Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine.
Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine.
复制标题
使用用于多元药物监视的汇总，去识别的电子健康记录数据：硫唑嘌呤的案例研究。
DOI：
10.1016/j.jbi.2013.10.009
复制
发表时间：
2014-12
影响因子：
4.5
通讯作者：
Kaelber, David C.
中科院分区：
文献类型：
--
作者：
Patel, Vishal N.;Kaelber, David C.
关键词：
To demonstrate the use of aggregated and de-identified electronic health record (EHR) data for multivariate post-marketing pharmacosurveillance in a case study of azathioprine (AZA). Using aggregated, standardized, normalized, and de-identified, population-level data from the Explore platform (Explorys, Inc.) we searched over 10 million individuals, of which 14,580 were prescribed AZA based on RxNorm drug orders. Based on logical observation identifiers names and codes (LOINC) and vital sign data, we examined the following side effects: anemia, cell lysis, fever, hepatotoxicity, hypertension, nephrotoxicity, neutropenia, and neutrophilia. Patients prescribed AZA were compared to patients prescribed one of 11 other anti-rheumatologic drugs to determine the relative risk of side effect pairs. Compared to AZA case report trends, hepatotoxicity (marked by elevated transaminases or elevated bilirubin) did not occur as an isolated event more frequently in patients prescribed AZA than other anti-rheumatic agents. While neutropenia occurred in 24% of patients (RR 1.15, 95% CI 1.07-1.23), neutrophilia was also frequent (45%) and increased in patients prescribed AZA (RR 1.28, 95% CI 1.22-1.34). After constructing a pairwise side effect network, neutropenia had no dependencies. A reduced risk of neutropenia was found in patients with co-existing elevations in total bilirubin or liver transaminases, supporting classic clinical knowledge that agranulocytosis is a largely unpredictable phenomenon. Rounding errors propagated in the statistically de-identified datasets for cohorts as small as 40 patients only contributed marginally to the calculated risk. Our work demonstrates that aggregated, standardized, normalized and de-identified population level EHR data can provide both sufficient insight and statistical power to detect potential patterns of medication side effect associations, serving as a multivariate and generalizable approach to post-marketing drug surveillance.
登录
查看更多内容
影响因子：
7.7
作者：
Hripcsak, George;Knirsch, Charles;Melton, Genevieve B.
通讯作者：
Melton, Genevieve B.
影响因子：
24.5
作者：
CONNELL, WR;KAMM, MA;LENNARDJONES, JE
通讯作者：
LENNARDJONES, JE
影响因子：
2.9
作者：
Kader, HA;Wenner, WJ;Baldassano, RN
通讯作者：
Baldassano, RN
影响因子：
9.9
作者：
Kuhn M;Campillos M;Letunic I;Jensen LJ;Bork P
通讯作者：
Bork P
影响因子：
3
作者：
Harpaz R;Chase HS;Friedman C
通讯作者：
Friedman C