影响重症肺炎患者预后的相关因素分析及构建nomogram预测模型的价值研究

殷菲

doi:10.13201/j.issn.1009-5918.2020.10.011

影响重症肺炎患者预后的相关因素分析及构建nomogram预测模型的价值研究

殷菲^,

- 苏州市第九人民医院急诊科(江苏苏州, 215200)
基金项目:
苏州市第九人民医院院级科研项目(No:YK202032)

详细信息

通讯作者: 殷菲,E-mail:yinfxyz@163.com

中图分类号: R459.7

收稿日期: 2020-06-18

Analysis of relevant factors affecting the prognosis of patients with severe pneumonia and research on the value of establishing nomogram prediction model

YIN Fei^,

- Department of Emergency, Suzhou No.;

More Information

Corresponding author: YIN Fei, E-mail: yinfxyz@163.com

Received Date: 18 June 2020

摘要

摘要: 目的:分析和筛选影响重症肺炎患者预后的相关因素,构建nomogram预测模型,并评估模型的临床应用价值。方法:选用2014-12-2019-12期间入住苏州市第九人民医院ICU的124例重症肺炎患者,根据预后情况分为预后良好组(n=61)和预后不良组(n=63)。采用单因素分析和多因素Logistic回归分析筛选影响预后的相关因素,构建预测模型,绘制nomogram列线图,并验证模型的区分度和校准度。再通过绘制预测模型、APACHEⅡ评分、SOFA评分、PSI评分、CURB-65评分的ROC曲线,计算各曲线的截断值、敏感度、特异度、AUC。利用AUC及NRI、IDI等指标对预测模型与APACHEⅡ评分、SOFA评分、PSI评分、CURB-65评分进行比较,评价新模型在预测预后方面的改善效果。结果:平均动脉压(MAP)、格拉斯哥评分(GCS)、动脉血乳酸(LAC)、白蛋白(Alb)、肌酐(SCr)为重症肺炎患者预后不良的独立影响因素。由上述独立影响因素建立的预测模型回归方程为:Logit(P)=15.670-0.061X₁-0.618X₂+0.469X₃-0.210X₄+0.014X₅,模型的C-index(即AUC)为0.928,HL拟合优度检验χ²(5)=7.366(P=0.498),Brier Score为0.109,Bootstrap方法内部验证的绝对误差为0.046。预测模型的AUC为0.928显著高于CURB-65评分(P<0.01),另其AUC高于APACHEⅡ评分、SOFA评分、PSI评分,差异无统计学意义(P>0.05)。经NRI、IDI分析显示预测模型相对APACHEⅡ评分、SOFA评分、PSI评分重新分类的连续性NRI分别为0.868(P=0.000)、0.966(P=0.000)、0.578(P<0.000),IDI分别为0.154(P<0.000)、0.119(P=0.020)、0.115(P=0.024)。预测模型的Brier Score低于上述4种评分系统。结论:MAP、GCS、LAC、SCr、Alb为影响重症肺炎患者预后的独立因素;由上述独立因素建立的nomogram预测模型具有良好的区分度及校准度,其评估重症肺炎患者预后准确性高,应用方便快捷,较传统评分系统有一定的改善效果,值得临床推广。
- 重症肺炎 /
- 预后 /
- 列线图 /
- 预测模型
Abstract: Objective:To analyze the prognostic factors of patients with severe pneumonia, establish an individualized nomogram prediction model and evaluate the clinical efficiency of the prediction model. Method:From December 2014 to December 2019, 124 patients with severe pneumonia were admitted to the ICU of the Ninth People's Hospital of Suzhou. They were divided into good prognosis group(n=61) and poor prognosis group(n=63) according to the clinical prognosis. Univariate analysis and multivariate Logistic regression analysis were used to analyze the prognostic factors of patients. The corresponding nomogram prediction mode was drawn according to the regression coefficients. The discrimination and calibration of the prediction model were estimated. The ROC curves of the prediction model, APACHE Ⅱ score, SOFA score, PSI score and CURB-65 score were drawn to calculate the cut-off value, sensitivity, specificity and area under curve(AUC). The predictive efficiency of the prediction model and scoring systems of APACHE Ⅱ, SOFA, PSI, CURB-65 were evaluated by AUC, net reclassification index(NRI) and integrated discrimination(IDI). Result:Mean arterial pressure(MAP), Glasgow score(GCS), arterial blood lactate(LAC), albumin(Alb) and creatinine(SCr) were the independent factors affecting poor prognosis in patients with severe pneumonia. The prediction model was established by the above independent factors. The regression equation was Logit(P)=15.670-0.061 X₁ -0.618 X₂ + 0.469 X₃-0.210 X₄ + 0.014 X₅. The powerful discrimination(AUC=0.928) and well calibration(HL P=0.498, Brier score 0.109) in predicting poor prognosis among the severe pneumonia patients was demonstrated. Furthermore, bootstrap method was used for internal verification which also showed the excellent calibration. The AUC of the prediction model was 0.928, which was significantly higher than that of the CURB-65 score(P<0.01). In addition, the AUC of the prediction model was not significantly higher than that of the APACHE Ⅱ score, SOFA score, PSI score(P>0.05). The results of continuous NRI and IDI analysis showed that the continuous NRI of the prediction model was 0.868(P=0.000), 0.966(P=0.000), 0.578(P<0.000), and the IDI was 0.154(P<0.000), 0.119(P=0.020), 0.115(P=0.024), respectively. The Brier score of the prediction model was lower than other scoring systems.Conclusion:MAP, GCS, LAC, SCr and Alb were the independent factors that affected the prognosis of patients with severe pneumonia. The nomogram prediction model which was established by the above independent factors had good discrimination and calibration. The accuracy of the model in evaluating the prognosis of patients with severe pneumonia was excellent. The model had a certain improvement effect compared with the traditional scoring systems and was worthy of clinical promotion.
- severe pneumonia /
- prognosis /
- nomogram /
- prediction model

HTML全文

参考文献(21)

[1]	Hansen V,Oren E,Dennis LK,et al.Infectious Disease Mortality Trends in the United States,1980-2014[J].JAMA,2016,316(20):2149-2151.
[2]	中华医学会呼吸病学分会.中国成人社区获得性肺炎诊断和治疗指南(2016年版)[J].中华结核和呼吸杂志,2016,39(4):253-279.
[3]	李晓燕,薄建萍.社区获得性肺炎诊疗新进展[J].国际呼吸杂志,2020,40(6):470-476.
[4]	Kolditz M,Ewig S,Klapdor B,et al.Community-acquired pneumonia as medical emergency:predictors of early deterioration[J].Thorax,2015,70(6):551-558.
[5]	Wang X,Jiao J,Wei R,et al.A new method to predict hospital mortality in severe community acquired pneumonia[J].Eur J Intern Med,2017,40:56-63.
[6]	Frenzen FS,Kutschan U,Meiswinkel N,et al.Admission lactate predicts poor prognosis independently of the CRB/CURB-65 scores in community-acquired pneumonia[J].Clin Microbiol Infect,2018,24(3):306.e1-306.e6.
[7]	Demirel B.Lactate levels and pneumonia severity index are good predictors of in-hospital mortality in pneumonia[J].The Clinical Respiratory Journal,2018,12(3):991-995.
[8]	Damayanti N,Abidin A,Keliat EN.The correlation between albumin levels with 30 days mortality in community acquired pneumonia patients[J].Earth Environ Sci,2018,125:e012141.
[9]	Miyazaki H,Nagata N,Akagi T,et al.Comprehensive analysis of prognostic factors in hospitalized patients with pneumonia occurring outside hospital:Serum albumin is not less important than pneumonia severity assessment scale[J].J Infect Chemother,2018,24(8):602-609.
[10]	Ramirez JA,Wiemken TL,Peyrani P,et al.Adults Hospitalized With Pneumonia in the United States:Incidence,Epidemiology,and Mortality[J].Clin Infect Dis,2017,65(11):1806-1812.
[11]	Rahimi-Levene N,Koren-Michowitz M,Zeidenstein R,et al.Lower hemoglobin transfusion trigger is associated with higher mortality in patients hospitalized with pneumonia[J].Medicine (Baltimore),2018,97(12):e0192.
[12]	Peyrani P,Ramirez J.Long-term Mortality in Hospitalized Patients With Community-Acquired Pneumonia[J].Am J Med Sci,2017,353(5):421.
[13]	Kim MA,Park JS,Lee CW,et al.Pneumonia severity index in viral community acquired pneumonia in adults[J].PLoS One,2019,14(3):e0210102.
[14]	Kim MW,Lim JY,Oh SH.Mortality prediction using serum biomarkers and various clinical risk scales in community-acquired pneumonia[J].Scand J Clin Lab Invest,2017,77(7):486-492.
[15]	Murillo-Zamora E,Medina-González A,Zamora-Pérez L,et al.Performance of the PSI and CURB-65 scoring systems in predicting 30-day mortality in healthcare-associated pneumonia[J].Med Clin (Barc),2018,150(3):99-103.
[16]	Alba AC,Agoritsas T,Walsh M,et al.Discrimination and Calibration of Clinical Prediction Models:Users' Guides to the Medical Literature[J].JAMA,2017,318(14):1377-1384.
[17]	Van Calster B,Nieboer D,Vergouwe Y,et al.A calibration hierarchy for risk models was defined:from utopia to empirical data[J].J Clin Epidemiol,2016,74:167-176.
[18]	Burke RE,Hess E,Barón AE,et al.Predicting Potential Adverse Events During a Skilled Nursing Facility Stay:A Skilled Nursing Facility Prognosis Score[J].J Am Geriatr Soc,2018,66(5):930-936.
[19]	Manouchehrinia A,Zhu F,Piani-Meier D,et al.Predicting risk of secondary progression in multiple sclerosis:A nomogram[J].Mult Scler,2019,25(8):1102-1112.
[20]	Cook NR.Quantifying the added value of new biomarkers:how and how not[J].Diagn Progn Res,2018,2:14.
[21]	Karaismailoglu E,Konar NM,Goksuluk D,et al.Factors effecting the model performance measures area under the ROC curve,net reclassification improvement and integrated discrimination improvement[J].Communications in Statistics-Simulation and Computation,2019,48(9):2586-2598.