Program for Clinical Research, Department of Dermatology, University of California, San Francisco School of Medicine, San Francisco, CA, U.S.A.
Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Br J Dermatol. 2018 Jun;178(6):1280-1287. doi: 10.1111/bjd.16340. Epub 2018 Apr 25.
Routinely collected electronic health data obtained for administrative and clinical purposes are increasingly used to study atopic dermatitis (AD). Methods for identifying AD patients in routinely collected electronic health data differ, and it is unknown how this might affect study results.
To evaluate how patients with AD have been identified in studies using routinely collected electronic health data, to determine whether these methods were validated and to estimate how the method for identifying patients with AD affected variability in prevalence estimates.
We systematically searched PubMed, Embase and Web of Science for studies using routinely collected electronic health data that reported on AD as a primary outcome. Studies of localized AD and other types of dermatitis were excluded. The protocol for this review was registered in PROSPERO (CRD42016037968).
In total, 59 studies met eligibility criteria. Medical diagnosis codes for inclusion and exclusion, number of occasions of a code, type of provider associated with a code and prescription data were used to identify patients with AD. Only two studies described validation of their methods and no study reported on disease severity. Prevalence estimates ranged from 0·18% to 38·33% (median 4·91%) and up to threefold variation in prevalence was introduced by differences in the method for identifying patients with AD.
This systematic review highlights the need for clear reporting of methods for identifying patients with AD in routinely collected electronic health data to allow for meaningful interpretation and comparison of results.
为管理和临床目的而常规收集的电子健康数据越来越多地用于研究特应性皮炎(AD)。在常规收集的电子健康数据中识别 AD 患者的方法不同,尚不清楚这可能如何影响研究结果。
评估使用常规收集的电子健康数据进行的研究中如何识别 AD 患者,确定这些方法是否经过验证,并估计识别 AD 患者的方法如何影响患病率估计值的变异性。
我们系统地检索了 PubMed、Embase 和 Web of Science,以查找报告 AD 为主要结局的使用常规收集的电子健康数据的研究。排除局部 AD 和其他类型的皮炎的研究。本综述的方案已在 PROSPERO(CRD42016037968)中注册。
共有 59 项研究符合纳入标准。纳入和排除的医学诊断代码、代码出现的次数、与代码相关的提供者类型和处方数据用于识别 AD 患者。只有两项研究描述了其方法的验证,没有研究报告疾病严重程度。患病率估计值范围为 0.18%至 38.33%(中位数为 4.91%),由于识别 AD 患者的方法存在差异,患病率的变化高达三倍。
本系统评价强调需要明确报告在常规收集的电子健康数据中识别 AD 患者的方法,以便对结果进行有意义的解释和比较。