The pre-operative predictive model for difficult elective laparoscopic cholecystectomy: A modification

Area of Science:

• Hepatobiliary Surgery
• Surgical Oncology
• Minimally Invasive Surgery

Background:

• Laparoscopic cholecystectomy (LC) is a common procedure, but challenging cases and conversions remain significant issues.
• Pre-operative prediction of surgical difficulty is essential for patient management and resource allocation.
• Existing scoring systems aim to predict LC difficulty, but their validity may vary across different clinical settings.

Purpose of the Study:

• To identify significant factors associated with very-difficult and converted laparoscopic cholecystectomy cases.
• To evaluate the predictive accuracy of Randhawa's scoring model for LC difficulty in a specific patient cohort.
• To assess the impact of modifying Randhawa's model on its predictive performance.

Main Methods:

• Prospective study of 152 patients undergoing LC between March 2018 and October 2019.
• Intra-operative grading scale used to categorize surgical difficulty into three levels.
• Multivariate analysis to identify predictors of very-difficult and converted cases; Randhawa's model performance assessed using ROC curves.

Main Results:

• 59.2% of cases were difficult, and 15.1% were very-difficult; 16 cases required conversion to open surgery.
• Significant factors for very-difficult LC included cholecystitis, ERCP, thickened gallbladder wall, and contracted gallbladder.
• A modified Randhawa's model demonstrated improved prediction accuracy (AUROC 0.82 vs. 0.75) compared to the original model.

Conclusions:

• Randhawa's model is a feasible tool for pre-operative planning in laparoscopic cholecystectomy.
• Modification of Randhawa's model enhances its predictive capability for identifying difficult LC cases.
• Accurate pre-operative prediction of LC difficulty can optimize surgical planning and patient outcomes.