摘要
目的探讨脓毒症患者预后多基因表达风险模型的构建并评估其预测效能。方法纳入2019年1月至2022年6月收治的脓毒症患者140例。根据患者住院后28 d死亡情况分为死亡组和存活组;对比两组患者临床资料、实验室指标;采用反转录-荧光定量PCR法测定所有患者外周血微小RNA103(miR103)、Toll样受体2(TLR2)、单个核细胞中信号转导子和转录激活子3(STAT3)、自噬相关蛋白Beclin1基因表达情况;使用多因素Cox回归模型确定脓毒症患者28 d死亡风险的相关因素,通过Ggrisk软件进行风险模型构建,绘制受试者工作特征(ROC)曲线评估模型效能。结果死亡组在序贯器官衰竭评分(SOFA评分)、年龄及急性生理功能和慢性健康状况Ⅱ评分(APACHEⅡ评分)方面与存活组比较差异有统计学意义(P<0.05)。死亡组患者miR103、TLR2、STAT3、Beclin1基因相对表达量与存活组比较差异有统计学意义(P<0.05)。多因素Cox回归分析显示,miR103、TLR2、STAT3、Beclin1基因为脓毒症患者28 d死亡的独立预测因子。ROC曲线分析显示,由上述基因构建的预测风险模型在早期预警28 d死亡方面具有良好的预测价值(AUC=0.964,P<0.001),敏感度为97.50%,特异度为91.00%。经过比较,由上述基因构建的预测风险模型预测效能显著高于APACHEⅡ评分及SOFA评分(均P<0.001)。结论miR103、TLR2、STAT3、Beclin1基因表达水平对脓毒症患者28 d死亡风险具有一定相关性,构建的多基因表达风险模型能够有效预测脓毒症28 d死亡风险。
Objective To explore the construction of a prognostic polygene expression risk model in patients with sepsis and to evaluate its predictive performance.Methods A total of 140 patients with sepsis from January 2019 to June 2022 were included.Patients were divided into the death group and survival group based on their 28-day mortality after hospitalization.Clinical data and laboratory indicators were compared between the two groups.Reverse transcription-quantitative polymerase chain reaction(RT-qPCR)was used to determine the gene expression levels of peripheral blood microRNA103(miR103),Toll-like Receptor 2(TLR2),Signal Transducer and Activator of Transcription 3(STAT3),and autophagy-related protein Beclin1 in all patients.Multivariate COX regression analysis was employed to identify relevant factors for the 28-day mortality risk in sepsis patients.The Ggrisk software was used for risk model construction,and the ROC curve was drawn to assess the model′s performance.Results The death group showed statistically significant differences in SOFA score,age,and APACHEⅡscore compared to the survival group(P<0.05).The relative expression levels of miR103,TLR2,STAT3,and Beclin1 genes in the death group were significantly different from those in the survival group(P<0.05).Multivariate COX regression analysis revealed that miR103,TLR2,STAT3,and Beclin1 genes were independent factors for the 28-day mortality risk in sepsis patients.ROC curve analysis demonstrated that the predictive risk model constructed from these genes had excellent predictive value for early warning of 28-day mortality(AUC=0.964,P=0.000),with a sensitivity of 97.50%and specificity of 91.00%.Comparative analysis showed that the predictive efficacy of the risk model constructed from these genes was significantly higher than that of the APACHEⅡscore and SOFA score(both P<0.001).Conclusion The expression levels of miR103,TLR2,STAT3,and Beclin1 genes are correlated with the 28-day mortality risk in sepsis patients.The constructed multigene expression risk model can effectively predict the 28-day mortality risk in sepsis,demonstrating superior performance compared to the APACHEⅡscore and SOFA score.
作者
王海曼
王亚苗
黎晓虹
WANG Hai-man;WANG Ya-miao;LI Xiao-hong(Trauma ICU,the First Affiliated Hospital of Hainan Medical College,Haikou 570102,Hainan,China)
出处
《广东医学》
CAS
2024年第4期397-402,共6页
Guangdong Medical Journal
基金
海南省卫生健康行业科研项目(18A200065)。
