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Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research

  • Seng Chan You (Department of Biomedical Systems Informatics, Yonsei University College of Medicine) ;
  • Seongwon Lee (Department of Biomedical Informatics, Ajou University School of Medicine) ;
  • Byungjin Choi (Department of Biomedical Informatics, Ajou University School of Medicine) ;
  • Rae Woong Park (Department of Biomedical Informatics, Ajou University School of Medicine)
  • Received : 2022.11.01
  • Accepted : 2022.11.10
  • Published : 2022.12.01

Abstract

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

Keywords

Acknowledgement

This work was supported by the Bio Industrial Strategic Technology Development Program (20003883, 20005021) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea), and grants from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR16C0001, HG22C0024).

References

  1. Stang PE, Ryan PB, Racoosin JA, et al. Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership. Ann Intern Med 2010;153:600-6. https://doi.org/10.7326/0003-4819-153-9-201011020-00010
  2. Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc 2012;19:54-60. https://doi.org/10.1136/amiajnl-2011-000376
  3. Hripcsak G, Duke JD, Shah NH, et al. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform 2015;216:574-8.
  4. Hripcsak G. OHDSI 2022 state of the community [Internet]. [place unknown]: Observational Health Data Sciences and Informatics; 2022 [cited 2022 October 23]. Available from: https://www.ohdsi.org/wp-content/uploads/2022/10/OHDSI2022-state-of-community-Hripcsak-FDA-Titans.pdf.
  5. Haendel MA, Chute CG, Robinson PN. Classification, ontology, and precision medicine. N Engl J Med 2018;379:1452-62. https://doi.org/10.1056/NEJMra1615014
  6. Seong Y, You SC, Ostropolets A, et al. Incorporation of Korean electronic data interchange vocabulary into observational medical outcomes partnership vocabulary. Healthc Inform Res 2021;27:29-38. https://doi.org/10.4258/hir.2021.27.1.29
  7. Yoon D, Ahn EK, Park MY, et al. Conversion and data quality assessment of electronic health record data at a Korean tertiary teaching hospital to a common data model for distributed network research. Healthc Inform Res 2016;22:54-8. https://doi.org/10.4258/hir.2016.22.1.54
  8. Blacketer C, Defalco FJ, Ryan PB, Rijnbeek PR. Increasing trust in real-world evidence through evaluation of observational data quality. J Am Med Inform Assoc 2021;28:2251-7. https://doi.org/10.1093/jamia/ocab132
  9. Hripcsak G, Ryan PB, Duke JD, et al. Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci U S A 2016;113:7329-36. https://doi.org/10.1073/pnas.1510502113
  10. Kostka K, Duarte-Salles T, Prats-Uribe A, et al. Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS. Clin Epidemiol 2022;14:369-84. https://doi.org/10.2147/CLEP.S323292
  11. Brat GA, Weber GM, Gehlenborg N, et al. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ Digit Med 2020;3:109.
  12. You SC, Jung S, Swerdel JN, et al. Comparison of first-line dual combination treatments in hypertension: real-world evidence from multinational heterogeneous cohorts. Korean Circ J 2020;50:52-68. https://doi.org/10.4070/kcj.2019.0173
  13. Suchard MA, Schuemie MJ, Krumholz HM, et al. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis. Lancet 2019;394:1816-26. https://doi.org/10.1016/S0140-6736(19)32317-7
  14. Schuemie MJ, Ryan PB, Pratt N, et al. Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND). J Am Med Inform Assoc 2020;27:1331-7. https://doi.org/10.1093/jamia/ocaa103
  15. Chan You S, Krumholz HM, Suchard MA, et al. Comprehensive comparative effectiveness and safety of first-line β-blocker monotherapy in hypertensive patients: a large-scale multicenter observational study. Hypertension 2021;77:1528-38. https://doi.org/10.1161/HYPERTENSIONAHA.120.16402
  16. You SC, Rho Y, Bikdeli B, et al. Association of ticagrelor vs clopidogrel with net adverse clinical events in patients with acute coronary syndrome undergoing percutaneous coronary intervention. JAMA 2020;324:1640-50. https://doi.org/10.1001/jama.2020.16167
  17. Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J Am Med Inform Assoc 2018;25:969-75. https://doi.org/10.1093/jamia/ocy032
  18. Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ Digit Med 2020;3:119.
  19. Dayan I, Roth HR, Zhong A, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med 2021;27:1735-43. https://doi.org/10.1038/s41591-021-01506-3
  20. Mamidi TK, Tran-Nguyen TK, Melvin RL, Worthey EA. Development of an individualized risk prediction model for COVID-19 using electronic health record data. Front Big Data 2021;4:675882.
  21. Tong J, Luo C, Islam MN, et al. Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites. NPJ Digit Med 2022;5:76.
  22. Williams RD, Markus AF, Yang C, et al. Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network. BMC Med Res Methodol 2022;22:35.
  23. Nestsiarovich A, Reps JM, Matheny ME, et al. Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study. Transl Psychiatry 2021;11:642.
  24. Jung H, Yoo S, Kim S, et al. Patient-Level fall risk prediction using the observational medical outcomes partnership's common data model: pilot feasibility study. JMIR Med Inform 2022;10:e35104.
  25. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ 2010;340:c221.
  26. Cooper H, Patall EA. The relative benefits of meta-analysis conducted with individual participant data versus aggregated data. Psychol Methods 2009;14:165-76. https://doi.org/10.1037/a0015565
  27. Selmer R, Haglund B, Furu K, et al. Individual-based versus aggregate meta-analysis in multi-database studies of pregnancy outcomes: the Nordic example of selective serotonin reuptake inhibitors and venlafaxine in pregnancy. Pharmacoepidemiol Drug Saf 2016;25:1160-9. https://doi.org/10.1002/pds.4033
  28. La Gamba F, Corrao G, Romio S, et al. Combining evidence from multiple electronic health care databases: performances of one-stage and two-stage meta-analysis in matched case-control studies. Pharmacoepidemiol Drug Saf 2017;26:1213-9. https://doi.org/10.1002/pds.4280
  29. Schuemie MJ, Chen Y, Madigan D, Suchard MA. Combining cox regressions across a heterogeneous distributed research network facing small and zero counts. Stat Methods Med Res 2022;31:438-50. https://doi.org/10.1177/09622802211060518
  30. Marquis-Gravel G, Roe MT, Robertson HR, et al. Rationale and design of the Aspirin Dosing-A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness (ADAPTABLE) trial. JAMA Cardiol 2020;5:598-607.