ArticleCurrent medical science2023
A Nomogram Model for Prediction of Mortality Risk of Patients with Dangerous Upper Gastrointestinal Bleeding: A Two-center Retrospective Study.
Article in Current medical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Article
- Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit.Current medical science · 2025Observational
- The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding (DUGIB), and identify high-risk patients who require emergent therapy.
methodsFrom January 2020 to April 2022, the clinical data of 256 DUGIB patients who received treatments in the intensive care unit (ICU) were retrospectively collected from Renmin Hospital of Wuhan University (n=179) and the Eastern Campus of Renmin Hospital of Wuhan University (n=77). The 179 patients were treated as the training cohort, and 77 patients as the validation cohort. Logistic regression analysis was used to calculate the independent risk factors, and R packages were used to construct the nomogram model. The prediction accuracy and identification ability were evaluated by the receiver operating characteristic (ROC) curve, C index and calibration curve. The nomogram model was also simultaneously externally validated. Decision curve analysis (DCA) was then used to demonstrate the clinical value of the model.
resultsLogistic regression analysis showed that hematemesis, urea nitrogen level, emergency endoscopy, AIMS65, Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB. The ROC curve analysis indicated the area under curve (AUC) of the training cohort was 0.980 (95%CI: 0.962-0.997), while the AUC of the validation cohort was 0.790 (95%CI:0.685-0.895). The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts (P=0.778, P=0.516).
conclusionThe developed nomogram is an effective tool for risk stratification, early identification and intervention for DUGIB patients.
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