Correlation of Preoperative Scoring System with Intraoperative Scoring System for Predicting Difficult Laparoscopic Cholecystectomy: A Research Protocol

Introduction: Laparoscopic Cholecystectomy (LC) is the gold standard of care for benign gallbladder disease. The incidence of conversion from LC to open cholecystectomy is 14.3%. A high degree of anatomical variation exists in the cystic duct, cystic artery and gallbladder, making this surgery challenging at times, even in the hands of the most experienced surgeons. Need of the study: Understanding the correlation between the preoperative score and the intraoperative score early in the disease course can facilitate timely conversion to open surgery, thereby reducing the risk of inadvertent injury. Additionally, this approach can improve the efficiency of operating room scheduling and ensure that a more skilled laparoscopic surgeon is available. Furthermore, it can assist surgeons in managing complications or legal issues that may arise postoperatively. Aim: This study aims to assess the correlation between preoperative grading and an intraoperative scoring system to forecast difficulties in LC and the potential for conversion to open cholecystectomy. Materials and Methods: A prospective observational study will be conducted in the inpatient department of General Surgery at Acharya Vinoba Bhave Rural Hospital (AVBRH) in Sawangi, Wardha, Maharashtra, India, from March 2024 to February 2026. A total of 35 patients with cholelithiasis will be included in the study. Patients will be scored based on a preoperative scale (the Modified Randhawa scoring system) and counselled regarding the difficulty and possibility of conversion, which will then be correlated with an intraoperative score to assess their predictive value for the risk of conversion. The correlation between preoperative and intraoperative scores will be assessed using Pearson or Spearman correlation coefficients. For intergroup comparisons, the t-test or Mann-Whitney U test will be applied with a 5% significance level. Logistic regression models will be evaluated using the Hosmer-Lemeshow test and ROC curve analysis.