摘要
目的:探究血清标志物Nomogram预测模型对老年重症肺炎(SP)预后的预测价值.方法:选取2022年1月至2023年1月武汉市红十字会医院收治的310例老年SP患者,按7∶3比例随机分为建模人群(n=217)与验证人群(n=93).比较建模人群、验证人群入院28d内预后情况,血清可溶性髓系细胞表达的触发受体-1(sTREM-1)、基质金属蛋白酶抑制剂-1(TIMP-1)、可溶性白细胞分化抗原14亚型(Presepsin)、N末端脑钠肽前体(NT-proBNP)、C反应蛋白(CRP)、饥饿素(Ghrelin)、降钙素原(PCT)、中性粒细胞与淋巴细胞比值(NLR)、肿瘤坏死因子-α(TNF-α)和白介素-6(IL-6)水平,Lasso-logistic回归分析老年SP预后不良的预测因素,并构建预后不良Nomogram预测模型,在验证人群中对Nomogram预测模型进行外部验证.结果:建模人群入院28d内死亡78例(35.94%),验证人群入院28d内死亡34例(36.56%),两组病死率比较无统计学差异(P＞0.05).建模人群、验证人群中,不同预后患者血清sTREM-1、NT-proBNP、TIMP-1、Presepsin、PCT、Ghrelin、CRP、IL-6、NLR、TNF-α水平比较,差异有统计学意义(P＜0.05).Lasso回归筛选预测因素,logistic回归分析显示,血清sTREM-1、TIMP-1、NT-proBNP、Presepsin、Ghrelin、PCT、NLR水平为老年SP预后不良的影响因素(P＜0.05).基于Lasso-logistic回归预测因素构建预测模型,验证人群受试者工作特征(ROC)曲线、临床决策曲线(DCA)显示,该预测模型具有良好的临床效用.结论:血清sTREM-1、TIMP-1、NT-proBNP、Presepsin、Ghrelin、PCT、NLR水平为老年SP患者预后不良的预测因子,基于以上因素构建Nomogram预测模型具有一定的临床价值.
Abstract
Objective:To investigate the predictive value of Nomogram prediction model based on serum mark-ers for senile severe pneumonia(SP).Methods:A total of 310 senile patients with SP admitted to Wuhan Red Cross Hospital from January 2022 to January 2023 were selected and randomly divided into modeling population(n=217)and validation population(n=93)according to a ratio of 7:3.The prognosis within 28 days after admis-sion was compared between the modeling population and the validation population.Serum soluble triggering re-ceptor expressed on myeloid cells-1(sTREM-1),tissue inhibitor of metalloproteinase-1(TIMP-1),soluble leuko-cyte differentiation antigen 14 subtype(Presepsin),N-terminal pro-brain natriuretic peptide(NT-proBNP),C-reac-tive protein(CRP),Ghrelin,procalcitonin(PCT),neutrophils lymphocytes ratio(NLR),and the levels of tumor necrosis factor-α(TNF-α)and interleukin-6(IL-6)were compared.Lasso-logistic regression analysis was used to analyze the predictive factors of poor prognosis in senile patients with SP,and a Nomogram prediction model of poor prognosis was constructed.The Nomogram prediction model was externally validated in the validation popu-lation.Results:Seventy-eight patients(35.94%)died within 28 days after admission in the modeling group,and 34 patients(36.56%)died within 28 days in the validation group.There was no significant difference in the case fatality rate between the two groups(P＞0.05).There were significant differences in serum sTREM-1,NT-proB-NP,TIMP-1,Presepsin,PCT,Ghrelin,CRP,IL-6,NLR and TNF-α levels among patients with different prognosis in modeling population and validation population(P＜0.05).Logistic regression analysis showed that serum lev-els of sTREM-1,TIMP-1,NT-proBNP,Presepsin,Ghrelin,PCT and NLR were the influencing factors for the poor prognosis of senile SP(P＜0.05).Based on Lasso-logistic regression prediction factors,the prediction mod-el was constructed to verify the receiver operating characteristic(ROC)curve and clinical decision curve(DCA)of the population.The results showed that the prediction model had good reference clinical utility.Conclusion:Serum sTREM-1,TIMP-1,NT-proBNP,Presepsin,Ghrelin,PCT and NLR levels are predictive factors of poor prognosis in senile patients with SP,and the Nomogram prediction model based on the above factors has certain clinical value.
维普
万方数据