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Updated: Jul 26, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A Nomogram Model for Prediction of Mortality Risk of Patients with Dangerous Upper Gastrointestinal Bleeding: A
Zhou Liu1, Liang Zhang2, Guang Li1
1Department of Intensive Care Unit, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
Objective:
This 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.
Methods:
From 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.
Results:
Logistic 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).
Conclusion:
The developed nomogram is an effective tool for risk stratification, early identification and intervention for DUGIB patients.
