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Research Paper | Surgery | India | Volume 11 Issue 1, January 2022
Clinical Predictors for Difficult Single Incision Laparoscopic Cholecystectomy
Amita Chaudhary | Rohit Chauhan | R S Jhobta
Abstract: Background: Single-incision laparoscopic cholecystectomy (SILC) has been developed to further minimize the invasiveness of laparoscopic cholecystectomy by reducing the number of incisions. Knowledge of preoperative clinical factors to predict difficult SILC may be used for the preoperative counselling of the patients regarding the successful outcome of the surgery as well as to predict the risk of conversion preoperatively for selected patients, prepare the patient psychologically and minimize the procedure related cost. Objective: The present study was conducted to know about clinical predictors for difficult SIC. Methods: The study was a hospital based prospective observational study where 50 patients undergoing elective laparoscopic cholecystectomy due to gall stone diseases were included in the study. Multiple logistic regression analysis was done to predict difficult single incision laparoscopic cholecystectomy. Results: Out of 50 patients 15 patients underwent difficult SILC, while 35 patients had undergone easy single incision laparoscopic cholecystectomy independent risk factors in multivariate logistic regression analysis for difficult SILC were: age >50 years, BMI ?25 kg and multiple attacks of acute cholecystitis in past. Conclusion: Clinical Factors associated with difficult SILC age >50 years, BMI ?25 kg and multiple attacks of acute cholecystitis in past.
Keywords: single incision laparoscopic cholecystectomy, gall stone diseases, risk factors, multiple attacks of acute cholecystitis in past
Edition: Volume 11 Issue 1, January 2022,
Pages: 78 - 81
How to Cite this Article?
Amita Chaudhary, Rohit Chauhan, R S Jhobta, "Clinical Predictors for Difficult Single Incision Laparoscopic Cholecystectomy", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=SR211229193348, Volume 11 Issue 1, January 2022, 78 - 81, #ijsrnet
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