健康管理人群支气管哮喘风险预测模型

doi:10.6040/j.issn.1671-7554.0.2017.425

摘要/Abstract

摘要： 目的构建健康管理人群支气管哮喘的风险预测模型。方法基于健康管理队列人群,针对队列基线中无支气管哮喘的77 493人健康体检对象随访,随访结局为支气管哮喘;采用单因素Cox回归模型筛选预测因子,单因素有意义的变量进入多因素Cox回归,采用向后消除法筛选变量,利用筛选出的预测因子构建Cox风险预测模型;采用ROC曲线下的面积评估模型判别准确度,十折交叉验证评估模型稳定性。结果随访9年期间134人被诊断为支气管哮喘。总发病密度48.28/10万人年。最终纳入模型的变量包括:年龄、嗜酸性粒细胞计数、低密度脂蛋白胆固醇、鼻炎史、气管/支气管炎史、慢性阻塞性肺疾病史。ROC曲线下面积(95%CI)为0.725(0.673~0.778),十折交叉验证ROC曲线下面积(95%CI)为0.707(0.647~0.767)。结论本研究构建的支气管哮喘风险预测模型可用于预测体检人群的支气管哮喘发病风险。

关键词: 支气管哮喘, 风险预测模型, 队列研究, Cox回归, 健康管理人群

Abstract: Objective To construct a prediction model for bronchial asthma risks based on a health management population. Methods A cohort consisting of 77,493 non-bronchial asthma individuals at baseline was followed up to detect the incidence of bronchial asthma. The risk factors were screened with a single-variable Cox regression model. The selected risk factors were analyzed with multivariate Cox regression model and backwards method. Then a Cox risk prediction model was constructed. The validation and predictive ability of the model were evaluated with the area under the receiver operator characteristic(ROC)curve. The stability of the model was tested with ten-fold cross validation method. Results During the 9-year follow-up, 134 new cases of bronchial asthma were observed. The variables finally included in the prediction model were age, eosinophil count(EOS), low density lipoprotein cholesterol(LDL-C), rhinitis history, trachea-bronchitis history and chronic obstructive pulmonary disease(COPD)history. The area under the ROC curve(95%CI)of the model was 0.725(0.673-0.778), and the area under the ROC curve(95%CI)of the ten-fold cross validation result was 0.707(0.647-0.767). Conclusion A prediction model for bronchial asthma risks has been constructed.

Key words: Bronchial asthma, Prediction model, Cohort study, Cox regression, Health management population

中图分类号:

R562.2

柳晓涓,丁荔洁,康凤玲,周苗,薛付忠. 健康管理人群支气管哮喘风险预测模型[J]. 山东大学学报 (医学版), 2017, 55(12): 56-61.

LIU Xiaojuan, DING Lijie, KANG Fengling, ZHOU Miao, XUE Fuzhong. A prediction model for bronchial asthma risk based on a health management population[J]. Journal of Shandong University (Health Sciences), 2017, 55(12): 56-61.

参考文献 34

[1]	Horak F, Doberer D, Eber E, et al. Diagnosis and management of asthma-Statement on the 2015 GINA Guidelines[J]. Wien Klin Wochenschr, 2016, 128(15-16): 541-554.
[2]	Bateman ED, Hurd SS, Barnes PJ, et al. Global strategy for asthma management and prevention: GINA executive summary[J]. Eur Respir J, 2008, 31(1): 143-178.
[3]	苏楠. 我国8省市支气管哮喘患者控制水平的流行病学调查[J]. 中华内科杂志, 2014, 53(8): 601-606. SU Nan. An epidemiological survey of current asthma control status in China[J]. Chin J Intern Med, 2014, 53(8): 601-606.
[4]	Lang DM. Severe asthma: epidemiology, burden of illness, and heterogeneity[J]. Allergy Asthma Proc, 2015, 36(6): 418-424.
[5]	Barnes PJ. Severe asthma: advances in current management and future therapy[J]. J Allergy Clin Immunol, 2012, 129(1): 48-59.
[6]	Himes BE, Dai Y, Kohane IS, et al. Prediction of chronic obstructive pulmonary disease(COPD)in asthma patients using electronic medical records[J]. J Am Med Inform Assoc, 2009, 16(3): 371-379.
[7]	Wildman MJ, Sanderson C, Groves J, et al. Predicting mortality for patients with exacerbations of COPD and Asthma in the COPD and Asthma Outcome Study(CAOS)[J]. QJM, 2009, 102(6): 389-399.
[8]	Corren J. Asthma phenotypes and endotypes: an evolving paradigm for classification[J]. Discov Med, 2013, 15(83): 243-249.
[9]	Matsui T. Sudden asthma death: etiology and prevention[J]. Nihon Rinsho, 2005, 63(7): 1214-1219.
[10]	Gullach AJ, Risgaard B, Lynge TH, et al. Sudden death in young persons with uncontrolled asthma-a nationwide cohort study in Denmark[J]. BMC Pulm Med, 2015, 15:35. doi:10.1186/s12890-015-0033-z.
[11]	Iribarren C, Tolstykh IV, Miller MK, et al. Adult asthma and risk of coronary heart disease, cerebrovascular disease, and heart failure: a prospective study of 2 matched cohorts[J]. Am J Epidemiol, 2012, 176(11): 1014-1024.
[12]	Tattersall MC, Guo M, Korcarz CE, et al. Asthma predicts cardiovascular disease events: the multi-ethnic study of atherosclerosis[J]. Arterioscler Thromb Vasc Biol, 2015, 35(6): 1520-1525.
[13]	Liu DW, Zhen XG, Liang Y, et al. Persistent asthma increases the risk of chronic kidney disease: a retrospective cohort study of 2354 patients with asthma[J]. Chin Med J(Engl), 2013, 126(21): 4093-4099.
[14]	Huang HL, Ho SY, Li CH, et al. Bronchial asthma is associated with increased risk of chronic kidney disease[J]. BMC Pulm Med, 2014, 14: 80. doi: 10.1186/1471-2466-14-80.
[15]	Taylor DR. Biomarkers of inflammation in asthma: a clinical perspective[J]. Semin Respir Crit Care Med, 2012, 33(6): 620-629.
[16]	Bannier MA, van de Kant KD, Jobsis Q, et al. Biomarkers to predict asthma in wheezing preschool children[J]. Clin Exp Allergy, 2015, 45(6): 1040-1050.
[17]	Zein JG, Dweik RA, Comhair SA, et al. Asthma is more severe in older adults[J]. PLoS One, 2015, 10(7): e0133490. doi: 10.1371/journal.pone.0133490.
[18]	Tamimi A, Serdarevic D, Hanania NA. The effects of cigarette smoke on airway inflammation in asthma and COPD: therapeutic implications[J]. Respir Med, 2012, 106(3): 319-328.
[19]	Liu T, Valdez R, Yoon PW, et al. The association between family history of asthma and the prevalence of asthma among US adults: National Health and Nutrition Examination Survey, 1999—2004[J]. Genet Med, 2009, 11(5): 323-328.
[20]	Pescatore AM, Dogaru CM, Duembgen L, et al. A simple asthma prediction tool for preschool children with wheeze or cough[J]. J Allergy Clin Immunol, 2014, 133(1): 111-118.
[21]	Klaassen EM, van de Kant KD, Jöbsis Q, et al. Exhaled biomarkers and gene expression at preschool age improve asthma prediction at 6 years of age[J]. Am J Respir Crit Care Med, 2015, 191(2): 201-207.
[22]	Devulapalli CS, Carlsen KC, Håland G, et al. Severity of obstructive airways disease by age 2 years predicts asthma at 10 years of age[J]. Thorax, 2008, 63(1): 8-13.
[23]	van der Mark LB, van Wonderen KE, Mohrs J, et al. Predicting asthma in preschool children at high risk presenting in primary care: development of a clinical asthma prediction score[J]. Prim Care Respir J, 2014, 23(1): 52-59.
[24]	Luo G, Nkoy FL, Stone BL, et al. A systematic review of predictive models for asthma development in children[J]. BMC Med Inform Decis Mak, 2015, 15: 99. doi: 10.1186/s12911-015-0224-9.
[25]	中华医学会呼吸病学分会哮喘学组. 支气管哮喘防治指南(2016年版)[J]. 中华结核和呼吸杂志, 2016, 39(9): 1-24.
[26]	Scichilone N, Rizzo M, Benfante A, et al. Serum low density lipoprotein subclasses in asthma[J]. Respir Med, 2013, 107(12): 1866-1872.
[27]	Rasmussen F, Hancox RJ, Nair P, et al. Associations between airway hyperresponsiveness, obesity and lipoproteins in a longitudinal cohort[J]. Clin Respir J, 2013, 7(3): 268-275.
[28]	Furuta GT, Atkins FD, Lee NA, et al. Changing roles of eosinophils in health and disease[J]. Ann Allergy Asthma Immunol, 2014, 113(1): 3-8.
[29]	Brightling CE. Eosinophils, bronchitis and asthma: pathogenesis of cough and airflow obstruction[J]. Pulm Pharmacol Ther, 2011, 24(3): 324-327.
[30]	Meijer RJ, Postma DS, Kauffman HF, et al. Accuracy of eosinophils and eosinophil cationic protein to predict steroid improvement in asthma[J]. Clin Exp Allergy, 2002, 32(7): 1096-1103.
[31]	张娟, 朱建波,赵铭山. 嗜酸性粒细胞在支气管哮喘发病中作用的研究进展[J]. 中外医疗, 2011, 30(8):179, 181.
[32]	Khan DA. Allergic rhinitis and asthma: epidemiology and common pathophysiology[J]. Allergy Asthma Proc, 2014, 35(5): 357-361.
[33]	Boulay ME, Morin A, Laprise C, et al. Asthma and rhinitis: what is the relationship?[J]. Curr Opin Allergy Clin Immunol, 2012, 12(5): 449-454.
[34]	Nakagome K, Nagata M. Pathogenesis of airway inflammation in bronchial asthma[J]. Auris Nasus Larynx, 2011, 38(5): 555-563.

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