脓毒性休克患者发生顽固性休克的危险因素分析

发布时间：2018-01-01 09:22

  本文关键词：脓毒性休克患者发生顽固性休克的危险因素分析　出处：《广西医科大学》2014年硕士论文　论文类型：学位论文

  更多相关文章： 脓毒性休克 顽固性休克 危险因素 多因素分析

【摘要】：目的：探讨脓毒性休克患者发生顽固性休克的危险因素。 方法：同时以“脓毒症、休克”作为诊断检索广西医科大学第一附属医院病例库中2011年6月至2013年12月ICU病房(EICU、内科ICU、外科ICU)收治的病例,依据“脓毒性休克”诊断标准进行初步筛选后再按照纳入及排除标准选择符合条件的脓毒性休克患者,根据患者入住ICU经充分液体复苏并应用血管活性药物或(和)正性肌力药物治疗6h后是否发生顽固性休克分为研究组(顽固性休克组)和对照组(可逆性休克组)。搜集并整理入选脓毒性休克患者包括一般资料、实验室检查结果及相关治疗情况在内的多项研究指标。先将各项指标进行单因素分析,再将有统计学差异的变量(P0.05为差异具有统计学意义)进行多因素Logistic回归分析,保留在Logistic回归模型中的因素确定为发生顽固性休克有意义的独立危险因素,并根据Logistic回归模型方程进一步分析评价这些危险因素对预测发生顽固性休克的诊断价值。 结果：单因素分析结果显示MAP、APACHE II评分、昏迷状态、高血压病、MODS、肌酐、BNP、动脉血乳酸、2h乳酸清除率、Pa02、血糖、治疗时限这12个研究指标的组间比较具有统计学意义。将单因素分析中有统计学意义的研究指标纳入多因素非条件Logistic回归模型中进行分析,结果显示保留在Logistic回归模型中的独立危险因素依次是2h乳酸清除率(OR=3.166E-5)、乳酸(OR=7.751)、APACHEⅡ评分(OR=1.521)、高血压病史(OR=1.302)、肌酐(OR=1.021)。根据Logistic回归模型方程计算出此概率模型判断脓毒性休克患者发生顽固性休克的准确度为94.62%(123/130)、敏感度为90.91%(40/44)、特异度为96.51%(83/86),说明利用上述危险因素预测脓毒性休克患者是否会进展为顽固性休克具有较好的诊断价值。 结论：2h乳酸清除率低、高乳酸值、APACHEⅡ评分、高血压病史、高肌酐值为脓毒性休克患者发生顽固性休克的独立危险因素,这些危险因素能较好地预测脓毒性休克患者是否有发生顽固性休克的可能。
[Abstract]:Objective: to investigate the risk factors of refractory shock in septic shock patients. Methods: at the same time, "sepsis, shock" was used as a diagnostic search for EICU, ICU in ICU ward of Guangxi Medical University from June 2011 to December 2013 in the first affiliated Hospital of Guangxi Medical University. Surgical ICU admitted cases, according to the "septic shock" diagnostic criteria for preliminary screening and then according to the inclusion and exclusion criteria for the selection of qualified septic shock patients. Patients with refractory shock were divided into study group (intractable shock group) and control group (n = 6) according to whether they were treated with vasoactive drugs or / and positive muscle strength drugs or not after 6 hours of treatment according to the patients admitted to ICU with adequate fluid resuscitation. Collecting and sorting out the general data of septic shock patients in the reversible shock group. The results of laboratory examination and related treatment, including a number of research indicators. First of all, the single factor analysis of the indicators. Then the variables with statistical difference (P0.05 for the difference is statistically significant) multivariate Logistic regression analysis. The factors retained in the Logistic regression model were identified as significant independent risk factors for refractory shock. The diagnostic value of these risk factors in predicting refractory shock was further analyzed and evaluated according to the Logistic regression model. Results: the results of univariate analysis showed that MAPP Apache II score, coma state, hypertension mods, creatinine BNPs, arterial blood lactate 2 h lactate clearance rate and Pa02. Blood sugar. The comparison of the 12 study indexes with time limit of treatment was statistically significant. The study indexes with statistical significance in univariate analysis were analyzed in multivariate unconditioned Logistic regression model. The results showed that the independent risk factors retained in the Logistic regression model were 2 h lactate clearance rate (ORL) 3.166E-5 and lactate ORO 7.751respectively. The score of APACHE 鈪，

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