the time of ARC onset, we extracted 162 and 88 covariates, respectively. Following data imputation, we applied a three-stage feature selection strategy, consisting of the EPV principle, Bootstrap LASSO with &gt; 88.5% selection frequency, and clinical expert review. This process yielded 9 and 10 key predictors from the two timepoints for subsequent model construction. Among nine machine learning algorithms evaluated, XGBoost achieved the highest discriminative performance (AUC 0.80) using early ICU data. SHAP interpretability analysis revealed that respiratory rate, body temperature, and serum sodium were the most influential predictors. To facilitate further research and external validation, we developed a freely accessible online calculator (<a href="https://wuyunzhe.shinyapps.io/arc_prediction/">https://wuyunzhe.shinyapps.io/arc_prediction/</a>) that enables exploratory bedside risk assessment with automatic stratification. In conclusion, the XGBoost model, leveraging routinely collected early ICU data, provides an interpretable and clinically applicable tool for predicting 28-day mortality in septic patients with ARC, demonstrating potential for future clinical application after prospective validation.</p>

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Machine learning-based risk prediction of 28-day mortality for sepsis patients with augmented renal clearance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月23日

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  • Yunzhe Wu,
  • Fan Yang,
  • Hongjie Yang,
  • Tong Wu,
  • RuoYu Zhuang,
  • Xiaoli Wang,
  • Yide Lu,
  • Danfeng Dong

摘要

Augmented renal clearance (ARC) frequently occurs in critically ill septic patients and is known to impact survival outcomes. To address this, we aimed to develop an interpretable machine learning model for early mortality prediction in this high-risk population using the MIMIC-IV database. A total of 518 septic patients with ARC were enrolled, with a 28-day mortality rate of 17.2%. From the first 24 h of ICU admission and the time of ARC onset, we extracted 162 and 88 covariates, respectively. Following data imputation, we applied a three-stage feature selection strategy, consisting of the EPV principle, Bootstrap LASSO with > 88.5% selection frequency, and clinical expert review. This process yielded 9 and 10 key predictors from the two timepoints for subsequent model construction. Among nine machine learning algorithms evaluated, XGBoost achieved the highest discriminative performance (AUC 0.80) using early ICU data. SHAP interpretability analysis revealed that respiratory rate, body temperature, and serum sodium were the most influential predictors. To facilitate further research and external validation, we developed a freely accessible online calculator (https://wuyunzhe.shinyapps.io/arc_prediction/) that enables exploratory bedside risk assessment with automatic stratification. In conclusion, the XGBoost model, leveraging routinely collected early ICU data, provides an interpretable and clinically applicable tool for predicting 28-day mortality in septic patients with ARC, demonstrating potential for future clinical application after prospective validation.