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Updated: Jan 20, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Desarrollo y validación de un modelo de predicción de riesgo de hemorragia gastrointestinal de nueva aparición en
1Department of Minimally Invasive Intervention, Ganzhou People's Hospital No. 16 Hongqi Avenue, Zhanggong District, Ganzhou 341000, Jiangxi, China.
Objectives:
To explore risk factors for new-onset gastrointestinal bleeding (GIB) in critically ill patients and construct a predictive model.
Methods:
A retrospective study of 241 intensive care unit patients was conducted. Clinical data, laboratory indicators, treatments, and outcomes were collected. Risk factors were analyzed via univariate and multivariate logistic regression. A nomogram was established, and its performance was assessed using receiver operating characteristic curves, concordance index (C-index), calibration plots, Hosmer-Lemeshow test, bootstrap resampling, and decision curve analysis (DCA). Internal and external validation were performed.
Results:
Age ≥ 65, shock, sepsis, renal dysfunction, hepatic failure, mechanical ventilation > 48 h, and hemoglobin < 8 g/dL were independent risk factors for new-onset GIB, while albumin < 30 g/L emerged as a predictive factor. The nomogram demonstrated strong discrimination (C-index 0.825 in the training cohort, 0.804 in the validation cohort), good calibration, and favorable clinical utility. DCA confirmed its benefit in guiding clinical decisions.
Conclusion:
Multiple comorbidities and treatment-related factors contribute to new-onset GIB in critically ill patients. The developed nomogram provides an effective tool for individualized risk assessment, supporting early intervention and improved outcomes.
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