A Nomogram-Based Model for Predicting the Risk of Severe Acute Cholangitis Occurrence

Area of Science:

• Hepatology
• Infectious Diseases
• Clinical Prediction Models

Background:

• Acute cholangitis is a serious biliary infection with potential complications.
• Current risk assessment methods for acute cholangitis have limitations in predicting severity.
• There is a need for improved risk prediction models for acute cholangitis.

Purpose of the Study:

• To develop and validate a nomogram-based model for predicting severe acute cholangitis.
• To integrate multiple clinical and laboratory variables for enhanced risk prediction.
• To provide a tool for early identification of high-risk patients.

Main Methods:

• Retrospective data collection from 152 acute cholangitis patients (Jan 2019-Mar 2022).
• Utilized univariate and multivariate analyses to identify independent risk factors for severe disease.
• Constructed a nomogram integrating identified risk factors and validated using ROC curves, calibration curves, and DCA.

Main Results:

• Aspartate to Alanine Transaminase ratio (TR), Neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), and D-dimer (DD) were identified as independent risk factors.
• The developed nomogram demonstrated good differentiation and calibration.
• Decision Curve Analysis showed significant clinical utility for risk stratification.

Conclusions:

• The nomogram model serves as an effective tool for predicting severe acute cholangitis risk.
• It can assist clinicians in making informed intervention and treatment decisions.
• Early identification of high-risk patients can lead to timely management.