UNI-MB - logo
UMNIK - logo
 
E-resources
Peer reviewed Open access
  • Preoperative predictors of ...
    Panni, Roheena Z.; Strasberg, Steven M.

    Journal of hepato-biliary-pancreatic sciences, January 2018, Volume: 25, Issue: 1
    Journal Article

    Background Observational studies have identified risk factors for conversion from laparoscopic to open cholecystectomy in acute cholecystitis. The aim of this study is to evaluate the reliability of these predictors and to identify sources of heterogeneity in the studies. Methods OVID was searched for papers published from 1995 to 2016. Studies with more than 100 patients were included. Risk factors for conversion were ed and categorized by statistical significance. Results Eleven studies were evaluated. Inflammation with difficulty in anatomic identification was the most common reason of conversion. Because of heterogeneity among studies a quantitative approach was not possible. Therefore, qualitative analysis using a heat map was performed along with investigation into sources of heterogeneity with the aim of creating a framework for future quantitative studies. Age, maleness, and white blood cell count were most commonly identified predictors of conversion. Sources of heterogeneity were criteria for diagnosis of acute cholecystitis, selection of patients for laparoscopic cholecystectomy, selection of variables and variations in their thresholds. Conclusions In acute cholecystitis, inflammation is the most common reason for conversion. Age, maleness and white blood cell count are common predictors of conversion. Large scale prospective studies with minimal heterogeneity are needed to establish validity of these and other predictors. Highlight Risk factors for conversion to open cholecystectomy in acute cholecystitis were evaluated in published observational studies. Heterogeneity in criteria for diagnosis, selection of patients, selection of variables, and variations in thresholds was observed. This limited conclusions. Large scale prospective studies with limited and measurable heterogeneity are needed for predictive models.