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

TL;DR

This study creates a predictive model to help identify patients with gallstones who may also have occult bile duct stones, using factors like age and blood test results.

Contribution

A novel predictive model for diagnosing occult choledocholithiasis in gallstone patients is developed with high accuracy and sensitivity.

Findings

Age, ALT, GGT, DBIL, gallstone location, and bile duct dilation are independent risk factors for occult choledocholithiasis.

The predictive model achieved a high AUC of 0.940, sensitivity of 0.839, and specificity of 0.891.

The model provides a valuable diagnostic tool for identifying occult bile duct stones in gallstone patients.

Abstract

Occult choledocholithiasis, if not diagnosed and treated in a timely manner, can have severe consequences. The purpose of this study is to construct a predictive model to assist in the diagnosis.

A total of 988 case datasets were included. Data were analyzed using chi‐square tests and multivariate logistic regression. Ultimately, a predictive model for gallstones combined with occult choledocholithiasis was constructed.

Multivariate logistic regression analysis revealed that age, alanine aminotransferase (ALT), gamma‐glutamyl transferase (GGT), direct bilirubin (DBIL), location of gallstones, and ultrasonographic indication of common bile duct dilation are independent risk factors for gallstones combined with occult choledocholithiasis. A predictive model was constructed based on these factors: logit(P) = −5.109 + 2.007x1 + 1.175x2 + 3.479x3 + 1.412x4 + 2.199x5 + 2.473x6 (where x1–x6 represent age, location of gallstones, ultrasonographic indication of common bile duct dilation, ALT, GGT, DBIL, respectively). The model demonstrated a sensitivity of 0.839, specificity of 0.891, accuracy of 0.885, 95% CI of 0.913–0.967, and an AUC of 0.940.

Age, ALT, GGT, DBIL, gallstone location, and sonographic common bile duct dilation constitute independent risk factors for gallstones with occult choledocholithiasis. The prediction model based on these indicators provides a valuable tool for the diagnosis of occult choledocholithiasis.

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