Risk Factors and Predictive Modeling of Occult Choledocholithiasis in Patients with Cholecystolithiasis

Area of Science:

• Gastroenterology and Hepatology
• Medical Diagnostics
• Predictive Modeling

Background:

• Occult choledocholithiasis poses significant health risks if not diagnosed promptly.
• Timely diagnosis and treatment are crucial for managing this condition.
• The development of effective diagnostic tools is essential.

Purpose of the Study:

• To construct a predictive model for the diagnosis of gallstones combined with occult choledocholithiasis.
• To identify independent risk factors associated with this condition.
• To provide a tool for assisting clinicians in diagnosis.

Main Methods:

• Analysis of 988 case datasets.
• Application of chi-square tests and multivariate logistic regression.
• Construction of a predictive model based on identified risk factors.

Main Results:

• Identified age, ALT, GGT, DBIL, gallstone location, and common bile duct dilation as independent risk factors.
• Developed a predictive model with high sensitivity (0.839), specificity (0.891), and accuracy (0.885).
• Achieved an Area Under the Curve (AUC) of 0.940, indicating strong predictive performance.

Conclusions:

• Age, liver enzymes (ALT, GGT), direct bilirubin, gallstone location, and bile duct dilation are key indicators.
• The developed prediction model is a valuable tool for diagnosing occult choledocholithiasis.
• Early and accurate diagnosis can lead to better patient outcomes.