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肝硬化失代偿期患者早期死亡转归多因素分析 被引量:4

Multivariate Analysis of Early Death Outcomes in Patients with Decompensated Cirrhosis
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摘要 目的对胆红素水平及相关潜在影响因素与肝硬化失代偿期患者早期死亡转归之间关联进行分析。方法该研究方便选取2012年12月—2013年12月确诊的肝硬化失代偿期患者111例纳入研究,根据随访1年预后死亡情况检出死亡组患者31例;随访存活的存活对照组80例;对患者进行分析,讨论胆红素水平与肝硬化失代偿期患者早期死亡转归之间关联情况。结果回归分析结果提示TBIL增高是死亡预后发生的危险因素(OR=2.448,P<0.01);Cys C增高、年龄增高以及BMI增高也均可增加预后出现死亡的风险(OR=1.280、1.225、1.241,P<0.01)。结论TBIL增高是死亡预后发生的危险因素,此外Cys C增高、年龄增高以及BMI增高也均可增加预后出现死亡的风险。 Objective To analyze the association between the level of bilirubin,related potential factors and early mortality in patients with decompensated cirrhosis.Methods In this study,111 patients with decompensated cirrhosis who were diagnosed from December 2012 to December 2013 were convenient included in the study.31 patients with death were detected according to the short-term prognosis of death;80 survivors of survival were followed up;patients were analyzed to discuss bilirubin levels and liver.Association of early death outcomes in patients with decompensated decompensated patients.Use the SPSS software package to analyze the data.Results The regression analysis suggested that the increase of TBIL was a risk factor for the prognosis of death(OR=2.448,P<0.01).The increase of Cys C,increase of age,and increase of BMI also increased the risk of prognosis of death.(OR=1.280,1.225,1.241,P<0.01).Conclusion Increased TBIL is a risk factor for the prognosis of death.In addition,increased Cys C,increased age,and increased BMI all increase the risk of death from the prognosis.
作者 李风成 程君 谭林 高学武 王金玲 徐静 李莎莎 姜宁 LI Feng-cheng;CHENG Jun;TAN Lin;GAO Xue-wu;WANG Jin-lin;XU Jing;LI Sha-sha;JIANG Ning(Second Ward,Department of Hepatology,the Second People's Hospital of Fuyang,Fuyang,Anhui Province,236015 China;Department of Infectious Diseases,the First Affiliated Hospital of Anhui Medical University,Hefei,Anhui Province,230022 China)
出处 《中外医疗》 2020年第9期14-16,共3页 China & Foreign Medical Treatment
关键词 胆红素 肝硬化失代偿期 早期死亡转归 非条件Logistic回归模型 Bilirubin Decompensated cirrhosis Early death outcome Non-conditional Logistic regression model
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