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Background <p>Critically ill patients in intensive care units (ICUs) exhibit high mortality due to complex interactions among metabolic instability, organ dysfunction, and therapeutic interventions. While conventional severity scores like SOFA and APACHE II are widely used, they insufficiently integrate dynamic metabolic markers.</p> Methods <p>We performed a retrospective cohort study of 1,004 adult ICU patients. Demographic, clinical, and laboratory data were analyzed using multivariate logistic regression and machine learning algorithms (Random Forest, XGBoost). Model performance was assessed using AUC-ROC, calibration slope, and sensitivity at fixed specificity threshold.</p> Results <p>The ICU mortality was 30.7%. Independent predictors of mortality included SOFA score ≥ 8 (aOR = 4.0), mechanical ventilation (aOR = 3.1), hyperlactatemia ≥ 4 mmol/L (aOR = 2.6), vasopressor use (aOR = 2.7), and GCS ≤ 8 (aOR = 2.4). The XGBoost model achieved superior discrimination (AUC = 0.86; 95% CI: 0.83–0.89) compared to logistic regression (AUC = 0.84), SOFA (AUC = 0.72), and APACHE II (AUC = 0.75). A validated nomogram incorporating eight key predictors stratified mortality risk into four actionable tiers. Subgroup analysis confirmed robust performance across age strata, sepsis status, and AKI stages.</p> Conclusions <p>Integrating metabolic markers with clinical severity scores enhances ICU mortality prediction. Our nomogram offers a practical, interpretable tool for bedside risk stratification and clinical decision support.</p>

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Predicting mortality in critically ill patients: a machine learning approach to electrolyte imbalances and clinical risk factors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月17日

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  • Hesham Kamal Habeeb Keryakos,
  • Walid Taha Hussein,
  • Mostafa Ahmed El-Sayed Abu-El-Ela,
  • Aml Kamal Helmy

摘要

Background

Critically ill patients in intensive care units (ICUs) exhibit high mortality due to complex interactions among metabolic instability, organ dysfunction, and therapeutic interventions. While conventional severity scores like SOFA and APACHE II are widely used, they insufficiently integrate dynamic metabolic markers.

Methods

We performed a retrospective cohort study of 1,004 adult ICU patients. Demographic, clinical, and laboratory data were analyzed using multivariate logistic regression and machine learning algorithms (Random Forest, XGBoost). Model performance was assessed using AUC-ROC, calibration slope, and sensitivity at fixed specificity threshold.

Results

The ICU mortality was 30.7%. Independent predictors of mortality included SOFA score ≥ 8 (aOR = 4.0), mechanical ventilation (aOR = 3.1), hyperlactatemia ≥ 4 mmol/L (aOR = 2.6), vasopressor use (aOR = 2.7), and GCS ≤ 8 (aOR = 2.4). The XGBoost model achieved superior discrimination (AUC = 0.86; 95% CI: 0.83–0.89) compared to logistic regression (AUC = 0.84), SOFA (AUC = 0.72), and APACHE II (AUC = 0.75). A validated nomogram incorporating eight key predictors stratified mortality risk into four actionable tiers. Subgroup analysis confirmed robust performance across age strata, sepsis status, and AKI stages.

Conclusions

Integrating metabolic markers with clinical severity scores enhances ICU mortality prediction. Our nomogram offers a practical, interpretable tool for bedside risk stratification and clinical decision support.