Mamtani Ronac, Haynes Kevin, Finkelman Brian S, Scott Frank I, Lewis James D
Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA.
Pharmacoepidemiol Drug Saf. 2014 Feb;23(2):111-8. doi: 10.1002/pds.3557. Epub 2013 Dec 19.
To develop a method to identify incident diabetes mellitus (DM) using an electronic medical records (EMR) database and test this classification by comparing incident and prevalent DM with common outcomes related to DM duration.
Incidence rates (IRs) of DM (defined as a first diagnosis or prescription) were measured in 3-month intervals through 36 months after registration in The Health Improvement Network, a primary care database, from 1994 to 2012. We used Joinpoint regression to identify the point where a statistically significant change in the trend of IRs occurred. Further analyses used this point to distinguish those likely to have incident (n = 50 315) versus prevalent (n = 28 337) DM. Incident and prevalent cohorts were compared using Cox regression for all-cause mortality, cardiovascular disease (CVD), diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy. Analyses were adjusted for age, sex, smoking, obesity, hyperlipidemia, hypertension, and calendar year.
Trends in DM IRs plateaued 9 months after registration (p = 0.04). All cause-mortality was increased (hazard ratio (HR) 1.62, 95% CI 1.53-1.70) among patients diagnosed with DM prior to 9 months following registration (prevalent DM) compared to those diagnosed after 9 months (incident DM). Similarly, the risk of DM-related complications was higher in prevalent versus incident DM patients [CVD, HR 2.24 (2.08-2.40); diabetic retinopathy, HR 1.31 (1.24-1.38); diabetic nephropathy, HR 2.30 (1.95-2.72); diabetic neuropathy, HR 1.28 (1.16-1.41)].
Joinpoint regression can be used to identify patients with newly diagnosed diabetes within EMR data. Failure to exclude patients with prevalent DM can lead to exaggerated associations of DM-related outcomes.
开发一种利用电子病历（EMR）数据库识别新发糖尿病（DM）的方法，并通过比较新发和现患DM与DM病程相关的常见结局来检验这种分类方法。
在1994年至2012年期间，在初级保健数据库“健康改善网络”中注册后的36个月内，以3个月为间隔测量DM的发病率（IRs，定义为首次诊断或处方）。我们使用Joinpoint回归来确定IRs趋势发生统计学显著变化的点。进一步的分析利用这一点来区分可能患有新发（n = 50315）与现患（n = 28337）DM的患者。使用Cox回归比较新发和现患队列在全因死亡率、心血管疾病（CVD）、糖尿病视网膜病变、糖尿病肾病和糖尿病神经病变方面的情况。分析对年龄、性别、吸烟、肥胖、高脂血症\、高血压和日历年份进行了调整。
DM的IRs趋势在注册后9个月趋于平稳（p = 0.04）。与注册后9个月诊断的患者（新发DM）相比，注册后9个月内诊断为DM的患者（现患DM）的全因死亡率增加（风险比（HR）1.62，95%可信区间1.53 - 1.70）。同样，现患DM患者与新发DM患者相比，DM相关并发症的风险更高[CVD，HR 2.24（2.08 - 2.40）；糖尿病视网膜病变，HR 1.31（1.24 - 1.38）；糖尿病肾病，HR 2.30（1.95 - 2.72）；糖尿病神经病变，HR 1.28（1.16 - 1.41）]。
Joinpoint回归可用于在EMR数据中识别新诊断的糖尿病患者。未能排除现患DM患者可能会导致DM相关结局的关联被夸大。
