Derivation and validation of a predictive model for subtotal cholecystectomy

Area of Science:

• Gastroenterology
• Surgical Innovation
• Patient Safety

Background:

• Subtotal cholecystectomy (STC) rates are rising as a strategy to prevent bile duct injury during laparoscopic cholecystectomy (LC).
• However, STC is associated with significant post-operative morbidity.
• Identifying risk factors for STC is crucial for improving patient outcomes.

Purpose of the Study:

• To identify risk factors associated with subtotal cholecystectomy (STC).
• To derive and validate a predictive risk model for STC.
• To improve pre-operative decision-making and patient counseling regarding STC.

Main Methods:

• A retrospective analysis of 2768 patients undergoing LC for biliary pathology between 2015-2020.
• Backward stepwise multivariable regression was used to develop a predictive model for STC.
• Internal validation was performed using bootstrapping, and patients were categorized into risk groups.

Main Results:

• The study included 99 cases (3.6%) of STC, with post-operative bile leaks (29.3%), collections (19.2%), and retained stones (10.1%).
• Predictors of STC included age >60, male sex, diabetes, acute cholecystitis, high CRP, multiple biliary admissions, pre-operative ERCP/stent, pre-operative cholecystostomy, and emergency LC (AUC=0.84).
• STC rates were 0.8% (low-risk), 3.9% (medium-risk), and 24.5% (high-risk).

Conclusions:

• Subtotal cholecystectomy is associated with considerable morbidity.
• A validated risk model incorporating patient and disease factors can identify patients at high risk for STC.
• This model aids surgeons in pre-operative decision-making and patient counseling.