ArticleJournal of cardiovascular translational research2025
Machine Learning Model for Risk Prediction of Prolonged Intensive Care Unit in Patients Receiving Intra-aortic Balloon Pump Therapy during Coronary Artery Bypass Graft Surgery.
Article in Journal of cardiovascular translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.Journal of cellular and molecular medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to construct machine learning models and predict prolonged intensive care units (ICU) stay in patients receiving perioperative intra-aortic balloon pump (IABP) therapy during cardiac surgery. 236 patients were divided into the normal (≤ 14 days) and prolonged (> 14 days) ICU groups based on the 75th percentile of ICU duration across the entire cohort. Seven machine learning models were trained and validated. The Shapley Additive explanations (SHAP) method was employed to illustrate the effects of the features. 94 patients (39.83%) experienced prolonged ICU stay. The XGBoost model outperformed other models in predictive performance, as evidenced by its highest area under the receiver operating characteristic curve (training: 0.92; validation: 0.73). The SHAP analysis identified tracheotomy, albumin, Sv1, and cardiac troponin T as the top four risk variables. The XGBoost model predicted risk variables for prolonged ICU stay in patients, possibly contributing to improving perioperative management and reducing ICU duration.
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Registered trials
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