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Updated: Jul 20, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A Nomogram-Based Model for Predicting the Risk of Severe Acute Cholangitis Occurrence
Jian Xu1, Zhi-Xiang Xu1, Jing Zhuang1
1Department of Gastroenterology, the Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu Province, 212000, People's Republic of China.
Background:
Acute cholangitis is a severe inflammatory disease associated with an infection of the biliary system, which can lead to complications and adverse outcomes. The existing nomogram-based risk assessment methods largely rely on a limited set of clinical features and laboratory indicators, and are mostly constructed using univariable models, which have limitations in predicting the severity. This study aims to develop a nomogram-based model that integrates multiple variables to improve risk prediction for acute cholangitis.
Methods:
Data were retrospectively collected from 152 patients with acute cholangitis who attended the People's Hospital of Jiangsu University between January 2019 and March 2022, and were graded as having mild to moderate versus severe cholangitis according to the 2018 Tokyo guidelines. Univariate and multivariate analyses were employed to discern independent risk factors associated with severe acute cholangitis, which were subsequently integrated into a nomogram model. The efficacy of the model was appraised by leveraging Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA).
Results:
Aspartate to alanine transaminase ratio (Transaminase ratio or TR), Neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), and D-dimer (DD) levels were independent risk factors for severe acute cholangitis. A nomogram model was constructed based on these 4 risk factors. ROC and calibration curves were well differentiated and calibrated. DCA had a high net gain in the range of 7% to 83%. The above model was tested internally. According to the nomogram model when patients using characteristic curve critical values were divided into a low-risk group and a high-risk group, the incidence in the high-risk group was significantly higher than in the low-risk group.
Conclusion:
This nomogram model may provide clinicians with an effective tool to predict the potential risk of severe acute cholangitis in patients and guide informed intervention measures and treatment decisions.
