Using pooled electronic health records data to conduct pharmacoepidemiology safety studies: Challenges and lessons learned

被引:3
|
作者
Beukelman, Timothy [1 ]
Chen, Lang [1 ]
Annapureddy, Narender [2 ]
Oates, Jim [3 ]
Clowse, Megan E. B. [4 ]
Long, Millie
Kappelman, Michael D. D. [5 ]
Rhee, Rennie L. L. [6 ]
Merkel, Peter A. A. [6 ,7 ]
Nowell, William Benjamin [8 ]
Xie, Fenglong [1 ]
Clinton, Cassie [1 ]
Curtis, Jeffrey R. R. [1 ]
机构
[1] Univ Alabama Birmingham, Div Clin Immunol & Rheumatol, Shelby 121H, 1825 Univ Blvd, Birmingham, AL 35233 USA
[2] Vanderbilt Univ, Div Rheumatol & Immunol, Med Ctr, Nashville, TN USA
[3] Med Univ South Carolina, Div Rheumatol, Charleston, SC USA
[4] Duke Univ, Div Rheumatol & Immunol, Durham, NC USA
[5] Univ N Carolina, Div Gastroenterol & Hepatol, Chapel Hill, NC USA
[6] Univ Penn, Dept Med, Div Rheumatol, Philadelphia, PA USA
[7] Univ Penn, Dept Biostat Epidemiol & Informat, Div Epidemiol, Philadelphia, PA USA
[8] Global Hlth Living Fdn, New York, NY USA
关键词
biological therapy; electronic health records; infections; pharmacoepidemiology; RHEUMATOID-ARTHRITIS; SERIOUS INFECTION; RISK;
D O I
10.1002/pds.5627
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
引用
收藏
页码:969 / 977
页数:9
相关论文
共 50 条
  • [41] Challenges in Using Real-world Clinical Practice Records for Validation of Clinical Trial Data in Inflammatory Bowel Disease: Lessons Learned
    Afzali, Anita
    Ciorba, Matthew A.
    Schwartz, David A.
    Sharaf, Mai
    Fourment, Chris
    Ritter, Timothy
    Wolf, Douglas C.
    Shafran, Ira
    Randall, Charles W.
    Kane, Sunanda V.
    INFLAMMATORY BOWEL DISEASES, 2018, 24 (01) : 2 - 4
  • [42] Lessons learned from a pay-for-performance scheme for appropriate prescribing using electronic health records from general practices in the Netherlands
    Arslan, I. G.
    Verheij, R. A.
    Hek, K.
    Ramerman, L.
    HEALTH POLICY, 2024, 149
  • [43] Public Health Surveillance in the Dialysis Setting: Opportunities and Challenges for Using Electronic Health Records
    Wise, Matthew E.
    Lovell, Chris
    SEMINARS IN DIALYSIS, 2013, 26 (04) : 399 - 406
  • [44] Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data
    Valik, John Karlsson
    Ward, Logan
    Tanushi, Hideyuki
    Johansson, Anders F.
    Farnert, Anna
    Mogensen, Mads Lause
    Pickering, Brian W.
    Herasevich, Vitaly
    Dalianis, Hercules
    Henriksson, Aron
    Naucler, Pontus
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [45] Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data
    John Karlsson Valik
    Logan Ward
    Hideyuki Tanushi
    Anders F. Johansson
    Anna Färnert
    Mads Lause Mogensen
    Brian W. Pickering
    Vitaly Herasevich
    Hercules Dalianis
    Aron Henriksson
    Pontus Nauclér
    Scientific Reports, 13
  • [46] Procurement of patient medical records from multiple health care facilities for public health research: feasibility, challenges, and lessons learned
    McMahon, James M.
    Brasch, Judith
    Podsiadly, Eric
    Torres, Leilani
    Quiles, Robert
    Ramos, Evette
    Crean, Hugh F.
    Haberer, Jessica E.
    JAMIA OPEN, 2023, 6 (02)
  • [47] Electronic health records as a tool for recruitment of participants' clinical effectiveness research: lessons learned from tobacco cessation
    Fraser, David
    Christiansen, Bruce A.
    Adsit, Robert
    Baker, Timothy B.
    Fiore, Michael C.
    TRANSLATIONAL BEHAVIORAL MEDICINE, 2013, 3 (03) : 244 - 252
  • [48] Comparing Approaches to Measuring the Adoption and Usability of Electronic Health Records: Lessons Learned from Canada, Denmark and Finland
    Kushniruk, Andre
    Kaipio, Johanna
    Nieminen, Marko
    Nohr, Christian
    Borycki, Elizabeth
    MEDINFO 2013: PROCEEDINGS OF THE 14TH WORLD CONGRESS ON MEDICAL AND HEALTH INFORMATICS, PTS 1 AND 2, 2013, 192 : 367 - 371
  • [49] Using Electronic Health Records and Data Warehouse Collaboratively in Community Health Centers
    Zeng, Xiaoming
    Forrestal, Elizabeth J.
    Cellucci, Leigh W.
    Kennedy, Michael H.
    Smith, Doug
    JOURNAL OF CASES ON INFORMATION TECHNOLOGY, 2013, 15 (04) : 45 - 62
  • [50] Challenges Encountered and Lessons Learned when Using a Novel Anonymised Linked Dataset of Health and Social Care Records for Public Health Intelligence: The Sussex Integrated Dataset
    Ford, Elizabeth
    Tyler, Richard
    Johnston, Natalie
    Spencer-Hughes, Vicki
    Evans, Graham
    Elsom, Jon
    Madzvamuse, Anotida
    Clay, Jacqueline
    Gilchrist, Kate
    Rees-Roberts, Melanie
    INFORMATION, 2023, 14 (02)