Key result
CatBoost machine learning model predicts prolonged postoperative ICU stay with ~0.65 AUC using bedside features.
Why the study?
Prolonged postoperative ICU length of stay after CABG drives resource use yet remains difficult to anticipate at the 24-hour ICU evaluation moment.
Observational (n=12,891)
Yes
Effect estimate: AUC 0.6452 (95% CI 0.6311-0.6602)
A machine learning model using eight bedside features collected within 24 hours of ICU admission provides modest discrimination for predicting prolonged ICU stay after CABG, with 24-hour fluid intake being the most influential predictor.
Modest external AUC limits immediate clinical use for CABG ICU planning; extends ML prediction but leaves open need for prospective validation.
Background Prolonged postoperative intensive care unit (ICU) length of stay (LOS) after coronary artery bypass grafting (CABG) drives resource use yet remains difficult to anticipate at the 24-hour ICU evaluation moment when extended monitoring beyond 72 h is first considered. We developed and externally validated an interpretable machine-learning decision-support calculator for this deployment moment. Methods Adult CABG patients were identified from MIMIC-IV 3.1 ( n = 6,919; 7:3 stratified split for development) and eICU-CRD 2.0 ( n = 5,972; external validation). The outcome was prolonged ICU LOS (> 3 days). Elastic Net plus Boruta selected eight bedside features collected within the first 24 h of ICU admission: 24-hour fluid intake, Charlson Comorbidity Index (CCI), Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score II (SAPS-II), Glasgow Coma Scale (GCS), vasopressor use, congestive heart failure, and atrial fibrillation. Nine machine-learning algorithms were compared by 10-fold cross-validation paired t-tests with Bonferroni and Benjamini–Hochberg correction. Calibration metrics included Hosmer–Lemeshow test, Integrated Calibration Index (ICI), expected-to-observed ratio, intercept, and slope (full Methods). SHapley Additive exPlanations (SHAP) provided per-patient feature attribution. Results CatBoost was selected as the deployed model. On the MIMIC-IV internal test set ( n = 2,076), the area under the receiver-operating-characteristic curve (AUC) was 0.7739 (95% confidence interval 0.7379–0.8099), Hosmer–Lemeshow p = 0.224, calibration slope 0.973. On the eICU-CRD external cohort, AUC was 0.6452 (95% CI 0.6311–0.6602), calibration slope 0.998, ICI 0.023. At the prevalence-anchored threshold of 0.30, sensitivity was 0.55, specificity 0.65, positive predictive value 0.40, and negative predictive value 0.77. Decision Curve Analysis showed positive net benefit over treat-all and treat-none across t = 0.20–0.40. SHAP top-3 features were 24-hour fluid intake, CCI, and atrial fibrillation. Conclusions The model provides decision support at the post-CABG 24-hour ICU evaluation moment with modest discrimination and well-calibrated probabilities at deployment. The deployed online calculator renders per-patient feature contributions transparent at the bedside via SHAP; prospective validation is required before clinical deployment.
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Liu et al. (2026) conducted an observational in Coronary artery bypass grafting (CABG) (n=12,891). CatBoost machine learning model was evaluated on Prolonged ICU length of stay (>3 days) (AUC 0.6452, 95% CI 0.6311-0.6602). A CatBoost machine learning model using eight bedside features collected within 24 hours of ICU admission predicted prolonged postoperative ICU length of stay (>3 days) with an AUC of 0.6452 in external validation.
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