Indocyanine green fluorescent cholangiography in laparoscopic cholecystectomy: A systematic review and meta-analysis with trial sequential analysis of randomized controlled trials

Background: Indocyanine green fluorescent cholangiography is a noninvasive, real-time visualization method to prevent misinterpretation of biliary anatomy during laparoscopic cholecystectomy. This systematic review and meta-analysis focuses exclusively on randomized controlled trials that assess the efficacy of this technique during laparoscopic cholecystectomy. Methods: MEDLINE, Embase, and Cochrane databases were searched up to August 2024. The main endpoints of interest included bile duct injuries and identification success rates of biliary structures. A trial sequential analysis was performed to establish implications for further research. Results: Eight studies comprising 1,586 patients were included. The results showed no statistically significant differences in bile duct injuries (odds ratio, 0.73; 95% confidence interval, 0.05-10.80; I2 1/4 32%), identification success rates of the cystic duct (odds ratio, 1.73; 95% confidence interval, 0.78-3.86; I2 1/4 32%) or common hepatic duct (odds ratio, 2.80; 95% confidence interval, 0.96-8.15; I2 1/4 82%). However, a significant difference was observed in the success rate odds for identifying the common bile duct (odds ratio, 4.08; 95% confidence interval, 1.77-9.41; I2 1/4 64%). The trial sequential analysis provided certainty that the improved success rate for identifying the common bile duct is not a result of type 1 error, but further studies are necessary to assess the results of most outcomes. Conclusion: Indocyanine green fluorescent cholangiography significantly improved the odds of success in identifying the common bile duct during laparoscopic cholecystectomy. However, additional randomized controlled trials are needed to confirm its effects on identifying other biliary structures and affecting patient outcomes. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.