Development of a Nomogram for Predicting Multiple Organ Dysfunction in Acute Pancreatitis.

Area of Science:

• Clinical Medicine
• Medical Informatics
• Predictive Analytics

Background:

• Multiple Organ Dysfunction Syndrome (MODS) is a critical, often fatal, complication of acute pancreatitis (AP).
• Early identification of high-risk AP patients is essential for timely intervention and improved survival rates.

Purpose of the Study:

• To develop and validate a clinical nomogram for predicting MODS risk in AP patients.
• The nomogram utilizes clinical and laboratory data available within 24 hours of admission.

Main Methods:

• A retrospective cohort study of 693 AP patients was conducted.
• LASSO regression identified key predictors from 29 candidate variables.
• Generalized Linear Model (GLM), Random Forest (RF), and Support Vector Machine (SVM) models were constructed and compared.

Main Results:

• The GLM-based nomogram demonstrated superior performance.
• A 7-variable model achieved an AUC of 0.829 (training) and 0.846 (validation).
• The model exhibited good discrimination, calibration, and a high negative predictive value.

Conclusions:

• An internally validated 7-variable nomogram using GLM effectively predicts MODS in AP patients.
• The nomogram supports early risk stratification and resource allocation.
• Further external validation and prospective studies are recommended.