检验医学与临床2025，Vol.22Issue(8)：1138-1142,1147,6.DOI:10.3969/j.issn.1672-9455.2025.08.025
重症肺炎患者发生感染性休克的预测模型构建
Construction of a predictive model for septic shock in patients with severe pneumonia
摘要
Abstract
Objective To establish a predictive model for septic shock in patients with severe pneumonia.Methods A total of 200 patients with severe pneumonia admitted to the Affiliated Hospital of Hebei Univer-sity of Engineering from April 2021 to July 2023 were selected as the research objects.According to whether the patients developed septic shock,they were divided into the occurrence group and the non-occurrence group.Lasso regression analysis was used to screen variables,and multivariate Logistic regression analysis was used to analyze the influencing factors of septic shock in patients with severe pneumonia to construct a predic-tion model.The receiver operating characteristic(ROC)curve and decision curve were drawn to evaluate the prediction calibration of the model and analyze the net clinical benefit of the model.Results A total of 98 pa-tients were included in the occurrence group and 102 patients were included in the non-birth group.The pro-portion of patients with malnutrition,chronic obstructive pulmonary disease,gastrointestinal bleeding,the number of organs involved≥3,and the number of lung lobes involved≥3 in the occurrence group.The acute physiology and chronic health evaluation Ⅱ(APACHEⅡ)score,sequential organ failure assessment(SOFA)score,fibrinogen(FIB)and D-dimer(D-D)levels in the occurrence group were higher than those in the non-occurrence group,and activated partial thromboplastin time(APTT),prothrombin time(PT)and thrombin time(TT)were longer than those in the non-occurrence group.The differences were statistically significant(P<0.05).After selecting variables by Lasso regression analysis,multivariate Logistic regression analysis showed that chronic obstructive pulmonary disease,the number of lung lobes involved≥3,the number of or-gans involved≥3,APACHEⅡ score,SOFA score,FIB and D-D levels increased.Prolonged APTT,PT,and TT were all risk factors for septic shock in patients with severe pneumonia(P<0.05),and the model was es-tablished.ROC curve analysis showed that the area under the curve(AUC)of FIB,D-D,APTT,PT and TT combined to predict septic shock in patients with severe pneumonia was 0.901,and the AUC of the composite model containing coagulation function indicators to predict septic shock in patients with severe pneumonia was 0.930.The results of decision curve analysis showed that compared with the coagulation function index mod-el,the composite model containing coagulation function indicators had a higher net benefit rate.Conclusion The incidence of septic shock in patients with severe pneumonia is affected by APACHEⅡ score,SOFA score,chronic obstructive pulmonary disease,the number of lung lobes involved,the number of organs involved and coagulation function indicators.The composite model including coagulation function indicators has higher accuracy and net benefit rate in predicting septic shock in patients with severe pneumonia,and has good clinical application value.关键词
凝血功能/重症肺炎/感染性休克/模型/风险/影响因素Key words
coagulation function/severe pneumonia/septic shock/model/risk/influencing factor分类
临床医学引用本文复制引用
王芳,康震,付继京,武晓晓,张晓庆,刘雅,靳朝晖..重症肺炎患者发生感染性休克的预测模型构建[J].检验医学与临床,2025,22(8):1138-1142,1147,6.基金项目
河北省邯郸市科学技术研究与发展计划项目(23422083332). （23422083332）
