目的 探讨重症肺炎患者并发弥散性血管内凝血(disseminated intravascular coagulation,DIC)的影响因素,基于独立影响因素构建并验证风险预测模型.方法 前瞻性选取2021年1月至2023年1月在浙江省永康市中医院住院治疗的重症肺炎患者224例,根据患者住院期间有无并发DIC分为凝血组(n=55)和无凝血组(n=169),通过单因素分析及多因素Logistic回归分析得出独立影响因素,并基于回归分析法构建预测模型,使用R语言软件绘制对应的列线图,采用受试者工作特征(receiver operating characteristic,ROC)曲线、校准曲线检验模型的预测效能.另选取2023年2月至2023年12月收治的96例重症肺炎患者作为验证集,采用验证集数据绘制ROC及校准曲线对模型的预测效能进行外部验证.结果 单因素分析及多因素Logistic回归分析结果显示,入院体温、肺炎类型、急性生理学和慢性健康状况评价Ⅱ(acute physiology and chronic health evaluation Ⅱ,APACHE Ⅱ)、血小板(platelet,PLT)、白细胞(white blood cell,WBC)、凝血酶原时间(prothrombin time,PT)、D-二聚体、纤维蛋白原、超敏C反应蛋白(hypersensitive C-reactive protein,hs-CRP)、肿瘤坏死因子-α(tumor necrosis factor-α,TNF-α)及合并高脂血症均是重症肺炎患者并发DIC的独立影响因素(P＜0.05),基于以上11个独立影响因素构建风险预测模型的曲线下面积(the area under curve,AUC)为0.936,最佳截断值0.122,对应敏感度、特异度分别为96.4％、85.7％,模型的区分能力良好.校准曲线结果显示,平均绝对误差(mean absolute error,MAE)为0.049,校准曲线贴合理想曲线,表示模型具有较好的校准性能,模型较为可靠稳定.验证集ROC曲线AUC为0.978,校准曲线结果良好,提示模型具有较好的外部预测效能.结论 重症肺炎患者并发DIC受入院体温、肺炎类型、凝血功能、炎症反应等因素影响,基于以上11个独立影响因素构建的列线图风险预测模型预测效能良好,可为临床上预防重症肺炎患者并发DIC提供参考.
Construction and verification of a risk prediction model for the patients with severe pneumonia complicated by disseminated intravascular coagulation
Objective To investigate the influencing factors of severe pneumonia complicated by disseminated intravascular coagulation(DIC),thereby establishing and verifying risk prediction models based on independent influencing factors.Methods A total of 224 patients with severe pneumonia hospitalized in Yongkang Hospital of Traditional Chinese Medicine from January 2021 to January 2023 were prospectively selected and divided into coagulation group(n=55)and non-coagulation group(n=169)according to whether DIC occurred during hospitalization.Independent influencing factors were obtained by univariate analysis and multivariate Logistic regression analysis.The prediction model was constructed based on regression analysis,the corresponding nomogram was drawn with R language software,and the predictive efficiency of the model was tested by receiver operating characteristic(ROC)curve and calibration curve.In addition,96 patients admitted from February 2023 to December 2023 were selected as validation set,and ROC curve and calibration curve were drawn with validation set data to externally verify the predictive efficiency of the model.Results Univariate analysis and multivariate Logistic regression analysis showed that body temperature of the admission day,the type of pneumonia,acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ),platelet(PLT),white blood cell(WBC),prothrombin time(PT),D-dimer,fibrinogen,hypersensitive C-reactive protein(hs-CRP),tumor necrosis factor-α(TNF-α)and hyperlipidemia were independent influencing factors for the patients with severe pneumonia complicated by DIC(P＜0.05).The area under curve(AUC)of the risk prediction model based on the above 11 independent influencing factors was 0.936 and the optimal cut-off value was 0.122 with the sensitivity of 96.4％and the specificity of 85.7％,and the model has good discriminative ability.The calibration curve results showed that mean absolute error(MAE)was 0.049,and the calibration curve was close to the ideal curve,indicating that the model had good calibration performance and was relatively reliable and stable.The AUC of validation set was 0.978,the results of the calibration curve was good,indicating that the model has good external prediction efficiency.Conclusions DIC in the patients with severe pneumonia is affected by body temperature of the admission day,the type of pneumonia,coagulation function,inflammatory response and other factors.The risk prediction model based on the above 11 independent influencing factors has good predictive efficiency,and can provide the references for the prevention of DIC complications in clinic.
Severe pneumoniaDisseminated intravascular coagulationNomogramRisk prediction modelBody temperature of the admission dayType of pneumoniaCoagulation functionInflammatory response
万方数据
维普
