ArticleJournal of general internal medicine2025
Construction and Validation of a Risk Prediction Model for Acute Gastrointestinal Injury in Non-ICU Elderly Critically Ill Patients.
Article in Journal of general internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Gastrointestinal dysfunction score for mortality prediction in intensive care unit patients with pre-existing digestive system disease: a prospective observational study.Frontiers in nutrition · 2026Article
- Early Dietary Fiber Intake in ICU Patients and Its Associations with Acute Gastrointestinal Injury: A Prospective Observational Study.Journal of multidisciplinary healthcare · 2026Article
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8 authors.
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Abstract
backgroundAcute gastrointestinal injury (AGI) has a relatively high prevalence among elderly critically ill patients in non-intensive care units (non-ICUs), and significantly influences their clinical outcomes. Therefore, it is important to identify people at risk for AGI and take preventive measures as early as possible.
objectiveWe aimed to construct and validate a risk prediction model for AGI in non-ICU elderly critically ill patients.
designCase-control study.
participantsIn total, 538 elderly critically ill patients admitted to the general medical department of a tertiary hospital in Shanxi from April 2021 to May 2024. MAIN MEASURES: Influential factors for AGI were determined using univariate and multifactorial logistic regression analyses. We constructed a risk prediction model and created a nomogram. The bootstrap resampling method was utilized for internal validation. A total of 151 patients from different time periods were selected for the external validation. KEY
resultsThe multifactorial logistic regression analysis revealed that the independent predictors for AGI were the duration of antibiotic use, number of vasoactive drugs, delayed enteral nutrition, age-corrected Charlson comorbidity index, and white blood cell count, all of which were included in the model and created a nomogram. The Omnibus test showed that the overall efficacy of the model was good (P < 0.001). The area under the receiver operating characteristic curve (AUC) was 0.807, the corrected AUC was 0.806, and the AUC was 0.796 for external validation, indicating good model discrimination. The calibration curves and Hosmer-Lemeshow tests revealed that the model was well calibrated (P = 0.627, Brier = 0.172 in internal validation; and P = 0.366, Brier = 0.182 in external validation). The clinical decision curves showed that the model had good clinical utility.
conclusionsAGI is common in non-ICU elderly critically ill patients. This AGI risk prediction model can be used as a screening tool to identify high-risk patients for AGI and assist clinical decision making.
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