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• W3046129086 abstract "Background: Laparoscopic cholecystectomy is the main treatment of acute cholecystitis. Although considered relatively safe, it carries 6%–9% risk of major complications and 0.1%–1% risk of mortality. There is no consensus regarding the evaluation of the preoperative risks, and the management of patients with acute cholecystitis is usually guided by surgeon's personal preferences. We assessed the best method to identify patients with acute cholecystitis who are at high risk of complications and mortality. Methods: We performed a systematic review of studies that reported the preoperative prediction of outcomes in people with acute cholecystitis. We searched the Cochrane Library, MEDLINE, EMBASE, WHO ICTRP, ClinicalTrials.gov, and Science Citation Index Expanded until April 27, 2019. We performed a meta-analysis when possible. Results: Six thousand eight hundred twenty-seven people were included in one or more analyses in 12 studies. Tokyo guidelines 2013 (TG13) predicted mortality (two studies; Grade 3 versus Grade 1: odds ratio [OR] 5.08, 95% confidence interval [CI] 2.79–9.26). Gender predicted conversion to open cholecystectomy (two studies; OR 1.59, 95% CI 1.06–2.39). None of the factors reported in at least two studies had significant predictive ability of major or minor complications. Conclusion: There is significant uncertainty in the ability of prognostic factors and risk prediction models in predicting outcomes in people with acute calculous cholecystitis. Based on studies of high risk of bias, TG13 Grade 3 severity may be associated with greater mortality than Grade 1. Early referral of such patients to high-volume specialist centers should be considered. Further well-designed prospective studies are necessary." @default.
• W3046129086 created "2020-08-03" @default.
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• W3046129086 date "2021-01-01" @default.
• W3046129086 modified "2023-10-18" @default.
• W3046129086 title "Risk Prediction in Acute Calculous Cholecystitis: A Systematic Review and Meta-Analysis of Prognostic Factors and Predictive Models" @default.
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• W3046129086 doi "https://doi.org/10.1089/lap.2020.0151" @default.
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