Hyunah Shin1, Suehyun Lee2,3. 1. Health Care Data Science Center, Konyang University Hospital, 158 Gwanjeo-dong-ro, Seo-gu, Daejeon, Republic of Korea. 2. Health Care Data Science Center, Konyang University Hospital, 158 Gwanjeo-dong-ro, Seo-gu, Daejeon, Republic of Korea. shleemedi@kyuh.ac.kr. 3. Departments of Biomedical Informatics, Konyang University College of Medicine, 158 Gwanjeo-dong-ro, Seo-gu, Daejeon, Republic of Korea. shleemedi@kyuh.ac.kr.
Abstract
BACKGROUND: Adverse drug reactions (ADRs) are regarded as a major cause of death and a major contributor to public health costs. For the active surveillance of drug safety, the use of real-world data and real-world evidence as part of the overall pharmacovigilance process is important. In this regard, many studies apply the data-driven approaches to support pharmacovigilance. We developed a pharmacovigilance data-processing pipeline (PDP) that utilized electronic health records (EHR) and spontaneous reporting system (SRS) data to explore pharmacovigilance signals. METHODS: To this end, we integrated two medical data sources: Konyang University Hospital (KYUH) EHR and the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). As part of the presented PDP, we converted EHR data on the Observation Medical Outcomes Partnership (OMOP) data model. To evaluate the ability of using the proposed PDP for pharmacovigilance purposes, we performed a statistical validation using drugs that induce ear disorders. RESULTS: To validate the presented PDP, we extracted six drugs from the EHR that were significantly involved in ADRs causing ear disorders: nortriptyline, (hazard ratio [HR] 8.06, 95% CI 2.41-26.91); metoclopramide (HR 3.35, 95% CI 3.01-3.74); doxycycline (HR 1.73, 95% CI 1.14-2.62); digoxin (HR 1.60, 95% CI 1.08-2.38); acetaminophen (HR 1.59, 95% CI 1.47-1.72); and sucralfate (HR 1.21, 95% CI 1.06-1.38). In FAERS, the strongest associations were found for nortriptyline (reporting odds ratio [ROR] 1.94, 95% CI 1.73-2.16), sucralfate (ROR 1.22, 95% CI 1.01-1.45), doxycycline (ROR 1.30, 95% CI 1.20-1.40), and hydroxyzine (ROR 1.17, 95% CI 1.06-1.29). We confirmed the results in a meta-analysis using random and fixed models for doxycycline, hydroxyzine, metoclopramide, nortriptyline, and sucralfate. CONCLUSIONS: The proposed PDP could support active surveillance and the strengthening of potential ADR signals via real-world data sources. In addition, the PDP was able to generate real-world evidence for drug safety.
BACKGROUND: Adverse drug reactions (ADRs) are regarded as a major cause of death and a major contributor to public health costs. For the active surveillance of drug safety, the use of real-world data and real-world evidence as part of the overall pharmacovigilance process is important. In this regard, many studies apply the data-driven approaches to support pharmacovigilance. We developed a pharmacovigilance data-processing pipeline (PDP) that utilized electronic health records (EHR) and spontaneous reporting system (SRS) data to explore pharmacovigilance signals. METHODS: To this end, we integrated two medical data sources: Konyang University Hospital (KYUH) EHR and the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). As part of the presented PDP, we converted EHR data on the Observation Medical Outcomes Partnership (OMOP) data model. To evaluate the ability of using the proposed PDP for pharmacovigilance purposes, we performed a statistical validation using drugs that induce ear disorders. RESULTS: To validate the presented PDP, we extracted six drugs from the EHR that were significantly involved in ADRs causing ear disorders: nortriptyline, (hazard ratio [HR] 8.06, 95% CI 2.41-26.91); metoclopramide (HR 3.35, 95% CI 3.01-3.74); doxycycline (HR 1.73, 95% CI 1.14-2.62); digoxin (HR 1.60, 95% CI 1.08-2.38); acetaminophen (HR 1.59, 95% CI 1.47-1.72); and sucralfate (HR 1.21, 95% CI 1.06-1.38). In FAERS, the strongest associations were found for nortriptyline (reporting odds ratio [ROR] 1.94, 95% CI 1.73-2.16), sucralfate (ROR 1.22, 95% CI 1.01-1.45), doxycycline (ROR 1.30, 95% CI 1.20-1.40), and hydroxyzine (ROR 1.17, 95% CI 1.06-1.29). We confirmed the results in a meta-analysis using random and fixed models for doxycycline, hydroxyzine, metoclopramide, nortriptyline, and sucralfate. CONCLUSIONS: The proposed PDP could support active surveillance and the strengthening of potential ADR signals via real-world data sources. In addition, the PDP was able to generate real-world evidence for drug safety.
Authors: J Marc Overhage; Patrick B Ryan; Christian G Reich; Abraham G Hartzema; Paul E Stang Journal: J Am Med Inform Assoc Date: 2011-10-28 Impact factor: 4.497
Authors: Rave Harpaz; Santiago Vilar; William Dumouchel; Hojjat Salmasian; Krystl Haerian; Nigam H Shah; Herbert S Chase; Carol Friedman Journal: J Am Med Inform Assoc Date: 2012-10-31 Impact factor: 4.497
Authors: Yihua Xu; Xiaofeng Zhou; Brandon T Suehs; Abraham G Hartzema; Michael G Kahn; Yola Moride; Brian C Sauer; Qing Liu; Keran Moll; Margaret K Pasquale; Vinit P Nair; Andrew Bate Journal: Drug Saf Date: 2015-08 Impact factor: 5.606