Selective Cholecystectomy: using an evidence-based prediction model to plan for cholecystectomy

Area of Science:

• Gastroenterology
• Surgical Innovation
• Medical Informatics

Background:

• Symptomatic gall stones necessitate surgical intervention, with laparoscopic cholecystectomy being the standard.
• Conversion from laparoscopic to open cholecystectomy can lead to poorer patient outcomes.
• Predicting conversion accurately is vital for improving patient care and surgical safety.

Purpose of the Study:

• To review existing literature on conversion during laparoscopic cholecystectomy.
• To identify validated prediction models for surgical conversion.
• To assess the utility of prediction tools in clinical practice.

Main Methods:

• Systematic review of recent literature on laparoscopic cholecystectomy conversion.
• Identification of prediction models with internal and external validation.
• Evaluation of the Cairns Prediction Model's applicability and effectiveness.

Main Results:

• Two robust prediction models for laparoscopic cholecystectomy conversion were identified.
• The Cairns Prediction Model, utilizing nomograms, was found to be a validated and easily applicable tool.
• The model is currently in use and demonstrates predictive accuracy.

Conclusions:

• The Cairns Prediction Model aids in predicting conversion from laparoscopic to open cholecystectomy.
• Routine implementation of this model can decrease conversion rates.
• Improved prediction enhances the informed consent process and overall patient management for gall stone disease.