Predicting Difficult Laparoscopic Cholecystectomy Based on Clinicoradiological Assessment
DOI:
https://doi.org/10.3329/iahsmj.v4i2.62533Keywords:
BMI; Callots triangle; Cholecystitis; Conversion; Difficult cholecystectomy; Pericholecystic adhesion.Abstract
Background: Laparoscopic Cholecystectomy (LC) has become the treatment of choice for symptomatic gallstone disease. But it becomes difficult to perform safely and some cases require conversion to Open Cholecystectomy (OC). There is no clear consensus among the laparoscopic surgeons to determine preoperative parameters that can predict difficult laparoscopic cholecystectomy. The aim of this study is to predict the difficult laparoscopic cholecystectomies by correlating with preoperative clinical and radiological findings.
Materials and methods: This prospective observational study was performed in the Department of Surgery at Chittagong Medical College Hospital for a period of one year from April 2018 to March 2019. The sample size was 151. Pre-operative clinical and ultrasonographic criterias were correlated with intraoperative difficulties encountered. Peroperative difficulties were considered in terms of pericholecystic adhesion, difficult callots triangle dissection, difficult GB bed dissection and unusual bleeding during surgery.
Results: Out of 151 patients underwent LC in this study; 93 (61.6%) cases the procedure was uneventful and the other 58 (38.4%) procedures were difficult. Among those difficult 58 cases, 13 (8.6%) patients required conversion to open cholecystectomy. Difficult LC were found in BMI >30kg/m2, hospitalization for 3 or more times due to acute painful attack and GB wall thickness >3 mm.
Conclusion: Pre-operative prediction of difficult LC can be determined by correlating with clinical and radiological findings that help the surgeons to better prepare for intra-operative difficulties and risk of conversion to open cholecystectomy.
IAHS Medical Journal Vol 4(2), December 2021; 70-73
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Copyright (c) 2022 Mohammad Moinul Hasan, Syed Md Muhsin, Mohammad Ershad Alam, Shahed Mohammed Anwar, Faisal Mostafa, Mohammad Nurul Momin, Matiar Rahaman Khan
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.