Using pooled electronic health records data to conduct pharmacoepidemiology safety studies : Challenges and lessons learned
Abstract Purpose We assessed the suitability of pooled electronic health record (EHR) data from clinical research networks (CRNs) of the patient‐centered outcomes research network to conduct studies of the association between tumor necrosis factor inhibitors (TNFi) and infections. Methods EHR data from patients with one of seven autoimmune diseases were obtained from three CRNs and pooled. Person‐level linkage of CRN data and Centers for Medicare and Medicaid Services (CMS) fee‐for‐service claims data was performed where possible. Using filled prescriptions from CMS claims data as the gold standard, we assessed the misclassification of EHR‐based new (incident) user definitions. Among new users of TNFi, we assessed subsequent rates of hospitalized infection in EHR and CMS data. Results The study included 45 483 new users of TNFi, of whom 1416 were successfully linked to their CMS claims. Overall, 44% of new EHR TNFi prescriptions were not associated with medication claims. Our most specific new user definition had a misclassification rate of 3.5%–16.4% for prevalent use, depending on the medication. Greater than 80% of CRN prescriptions had either zero refills or missing refill data. Compared to using EHR data alone, there was a 2‐ to 8‐fold increase in hospitalized infection rates when CMS claims data were added to the analysis. Conclusions EHR data substantially misclassified TNFi exposure and underestimated the incidence of hospitalized infections compared to claims data. EHR‐based new user definitions were reasonably accurate. Overall, using CRN data for pharmacoepidemiology studies is challenging, especially for biologics, and would benefit from supplementation by other sources..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:32 |
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| Enthalten in: |
Pharmacoepidemiology and Drug Safety - 32(2023), 9, Seite 969-977 |
| Beteiligte Personen: |
Beukelman, Timothy [VerfasserIn] |
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| Anmerkungen: |
© 2023 John Wiley & Sons, Ltd. |
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| Umfang: |
9 |
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| doi: |
10.1002/pds.5627 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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WLY01657530X |
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| 520 | |a Abstract Purpose We assessed the suitability of pooled electronic health record (EHR) data from clinical research networks (CRNs) of the patient‐centered outcomes research network to conduct studies of the association between tumor necrosis factor inhibitors (TNFi) and infections. Methods EHR data from patients with one of seven autoimmune diseases were obtained from three CRNs and pooled. Person‐level linkage of CRN data and Centers for Medicare and Medicaid Services (CMS) fee‐for‐service claims data was performed where possible. Using filled prescriptions from CMS claims data as the gold standard, we assessed the misclassification of EHR‐based new (incident) user definitions. Among new users of TNFi, we assessed subsequent rates of hospitalized infection in EHR and CMS data. Results The study included 45 483 new users of TNFi, of whom 1416 were successfully linked to their CMS claims. Overall, 44% of new EHR TNFi prescriptions were not associated with medication claims. Our most specific new user definition had a misclassification rate of 3.5%–16.4% for prevalent use, depending on the medication. Greater than 80% of CRN prescriptions had either zero refills or missing refill data. Compared to using EHR data alone, there was a 2‐ to 8‐fold increase in hospitalized infection rates when CMS claims data were added to the analysis. Conclusions EHR data substantially misclassified TNFi exposure and underestimated the incidence of hospitalized infections compared to claims data. EHR‐based new user definitions were reasonably accurate. Overall, using CRN data for pharmacoepidemiology studies is challenging, especially for biologics, and would benefit from supplementation by other sources. | ||
| 700 | 1 | |a Chen, Lang |4 aut | |
| 700 | 1 | |a Annapureddy, Narender |4 aut | |
| 700 | 1 | |a Oates, Jim |4 aut | |
| 700 | 1 | |a Clowse, Megan E. B. |4 aut | |
| 700 | 1 | |a Long, Millie |4 aut | |
| 700 | 1 | |a Kappelman, Michael D. |4 aut | |
| 700 | 1 | |a Rhee, Rennie L. |4 aut | |
| 700 | 1 | |a Merkel, Peter A. |4 aut | |
| 700 | 1 | |a Nowell, William Benjamin |4 aut | |
| 700 | 1 | |a Xie, Fenglong |4 aut | |
| 700 | 1 | |a Clinton, Cassie |4 aut | |
| 700 | 1 | |a Curtis, Jeffrey R. |4 aut | |
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