A New Nomogram for Predicting 30-Day In-Hospital Mortality Rate of Acute Cholangitis Patients in the Intensive Care...

Area of Science:

• Medical Informatics
• Critical Care Medicine
• Predictive Analytics

Background:

• Acute cholangitis (AC) is a common, severe condition with high mortality, often leading to septic shock.
• Current prediction models for short-term mortality in AC patients are suboptimal.
• There is a need for improved tools to forecast mortality risk in AC.

Purpose of the Study:

• To develop and validate a novel predictive model for 30-day mortality in acute cholangitis patients.
• To identify independent risk factors associated with short-term mortality in AC.
• To compare the performance of the new model against established scoring systems.

Main Methods:

• Utilized data from the MIMIC-IV v2.0 database, including 506 AC patients.
• Employed multivariate logistic regression to construct a nomogram for 30-day mortality prediction.
• Evaluated model performance using AUC, calibration curves, NRI, IDI, and DCA.

Main Results:

• 14.0% of patients (71/506) died within 30 days.
• Identified GCS, SpO2, albumin, AST/ALT ratio, glucose, potassium, PTT, and peripheral vascular disease as independent risk factors.
• The developed nomogram demonstrated superior predictive performance and clinical utility compared to SOFA, OASIS, and SAPS II.

Conclusions:

• The novel prognostic nomogram is effective for predicting 30-day mortality in acute cholangitis patients.
• This model offers a valuable tool for risk stratification and clinical decision-making in AC management.
• The study highlights key factors influencing short-term outcomes in AC.