【摘要】 目的 基于倾向性评分匹配（PSM）方法构建脓毒性休克预测模型并进行效能验证。方法 选取114例脓毒症患者作为研究对象，根据是否并发脓毒性休克分为脓毒性休克组40例和脓毒症组74例。对研究对象进行PSM（脓毒性休克：脓毒症=1∶2），匹配后，脓毒性休克组、脓毒症组分别纳入30、60例。比较2组入院时的C反应蛋白（CRP）、降钙素原（PCT）、白细胞介素-6(IL-6)、血清淀粉样蛋白A(SAA)、可溶性内皮细胞蛋白C受体（sEPCR）、内皮细胞特异性分子1(ESM-1)、簇集蛋白（CLU）和急性生理学与慢性健康状况评分系统Ⅱ（APACHEⅡ）评分、脓毒症相关序贯器官衰竭（SOFA）评分，采用Cox比例风险回归模型分析脓毒性休克的影响因素，构建脓毒性休克预测模型，并通过受试者工作特征（ROC）曲线进行内部验证。绘制Kaplan-Meier生存曲线，分析各指标不同表达水平患者的生存预后差异。结果 匹配后，2组一般资料比较，差异无统计学意义（P> 0.05）；脓毒性休克组入院时血清PCT、CRP、SAA、IL-6、sEPCR、ESM-1水平及APACHEⅡ评分、SOFA评分均高于脓毒症组，血清CLU水平低于脓毒症组，差异有统计学意义（P<0.05）。Cox回归分析结果显示，PCT、CRP、SAA、IL-6、sEPCR、ESM-1、APACHEⅡ评分及SOFA评分均为脓毒血症休克的独立危险因素（P<0.05）, CLU则为独立保护因素（P<0.05）；基于这些因素构建脓毒性休克预测模型，内部验证结果显示其准确度为94.44%，曲线下面积为0.950，敏感度为93.33%，特异度为96.67%。死亡患者入院时PCT、CRP、SAA、IL-6、sEPCR、ESM-1水平及APACHEⅡ评分、SOFA评分均高于存活患者，CLU水平低于存活患者，差异有统计学意义（P<0.05）；与低表达水平或低分值患者相比，PCT、CRP、SAA、IL-6、sEPCR、ESM-1高表达水平患者和APACHEⅡ评分、SOFA评分高分值患者的病死率更高，CLU高表达水平患者的病死率更低，差异有统计学意义（P<0.05）。结论 脓毒症患者的血清生物标志物PCT、CRP、SAA、IL-6、sEPCR、ESM-1、CLU水平和APACHEⅡ评分、SOFA评分均与脓毒性休克的发生及生存预后密切相关，联合这些指标构建的预测模型能够准确预测脓毒性休克的发生。更多还原

【Abstract】 Objective To construct a predictive model for septic shock based on the propensity score matching(PSM) method and validate its effectiveness.Methods A total of 114 patients with sepsis were enrolled as study objects, and were divided into septic shock group(40 patients) and sepsis group(74 patients) according to whether they developed septic shock. PSM was performed with a ratio of septic shock to sepsis of 1∶ 2, resulting in the inclusion of 30 patients in the septic shock group and 60 patients in the sepsis group after matching. The levels of C-reactive protein(CRP), procalcitonin(PCT), interleukin-6(IL-6), serum amyloid A(SAA), soluble endothelial protein C receptor(sEPCR), endothelial cell-specific molecule 1(ESM-1), clusterin(CLU), and the Acute Physiology and Chronic Health Evaluation Ⅱ （APACHE Ⅱ） score and Sequential Organ Failure Assessment(SOFA) score at admission were compared between the two groups. Cox proportional hazards regression analysis was used to identify the factors influencing septic shock, and a predictive model for septic shock was constructed and internally validated using the receiver operating characteristic(ROC) curve. Kaplan-Meier survival curves were plotted to analyze the differences in survival prognosis among patients with different expression levels of the indicators.Results After matching,there were no statistically significant differences in general information between the two groups(P>0. 05). At admission, the septic shock group had higher levels of serum PCT, CRP, SAA, IL-6,sEPCR, ESM-1, and higher APACHE Ⅱ and SOFA scores, as well as a lower level of serum CLU compared with the sepsis group(P< 0. 05). Cox regression analysis showed that PCT, CRP, SAA,IL-6, sEPCR, ESM-1, APACHE Ⅱ score, and SOFA score were independent risk factors for septic shock(P< 0. 05), while CLU was an independent protective factor(P< 0. 05). The predictive model for septic shock, constructed based on these factors, showed an internal validation accuracy of94. 44%, an area under the curve of 0. 950, a sensitivity of 93. 33%, and a specificity of 96. 67%.Dead patients had higher levels of PCT, CRP, SAA, IL-6, sEPCR, ESM-1, and higher APACHE Ⅱ and SOFA scores, as well as a lower level of CLU at admission compared with survivors(P< 0. 05). Compared with patients with low expression levels or low scores, patients with high expression levels of PCT, CRP, SAA, IL-6, sEPCR, ESM-1, and high APACHE Ⅱ and SOFA scores had higher fatality rates, while patients with high CLU expression levels had a lower fatality rate(P< 0. 05).Conclusion The serum biomarkers including PCT, CRP, SAA, IL-6, sEPCR,ESM-1, CLU, and the APACHE Ⅱ and SOFA scores in sepsis patients are closely related to the occurrence of septic shock and survival prognosis. The predictive model constructed by combining these indicators can accurately predict the occurrence of septic shock.更多还原