您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（医学版）》

山东大学学报（医学版） ›› 2017, Vol. 55 ›› Issue (6): 66-71.doi: 10.6040/j.issn.1671-7554.0.2017.358

• • 上一篇    下一篇

基于健康管理队列的冠心病风险预测模型

王春霞1,许艺博2,3,杨宁1,夏冰1,王萍4,薛付忠2,3   

  1. 1.济宁医学院附属医院健康管理中心, 山东 济宁 272000;2.山东大学公共卫生学院生物统计学系, 山东 济南 250012;3.山东大学齐鲁生物医学大数据研究中心, 山东 济南 250012;4.山东大学齐鲁医院手术室, 山东 济南 250012
  • 收稿日期:2017-04-26 出版日期:2017-06-10 发布日期:2017-06-10
  • 通讯作者: 薛付忠. E-mail:xuefzh@sdu.edu.cn E-mail:xuefzh@sdu.edu.cn
  • 基金资助:
    国家国际科技合作专项项目(2014DFA32830);山东省医药卫生科技发展计划项目(2013WS0230)

A prediction model for coronary heart disease risks based on health management cohort

WANG Chunxia1, XU Yibo2,3, YANG Ning1, XIA Bing1, WANG Ping4, XUE Fuzhong2,3   

  1. 1. Health Management Center, Affiliated Hospital of Jining Medical University, Jining 272000, Shandong, China;
    2. Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China;
    3. Cheeloo Research Center for Biomedical Big Data, Shandong University, Jinan 250012, Shandong, China;
    4. Operating Room of Qilu Hospital, Shandong University, Jinan 250012, Shandong, China
  • Received:2017-04-26 Online:2017-06-10 Published:2017-06-10

PDF (PC)

343

摘要： 目的 构建基于山东省健康管理队列的冠心病风险预测模型。 方法 构建山东省健康管理队列,基于国际上较为通用的冠心病风险预测模型变量,应用Cox比例风险回归模型进行单因素分析,利用竞争风险模型建立心脑血管事件预测模型,使用十折交叉验证法检验模型稳定性。 结果 共纳入队列73 386人,其中男41 968人,女31 418人。队列中位随访时间3.10年。经随访共有1 545人发生冠心病,其中男958人,发病密度为5.95/1 000人年;女587人,发病密度为4.90/1 000人年。建立的男性模型AUC为0.809(95CI: 0.804~0.815),O/E值为0.98;女性模型AUC为0.869(95%CI: 0.863~0.874),O/E值为1.02。经十折交叉内部验证,男性模型AUC为0.806(95%CI: 0.801~0.812),女性为0.866(95%CI: 0.860~0.872)。 结论 构建的冠心病预测模型在健康管理队列中有较好的预测能力。

关键词: 健康管理队列, 风险预测模型, 冠心病

Abstract: Objective To establish a risk predicting model for coronary heart disease in health management cohort in Shandong province. Methods The cohort consisted of the health management cohort in Shandong province. Cox proportional hazards regression model was applied to screen the variables based on other predictive models. We used competing risk prediction model to establish the prediction model, and ten-fold cross validation to test the stability of the model. Results There were 73 386 subjects in the cohort, including 41 968 males and 31 418 females. The median follow-up time was 3.10 years. Coronary heart disease occurred in 1 545 sbujects, including 958 males and 587 females. The incidence density was 5.95 per 1 000 person-years for males and 4.90 per 1 000 person-years for females. The AUC of the male model was 0.809(95CI: 0.804-0.815), and the O/E value was 0.98. The AUC of the female model was 0.869(95%CI: 0.863-0.874), and the O/E value was 1.02. In the ten-fold cross validation model, the AUC of the male model was 0.806(95%CI: 0.801-0.812), and the AUC of the female model was 0.866(95%CI: 0.860-0.872). Conclusion The predictive model for coronary heart disease has good predictive ability in the health management cohort.

Key words: Coronary heart disease, Health management cohort, Risk prediction model

中图分类号: 

  • R541.4
[1] 隋辉, 陈伟伟, 王文. 《中国心血管病报告2014》要点介绍[J]. 中华高血压杂志, 2015, 23(7): 627-629.
[2] Gerber Y, Tanne D, Medalie JH, et al. Serum uric acid and long-term mortality from stroke, coronary heart disease and all causes[J]. Eur J Cardiovasc Prev Rehabil, 2006, 13(2): 193-198.
[3] Ford ES, Ajani UA, Croft JB, et al. Explaining the decrease in U.S. deaths from coronary disease, 1980-2000[J]. N Engl J Med, 2007, 356(23): 2388-2398.
[4] D'Agostino RS, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study[J]. Circulation, 2008, 117(6): 743-753.
[5] Pencina MJ, D'Agostino RS, Larson MG, et al. Predicting the 30-year risk of cardiovascular disease: the Framingham Heart Study[J]. Circulation, 2009, 119(24): 3078-3084.
[6] Goff DJ, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American college of cardiology/American heart association task force on practice guidelines[J]. Circulation, 2014, 129(25 Suppl 2): S49-S73.
[7] Cuende JI, Cuende N, Calaveras-Lagartos J. How to calculate vascular age with the SCORE project scales: a new method of cardiovascular risk evaluation?[J]. Eur Heart J, 2010, 31(19): 2351-2358.
[8] 中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2013年版)[J]. 中国糖尿病杂志, 2014, 22(8): 2-42.
[9] 中国高血压基层管理指南修订委员会. 中国高血压基层管理指南(2014年修订版)[J]. 中华高血压杂志,2015, 23(1): 24-43.
[10] 诸骏仁, 高润霖, 赵水平, 等. 中国成人血脂异常防治指南(2016年修订版)[J]. 中国循环杂志, 2016, 31(10): 937-953.
[11] 李海彬, 李霞, 王安心, 等. 竞争风险模型及其在Stata软件实现[J]. 中国卫生统计,2016,33(5): 889-891.
[12] Seoane-Pillado MT, Pita-Fernandez S, Valdes-Canedo F, et al. Incidence of cardiovascular events and associated risk factors in kidney transplant patients: a competing risks survival analysis[J]. BMC Cardiovasc Disord, 2017, 17(1): 72. doi: 10.1186/s12872-017-0505-6.
[13] Van Kempen BJ, Ferket BS, Kavousi M, et al. Performance of Framingham cardiovascular disease(CVD)predictions in the Rotterdam Study taking into account competing risks and disentangling CVD into coronary heart disease(CHD)and stroke[J]. Int J Cardiol, 2014, 171(3): 413-418.
[14] Tereshchenko LG, Cygankiewicz I, McNitt S, et al. Predictive value of beat-to-beat QT variability index across the continuum of left ventricular dysfunction: competing risks of noncardiac or cardiovascular death and sudden or nonsudden cardiac death[J]. Circ Arrhythm Electrophysiol, 2012, 5(4): 719-727.
[15] Sadeghpour S, Faghihimani E, Hassanzadeh A, et al. Predictors of all-cause and cardiovascular-specific mortality in type 2 diabetes: a competing risk modeling of an Iranian population[J]. Adv Biomed Res, 2016, 5: 82. doi: 10.4103/2277-9175.182213
[16] Miot A, Ragot S, Hammi W, et al. Prognostic value of resting heart rate on cardiovascular and renal outcomes in type 2 diabetic patients: a competing risk analysis in a prospective cohort[J]. Diabetes Care, 2012, 35(10): 2069-2075.
[17] Wilson PW, D'Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories[J]. Circulation, 1998, 97(18): 1837-1847.
[18] Wilson PW, Bozeman SR, Burton TM, et al. Prediction of first events of coronary heart disease and stroke with consideration of adiposity[J]. Circulation, 2008, 118(2): 124-130.
[19] Lloyd-Jones D, Adams RJ, Brown TM, et al. Heart disease and stroke statistics-2010 update: a report from the American Heart Association[J]. Circulation, 2010, 121(7): e46-e215. doi: 10.1161/CIRCULATIONAHA.109.192666.
[20] 中国成人血脂异常防治指南修订联合委员会. 《中国成人血脂异常防治指南(2016年修订版)》补充说明[J]. 中国循环杂志, 2017, 32(1): 53.
[21] Mosca L, Barrett-Connor E, Wenger NK. Sex/gender differences in cardiovascular disease prevention: what a difference a decade makes[J]. Circulation, 2011, 124(19): 2145-2154.
[22] Conroy RM, Pyorala K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project[J]. Eur Heart J, 2003, 24(11): 987-1003.
[23] Gorenoi V, Schonermark MP, Hagen A. Assessments tools for risk prediction of cardiovascular diseases[J]. GMS Health Technol Assess, 2009, 5: c11. doi: 10.3205/hta000073.
[24] Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study[J]. BMJ, 2007, 335(7611): 136. doi: https: //doi.org/10.1136/bmj.39261.471806.55.
[25] Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2[J]. BMJ, 2008, 336(7659): 1475-1482.
[1] 李吉庆,赵焕宗,宋炳红,张理纯,李向一,陈亚飞,王萍,薛付忠. 基于健康管理队列的心血管事件风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 56-60.
[2] 于涛,刘焕乐,冯新,徐付印,陈亚飞,薛付忠,张成琪. 基于健康管理队列的高血压风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 61-65.
[3] 张光,王广银,吴红彦, 张红玉,王停停,李吉庆,李敏,康凤玲,刘言训,薛付忠. 健康管理人群高脂血症风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 72-76.
[4] 苏萍,杨亚超,杨洋,季加东,阿力木·达依木,李敏,薛付忠,刘言训. 健康管理人群2型糖尿病发病风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 82-86.
[5] 孙苑潆,杨亚超,曲明苓,陈雁敏,李敏,王淑康,薛付忠,刘云霞. 健康管理人群代谢综合征发病风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 87-92.
[6] 周苗,夏同耀,孙爱玲,李明,申振伟,卞伟玮,蒋正,康凤玲,柳晓涓,薛付忠,刘静. 健康管理人群慢性肾脏病风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 98-103.
[7] 李敏,王春霞,夏冰,朱茜,孙苑潆,王淑康,薛付忠,贾红英. 健康管理人群脑卒中风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 93-97.
[8] 于媛媛,王春霞,苏萍,孙苑潆,薛付忠,刘言训. 健康管理队列白内障发病风险预测模型[J]. 山东大学学报（医学版）, 2017, 55(6): 104-107.
[9] 杨洋,张光,张成琪,宋心红,薛付忠,王萍,王丽,刘言训. 基于体检队列的2型糖尿病风险预测模型[J]. 山东大学学报（医学版）, 2016, 54(9): 69-72.
[10] 屈庆喜, 赵鑫, 孙文宇, 宋光民, 孟祥斌, 毕研文. 同期冠状动脉旁路移植术加颈动脉内膜剥脱术治疗冠心病合并颈动脉狭窄的疗效[J]. 山东大学学报（医学版）, 2015, 53(5): 71-74.
[11] 刘天骄, 郭媛, 李婷婷, 张建宁, 李俊, 张鹏. 血清CTRP9、APN水平与急性冠脉综合征的相关性[J]. 山东大学学报（医学版）, 2014, 52(9): 58-62.
[12] 姜红梅1，2，陈文强1. 老年冠心病心衰患者运动康复治疗中心理干预的临床意义[J]. 山东大学学报（医学版）, 2014, 52(4): 85-88.
[13] 张旭平，张冬，李梓，刘鲁祁. 糖尿病与原位大隐静脉病变的相关性[J]. 山东大学学报(医学版), 2013, 51(11): 66-70.
[14] 陈伟，谈红，李晓燕，杨燕，韦修莹，陈英剑，张红明. 冠心病患者外周血内皮祖细胞数量差异及其对内皮功能的影响[J]. 山东大学学报(医学版), 2012, 50(8): 73-76.
[15] 袁昕，郑哲，胡盛寿. EuroSCORE风险评估系统在预测冠脉旁路移植术后患者再入院风险中的应用价值[J]. 山东大学学报(医学版), 2012, 50(8): 77-80.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(医学版)》编辑部　
地址:山东大学科技期刊社（济南市历城区山大南路27号山东大学中心校区明德楼B座721室） 电话: 0531-88366918 E-mail: xbyxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发