Rowan Christopher G, Flory James, Gerhard Tobias, Cuddeback John K, Stempniewicz Nikita, Lewis James D, Hennessy Sean
Collaborative Healthcare Research and Data Analytics (COHRDATA), Santa Monica, CA, USA.
Department of Health Policy and Research, Weill Cornell Medical College, New York, NY, USA.
Pharmacoepidemiol Drug Saf. 2017 Aug;26(8):963-972. doi: 10.1002/pds.4234. Epub 2017 Jun 12.
Granular clinical and laboratory data available in electronic health record (EHR) databases provide researchers the opportunity to conduct investigations that would not be possible in insurance claims databases; however, for pharmacoepidemiology studies, accurate classification of medication exposure is critical.
The aim of this study was to evaluate the validity of classifying medication exposure using EHR prescribing (EHR-Rx) data.
We conducted a retrospective cohort study among patients with linked claims and EHR data in OptumLabs™ Data Warehouse. The agreement between EHR-Rx data and pharmacy claims (PC-Rx) data (for 40 medications) was determined using the positive predictive value (PPV) and medication possession ratio (MPR)-calculated in 1- and 12-month medication exposure periods (MEPs). Secondary analyses were restricted to incident vs prevalent EHR-Rxs, age ≥65 vs <65, white vs black race, males vs females, and number of EHR-Rxs.
The validity metrics varied substantially among the 40 medications assessed. Across all medications, the period PPV and MPR were 62% and 63% in the 1-month MEP. They were 78% and 43% in the 12-month MEP. Overall, PPV and MPR were higher for patients with a prevalent EHR-Rx and age <65.
Despite substantial variability among different medications, there was very good agreement between EHR-Rx data and PC-Rx data. To maximize the validity of classifying medication exposure with EHR prescribing data, researchers may consider using longer MEPs (eg, 12 months) and potentially require multiple EHR-Rxs to classify baseline medication exposure.
电子健康记录（EHR）数据库中可用的详细临床和实验室数据为研究人员提供了在保险理赔数据库中无法开展调查的机会；然而，对于药物流行病学研究而言，准确分类药物暴露情况至关重要。
本研究旨在评估使用EHR处方（EHR-Rx）数据对药物暴露进行分类的有效性。
我们在OptumLabs™数据仓库中对具有关联理赔和EHR数据的患者开展了一项回顾性队列研究。使用在1个月和12个月药物暴露期（MEP）计算的阳性预测值（PPV）和药物持有率（MPR）来确定EHR-Rx数据与药房理赔（PC-Rx）数据（针对40种药物）之间的一致性。二次分析仅限于新发与现患EHR-Rx、年龄≥65岁与<65岁、白种人与黑种人、男性与女性以及EHR-Rx的数量。
在所评估的40种药物中，有效性指标差异很大。在所有药物中，1个月MEP的时段PPV和MPR分别为62%和63%。在12个月MEP中，它们分别为78%和43%。总体而言，现患EHR-Rx且年龄<65岁的患者PPV和MPR更高。
尽管不同药物之间存在很大差异，但EHR-Rx数据与PC-Rx数据之间仍具有很好的一致性。为了最大限度地提高使用EHR处方数据对药物暴露进行分类的有效性，研究人员可考虑使用更长的MEP（如12个月），并可能需要多个EHR-Rx来分类基线药物暴露情况。
