关键词: 胆囊切除术, 腹腔镜；剖腹术；Meta分析；Logistic模型；风险预测

Abstract: Objective　Meta-analysis was used to construct a risk model for predicting conversion to open surgery in laparoscopic cholecystectomy（LC）．Methods　The Cochrane Library，PubMed，EMBase，Wanfang Data Knowledge Service Platform，VIP Network and CNKI databases were searched and both nationally and internationally published articles involving conversion to open surgery in LC up to May 2017 were retrieved.The overall risk of conversion to open surgery in LC was calculated by Meta-analysis.The corresponding risk prediction model was established based on natural log（ln） transformation of the overall risk.Results　A total of 25 articles and 34 112 patients were included.Meta-analysis showed that the following variables were significantly different between the conversion group and non-conversion group：sex（male），age（advanced age），history of upper abdominal surgery，time of onset of cholecystitis greater than 72 h，history of cholecystitis，diabetes complications，body mass index（BMI）≥25 kg/m2，gallbladder wall thickening，cholecystolithiasis incarceration，high white blood cell count before surgery，and high level of total bilirubin before surgery，these were the risk factors for conversion to open surgery in LC（OR>1.00，P<0.05）．These 11 risk factors were included in the Logistic prediction model which was expressed as Logit（P）=α+0.49X1+0.80X2+1.31X3+1.42X4+1.41X5+0.75X6+0.46X7+1.52X8+0.87X9+1.16X10+1.04X11.X1，X2，…，X11 expressed as　sex（male），age（advanced age），history of upper abdominal surgery，time of onset of cholecystitis greater than 72 h，history of cholecystitis，diabetes complications，BMI≥25 kg/m2，gallbladder wall thickening，cholecystolithiasis incarceration，high white blood cell count before surgery，and high level of total bilirubin before surgery.Conclusion　Based on Meta-analysis，a Logistic risk prediction model for conversion to open surgery in LC was established.This model can be used to predict the risk of conversion to open surgery in LC.

Key words: Cholecystectomy, laparoscopic；Laparotomy；Meta-analysis；Logistic models；Risk prediction