ArticleMedicine2026
Development of a prediction model for the risk of prolonged ICU length of stay in patients undergoing coronary artery bypass surgery: A retrospective analysis based on the MIMIC-IV database.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Development and external validation of an interpretable machine learning model for predicting prolonged postoperative ICU length of stay in coronary artery bypass grafting patients using MIMIC-IV 3.1 and eICU-CRD 2.0.BMC medical informatics and decision making · 2026Article
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Authors and funding
4 authors.
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Abstract
The duration of hospitalization, particularly in the intensive care unit (ICU), for patients undergoing coronary artery bypass grafting (CABG), is influenced by both patient prognosis and treatment-related costs. Reducing the ICU length of stay (LOS) in CABG patients is critical for improving healthcare resource utilization. This study aimed to develop a model for identifying risk factors associated with prolonged ICU LOS in CABG patients, thus providing a foundation for clinical treatment, healthcare safety, and quality management research. In this single-center, retrospective cohort study, data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed. Clinical data from 6152 CABG patients who required treatment in ICU were included. The dataset was split into a training set and an internal validation set. Predictor variables were identified using least absolute shrinkage and selection operator regression. A nomogram prediction model was developed based on the identified predictors. The predictors incorporated into the model included the Charlson comorbidity index, acute physiology score III, atrial fibrillation, heart failure (HF), body mass index, acute kidney injury stage, type 1 diabetes mellitus and glucose levels. The prediction model demonstrated robust performance, achieving an area under the curve of 0.789 in the training cohort and 0.790 in the validation cohort. The Hosmer-Lemeshow (H-L) test and calibration plots validated the model's accuracy in both cohorts. The nomogram prediction model proposed in this study provides significant clinical utility for predicting prolonged ICU LOS in patients undergoing CABG. This model can assist clinicians in identifying CABG patients at risk for prolonged ICU LOS, thus enabling timely interventions and improving prognostic outcomes.
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