留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

电子病历数据库在临床研究中的应用及偏倚控制

王丽 程凯亮

王丽, 程凯亮. 电子病历数据库在临床研究中的应用及偏倚控制[J]. 协和医学杂志, 2018, 9(2): 177-182. doi: 10.3969/j.issn.1674-9081.2018.02.014
引用本文: 王丽, 程凯亮. 电子病历数据库在临床研究中的应用及偏倚控制[J]. 协和医学杂志, 2018, 9(2): 177-182. doi: 10.3969/j.issn.1674-9081.2018.02.014
Li Wang, Kailiang Cheng. Database of Electronic Health Records: Application in Clinical Research and Bias Control[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(2): 177-182. doi: 10.3969/j.issn.1674-9081.2018.02.014
Citation: Li Wang, Kailiang Cheng. Database of Electronic Health Records: Application in Clinical Research and Bias Control[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(2): 177-182. doi: 10.3969/j.issn.1674-9081.2018.02.014

电子病历数据库在临床研究中的应用及偏倚控制

doi: 10.3969/j.issn.1674-9081.2018.02.014
基金项目: 

公益性卫生行业专项 201502005

详细信息
    通讯作者:

    王丽  电话:010-69156971, E-mail:wangli0528@vip.sina.com; liwang@ibms.pumc.edu.cn

  • 中图分类号: R-1

Database of Electronic Health Records: Application in Clinical Research and Bias Control

More Information
  • 摘要: 电子病历数据库(electronic health records, EHRs)的出现为大样本临床队列研究提供了低成本、高效率的愿景和机遇,但利用EHRs开展临床研究仍然存在误区和挑战。本文回归流行病学的本质,通过实例探讨EHRs的特征,EHRs中可获得的信息与临床研究中暴露变量、结局变量及协变量之间的对应关系,如何利用EHRs进行患病率及发病率估计、开展疗效和政策评价以及在临床研究中对选择偏倚、混杂偏倚尤其是未测量混杂偏倚的控制等,旨在促进EHRs与临床研究的整合,提高临床研究的效率及质量。
  • 表  1  电子病历数据库中信息与临床研究中变量的对应关系[3]

    电子病历数据库信息分类 电子病历数据库中的相关信息 可能在临床研究中扮演的角色
    人口学 年龄、性别、民族、居住地 暴露因素、协变量(混杂因素、效应修饰因子)
    生命指征 脉搏、血压 结局变量、暴露变量、协变量
    诊断 ICD-9、ICD-10 结局变量、疾病严重程度、结合诊断时间可提示病程
    实验室检查 血尿常规、肝肾功能、代谢相关指标 结局变量、协变量、同时可帮助评价诊断的准确性、疾病的严重程度等
    用药 名称、剂量、频率、使用时间 干预指标、提示疾病的严重程度
    影像学 超声、磁共振成像等 辅助诊断
    文本描述 - 病史、其他暴露信息等
    ICD:国际疾病分类
    下载: 导出CSV
  • [1] ISO/TR 20514: 2005. Health informatics-electronic health record-definition, scope, and context[EB/OL].https://www.iso.org/standard/39525.html.
    [2] 王雯, 刘艳梅, 谭婧, 等.回顾性数据库研究的概念、策划与研究数据库构建[J].中国循证医学杂志, 2018, 18:230-237. http://www.cnki.com.cn/Article/CJFDTotal-ZZXZ201802016.htm
    [3] Casey JA, Schwartz BS, Stewart WF, et al. Using electronic health records for population health research: a review of methods and applications[J]. Annu Rev Publ Health, 2016, 37:61-81. doi:  10.1146/annurev-publhealth-032315-021353
    [4] Kavakiotis I, Tsave O, Salifoglou A, et al. Machine learning and data mining methods in diabetes research[J]. Comput Struct Biotechnol J, 2017, 15:104-116. doi:  10.1016/j.csbj.2016.12.005
    [5] Ng SC, Leung WK, Shi HY, et al. Epidemiology of inflammatory bowel disease from 1981 to 2014: results from a territory-wide population-based registry in HongKong[J]. Infl-amm Bowel Dis, 2016, 22:1954-1960. doi:  10.1097/MIB.0000000000000846
    [6] Esteban-Vasallo MD, Dominguez-Berjon MF, Astray-Moch-ales J, et al. Epidemiological usefulness of population-based electronic clinical records in primary care: estimation of the prevalence of chronic diseases[J]. Fam Pract, 2009, 26:445-454. doi:  10.1093/fampra/cmp062
    [7] Tomasallo CD, Hanrahan LP, Tandias A, et al. Estimating wisconsin asthma prevalence using clinical electronic health records and public health data[J]. Am J Public Health, 2014, 104:E65-E73. http://europepmc.org/abstract/med/24228643
    [8] Bagley SC, Altman RB. Computing disease incidence, prevalence and comorbidity from electronic medical records[J]. J Biomed Inform, 2016, 63:108-111. doi:  10.1016/j.jbi.2016.08.005
    [9] Mamtani R, Haynes K, Finkelman BS, et al. Distinguishing incident and prevalent diabetes in an electronic medical records database[J]. Pharmacoepidemiol Drug Saf, 2014, 23:111-118. doi:  10.1002/pds.3557
    [10] Wu CY, Lin JT, Ho HJ, et al. Association of nucleos(t)-ide analogue therapy with reduced risk of hepatocellular carcinoma in patients with chronic hepatitis B: a nationwide cohort study[J]. Gastroenterology, 2014, 147:143-151 doi:  10.1053/j.gastro.2014.03.048
    [11] Qiu Q, Duan XW, Li Y, et al. Impact of partial reimbursement on hepatitis B antiviral utilization and adherence[J]. World J Gastroenterol, 2015, 21:9588-9597. doi:  10.3748/wjg.v21.i32.9588
    [12] Qiu Q, Li Y, Cheng K, et al. Cost-effectiveness of partial reimbursement for hepatitis B anti-viral drugs in Beijing, China: an analysis based on a retrospective cohort study[J]. Lancet, 2015, 386:S23-S23. http://www.onacademic.com/detail/journal_1000038924218110_f114.html
    [13] Lucas R, Ponsonby AL, McMichael A, et al. Observational analytic studies in multiple sclerosis: controlling bias through study design and conduct. The Australian Multicentre Study of Environment and Immune Function[J]. Mult Scler, 2007, 13:827-839. doi:  10.1177/1352458507077174
    [14] Brookhart MA, Sturmer T, Glynn RJ, et al. Confounding control in healthcare database research: challenges and potential approaches[J]. Med Care, 2010, 48:S114-S120. http://drc.bmj.com/lookup/external-ref?access_num=20473199&link_type=MED&atom=%2Fbmjdrc%2F5%2F1%2Fe000435.atom
    [15] Pearce N, Checkoway H, Kriebel D. Bias in occupational epidemiology studies[J]. Occup Environ Med, 2007, 64:562-568. doi:  10.1136/oem.2006.026690
    [16] Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects[J]. Biometrika, 1983, 70:41-55. doi:  10.1093/biomet/70.1.41
    [17] Brookhart MA, Wang PS, Solomon DH, et al. Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable[J]. Epidemiology, 2006, 17:268-275. doi:  10.1097/01.ede.0000193606.58671.c5
    [18] Ashenfelter O, Card D. Using the longitudinal structure of earnings to estimate the effect of training programs[J]. Rev Econ Stat, 1985, 67:648-660. doi:  10.2307/1924810
    [19] Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study[J]. Stat Med, 2007, 26:734-753. doi:  10.1002/sim.2580
    [20] Mccandless LC, Gustafson P, Austin PC. Bayesian propensity score analysis for observational data[J]. Stat Med, 2009, 28:94-112. doi:  10.1002/sim.3460
    [21] Li L, Shen C, Wu AC, et al. Propensity score-based sensitivity analysis method for uncontrolled confounding[J]. Am J Epidemiol, 2011, 174:345. doi:  10.1093/aje/kwr096
    [22] Schlesselman JJ. Assessing effects of confounding variables[J]. Am J Epidemiol, 1978, 108:3-8. http://academic.oup.com/aje/article/108/1/3/195354
    [23] Greenland S. The Impact of Prior Distributions for uncont-rolled confounding and response bias[J]. JASA, 2003, 98:47-54. doi:  10.1198/01621450338861905
    [24] Mccandless LC, Gustafson P, Austin PC, et al. Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure patients[J]. Epidemiol Perspect Innov, 2009, 6:5. doi:  10.1186/1742-5573-6-5
    [25] An W. Bayesian propensity score estimators: incorporating uncertainties in propensity scores into causal inference[J]. Sociol Methodol, 2015, 40:151-189. http://biomet.oxfordjournals.org/cgi/ijlink?linkType=ABST&journalCode=spsmx&resid=40/1/151
    [26] Greenland S. An introduction to instrumental variables for epidemiologists[J]. Int J Epidemiol 2000;29:1102. http://ije.oxfordjournals.org/content/29/4/722
    [27] Stukel TA, Fisher ES, Wennberg DE, et al. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods[J]. JAMA, 2007, 297:278-285. doi:  10.1001/jama.297.3.278
    [28] Ashenfelter O. Estimating the effect of training programs on earnings[J]. Rev Econ Stat, 1978, 60:47-57. doi:  10.2307/1924332
    [29] 黄远飞, 张家业.城乡医保整合对农村居民医疗服务利用的影响-以广州市为例[J].中国公共政策评论, 2017, 1:34-52. http://search.cnki.net/down/default.aspx?filename=ZGZP201701004&dbcode=CJFD&year=2017&dflag=pdfdown
  • 加载中
表(1)
计量
  • 文章访问数:  242
  • HTML全文浏览量:  64
  • PDF下载量:  251
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-02-07
  • 刊出日期:  2018-03-30

目录

    /

    返回文章
    返回

    【温馨提醒】近日,《协和医学杂志》编辑部接到作者反映,有多名不法人员冒充期刊编辑发送见刊通知,鼓动作者添加微信,从而骗取版面费的行为。特提醒您,本刊与作者联系的方式均为邮件通知或电话,稿件进度通知邮箱为:mjpumch@126.com,编辑部电话为:010-69154261,请提高警惕,谨防上当受骗!如有任何疑问,请致电编辑部核实。谢谢!