Browsing by Author "Stanisic, Veselin (35184186800)"

Introduction: Difficult laparoscopic cholecystectomy (DLC) is a stressful condition for surgeon which is followed by greater risk for various injuries (biliary, vascular etc.) Preoperative factors that are related to DLC are landmarks for surgeon to assess the possibilities for overcoming difficulties and making early decision about conversion to an open surgery. In prospective cohort study we evaluated and defined the importance and impact of preoperative parameters on difficulties encountered during surgery, defined DLC, predictors of DLC and index of DLC. Materials and methods: All patients in the study were operated by the same surgeon. We defined the total duration of the operation as the time from insertion of Veress needle to the extraction of gallbladder (GB) and DLC as a laparoscopic cholecystectomy (LC) that lasted longer than the average duration of LC and the value of one standard deviation. Results: Multivariate logistic regression analysis identified five predictors significantly related to DLC: GB wall thickness > 4 mm, GB fibrosis, leukocytosis ˃10 × 109 g/L, ˃ 5 pain attacks that lasted longer than 4 h and diabetes mellitus. The sensitivity of the generated index of DLC in our series is 81.8% and specificity 97.2%. Conclusion: Preoperative prediction of DLC is important for the surgeon, for his operating strategy, better organization of work in operating room, reduction of treatment expenses, as well as for the patient, for his timely information, giving a consent for an operation and a better psychological preparation for possible open cholecystectomy (OC). © 2020 The Authors

Background/Aims: Predicting technical difficulties in laparoscopic cholecystectomy (LC) in a small regional hospital increases efficacy, cost-benefit and safety of the procedure. The aim of the study was to assess whether it is possible to accurately predict a difficult LC (DLC) in a small regional hospital based only on the routine available clinical work-up parameters (patient history, ultrasound examination and blood chemistry) and their combinations. Methodology: A prospective, cohort, of 369 consecutive patients operated by the same surgeon was analyzed. Conversion rate was 10 (2.7%). DLC was registered in 55 (14.90%). Various data mining techniques were applied and assessed. Results: Seven significant predictors of DLC were identified: i) shrunken (fibrotic) gallbladder (GB); ii) ultrasound (US) GB wall thickness >4 mm; iii) >5 attacks of pain lasting >5 hours; iv) WBC >10×109 g/L; v) pericholecystic fluid; vi) urine amylase >380 IU/L, and vii) BMI >30kg/m2. Bayesian network was selected as the best classifier with accuracy of 94.57, specificity 0.98, sensitivity 0.77, AUC 0.96 and F-measure 0.81. Conclusion: It is possible to predict a DLC with high accuracy using data mining techniques, based on routine preoperative clinical parameters and their combinations. Use of sophisticated diagnostic equipment is not necessary. © H.G.E. Update Medical Publishing S.A.