Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases

Journal of the American Medical Informatics Association

Volumn 17, Issue 6, 2010, Pages 652-662

  Reisinger, Stephanie J ^a   Ryan, Patrick B ^b   O'Hara, Donald J ^a   Powel, Gregory E ^b   Painter, Jeffery L ^b   Pattishall, Edward N ^b   Morris, Jonathan A ^a  

^a ProSanos Corporation   (United States)

^b GLAXOSMITHKLINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACETYLSALICYLIC ACID; AMLODIPINE; AMOXICILLIN; ATENOLOL; ATORVASTATIN; CLOPIDOGREL; CYCLOBENZAPRINE; DEXTROPROPOXYPHENE; DIGOXIN; FEXOFENADINE; FLUTICASONE; FUROSEMIDE; GLYCERYL TRINITRATE; HYDROCHLOROTHIAZIDE; HYDROCODONE; LISINOPRIL; METFORMIN; METOPROLOL; OMEPRAZOLE; PARACETAMOL; POTASSIUM CHLORIDE; PREDNISONE; RAMIPRIL; ROFECOXIB; SIMVASTATIN;

ACUTE HEART INFARCTION; ADOLESCENT; ADULT; AGED; ARTICLE; CASE STUDY; CHILD; CONTROLLED STUDY; DATA BASE; DEMOGRAPHY; DRUG EXPOSURE; DRUG SAFETY; DRUG SURVEILLANCE PROGRAM; DRUG USE; ELECTRONIC MEDICAL RECORD; FAMILY HISTORY; HEALTH CARE; HUMAN; INFANT; MAJOR CLINICAL STUDY; NOMENCLATURE; OBSERVATIONAL METHOD; PHARMACY; PRESCHOOL CHILD; PRESCRIPTION; SCHOOL CHILD; VALIDATION STUDY;

ADOLESCENT; ADULT; AGED; CHILD; CYCLOOXYGENASE 2 INHIBITORS; DATA MINING; DRUG INFORMATION SERVICES; FEMALE; HUMANS; INFORMATION SYSTEMS; LACTONES; MALE; MIDDLE AGED; MODELS, THEORETICAL; MYOCARDIAL INFARCTION; PRODUCT SURVEILLANCE, POSTMARKETING; REPRODUCIBILITY OF RESULTS; SULFONES; SYSTEMS INTEGRATION; UNITED STATES; VOCABULARY, CONTROLLED;

EID: 78650457394     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/jamia.2009.002477     Document Type: Article

References (53)

1. Anon. Managing the Risks From Medical Product Use: Creating a Risk Management Framework. Rockville, MD: US Department of Health and Human Services, Food and Drug Administration, 1999. Contract No.: Document Number.
2. Almenoff J, Tonning JM, Gould AL, et al. Perspectives on the use of data mining in pharmacovigilance. Drug Saf 2005;28:981-1007.
3. Baciu A, Stratton K, Burke S. The Future of Drug Safety: Promoting and Protecting the Health of the Public. Institute of Medicine, 2006. Contract No.: Document Number.
4. FDA. The future of drug safety-promoting and protecting the health of the public, FDA's Response to the Institute of medicine's 2006 report, 2007. http://www.fda.gov/downloads/Drugs/DrugSafety/ PostmarketDrugSafetyInformationforPatientsandProviders/UCM171627.pdf (accessed 28 Nov 2009).
5. Furberg CD, Levin AA, Gross PA, et al. The FDA and drug safety: a proposal for sweeping changes. Arch Intern Med 2006;166:1938-42.
6. Goldman SA. Limitations and strengths of spontaneous reports data. Clin Ther 1998;20(Suppl C):C40-4.
7. Couzin J. Drug safety. Gaps in the safety net. Science 2005;307:196-8.
8. Psaty BM, Furberg CD. COX-2 inhibitors-lessons in drug safety. N Engl J Med 2005;352:1133-5.
9. Public Law 110-85: Food and Drug Administration Amendments Act of 2007, 2007.
10. FDA. The sentinel initiative: a national strategy for monitoring medical product safety, 2008. http://www.fda.gov/Safety/FDAsSentinelInitiative/ucm089474.htm (accessed May 2008).
11. Observational medical outcomes partnership, 2009. http://omop.fnih.org (accessed 28 Nov 2009).
12. EU-ADR. 2009. http://www.alert-project.org/ (accessed 28 Nov 2009).
13. IMI-PROTECT. 2009. http://www.imi-protect.eu/ (accessed 28 Nov 2009).
14. Strom B. Pharmacoepidemiology. 4th edn. Chichester, UK: Wiley, 2005.
15. Hartzema AG, Tilson HH, Chan KA. Pharmacoepidemiology and Therapeutic Risk Management. Cincinnati, OH: Harvey Whitney Books, 2008.
16. FDA. Merck Withdraws Vioxx; FDA Issues Public Health Advisory. http://www.fda. gov/fdac/features/2004/604_vioxx.html (accessed 26 Apr 2009).
17. Juni P, Nartey L, Reichenbach S, et al. Risk of cardiovascular events and rofecoxib: cumulative meta-analysis. Lancet 2004;364:2021-9.
18. Velentgas P, West W, Cannuscio CC, et al. Cardiovascular risk of selective cyclooxygenase-2 inhibitors and other non-aspirin non-steroidal anti-inflammatory medications. Pharmacoepidemiol Drug Saf 2006;15:641-52.
19. Abraham NS, El-Serag HB, Hartman C, et al. Cyclooxygenase-2 selectivity of non-steroidal anti-inflammatory drugs and the risk of myocardial infarction and cerebrovascular accident. Aliment Pharmacol Ther 2007;25:913-24.
20. Chen LC, Ashcroft DM. Risk of myocardial infarction associated with selective COX-2 inhibitors: meta-analysis of randomised controlled trials. Pharmacoepidemiol Drug Saf 2007;16:762-72.
21. Scott PA, Kingsley GH, Smith CM, et al. Non-steroidal anti-inflammatory drugs and myocardial infarctions: comparative systematic review of evidence from observational studies and randomised controlled trials. Ann Rheum Dis 2007;66:1296-304.
22. Psaty BM, Furberg CD. The record on rosiglitazone and the risk of myocardial infarction. N Engl J Med 2007;357:67-9.
23. Avorn J. Evaluating drug effects in the post-Vioxx world: there must be a better way. Circulation 2006;113:2173-6.
24. Ray A. Beyond debacle and debate: developing solutions in drug safety. Nat Rev Drug Discov 2009;8:775-9.
25. Arellano FM, Yood MU, Wentworth CE, et al. Use of cyclo-oxygenase 2 inhibitors (COX-2) and prescription non-steroidal anti-inflammatory drugs (NSAIDS) in UK and USA populations. Implications for COX-2 cardiovascular profile. Pharmacoepidemiol Drug Saf 2006;15:861-72.
26. Garcia Rodriguez LA, Varas-Lorenzo C, Maguire A, et al. Nonsteroidal antiinflammatory drugs and the risk of myocardial infarction in the general population. Circulation 2004;109:3000-6.
27. Garcia Rodriguez LA, Gonzalez-Perez A. Long-term use of non-steroidal anti-inflammatory drugs and the risk of myocardial infarction in the general population. BMC Med 2005;3:17.
28. Mamdani M, Rochon P, Juurlink DN, et al. Effect of selective cyclooxygenase 2 inhibitors and naproxen on short-term risk of acute myocardial infarction in the elderly. Arch Intern Med 2003;163:481-6.
29. Moore RA, Derry S, McQuay HJ. Cyclo-oxygenase-2 selective inhibitors and nonsteroidal anti-inflammatory drugs: balancing gastrointestinal and cardiovascular risk. BMC Musculoskelet Disord 2007;8:73.
30. Ray WA, Stein CM, Hall K, et al. Non-steroidal anti-inflammatory drugs and risk of serious coronary heart disease: an observational cohort study. Lancet 2002;359:118-23.
31. Ray WA, Stein CM, Daugherty JR, et al. COX-2 selective non-steroidal antiinflammatory drugs and risk of serious coronary heart disease. Lancet 2002;360:1071-3.
32. Schmidt H, Woodcock BG, Geisslinger G. Benefit-risk assessment of rofecoxib in the treatment of osteoarthritis. Drug Saf 2004;27:185-96.
33. Solomon DH, Schneeweiss S, Glynn RJ, et al. Relationship between selective cyclooxygenase-2 inhibitors and acute myocardial infarction in older adults. Circulation 2004;109:2068-73.
34. Solomon DH, Glynn RJ, Levin R, et al. Nonsteroidal anti-inflammatory drug use and acute myocardial infarction. Arch Intern Med 2002;162:1099-104.
35. van Staa TP, Smeeth L, Persson I, et al. What is the harm-benefit ratio of Cox-2 inhibitors? Int J Epidemiol 2008;37:405-13.
36. Hernandez-Diaz S, Varas-Lorenzo C, Garcia Rodriguez LA. Non-steroidal antiinflammatory drugs and the risk of acute myocardial infarction. Basic Clin Pharmacol Toxicol 2006;98:266-74.
37. Brown J, Lane K, Moore K, et al. Database Models to Implement the FDA Sentinel Initiative FDA, 2009. http://www.regulations.gov/search/Regs/home. html#documentDetail?R=090000648098c282 (accessed 21 Sep 2010).
38. Ryan PB, Griffin D, Whittenburg L, et al. Points to consider in developing a common semantic data model and terminology dictionary for observational analyses. Observational Medical Outcomes Partnership, 2009. http://omop.fnih.org/ CDMandTerminologies (accessed 28 Nov 2009).
39. Ryan PB, Griffin D, Reich C, et al. OMOP common data model (CDM) Specifications, 2009. http://omop.fnih.org/CDMandTerminologies (accessed 28 Nov 2009).
40. Reich C. OMOP standard vocabulary specifications, 2009. http://omop.fnih.org/CDMandTerminologies (accessed 28 Nov 2009).
41. National Library of Medicine. Unified Medical Language System (UMLS), 2009. http://www.nlm.nih.gov/research/umls/ (accessed 28 Nov 2009).
42. Ryan PB, Painter JL, Merrill GH. Defining Medical Conditions by Mapping ICD-9 to MedDRA: A Systematic Approach to Integrating Disparate Observational Data Sources for Enabling Enhanced Pharmacovigilance Analyses. Boston, MA, USA: Drug Information Association, 2008.
43. Merrill GH, Ryan PB, Painter JL. Using SNOMED to Normalize and Aggregate Drug References in the SafetyWorks Observational Pharmacovigilance Project. Phoenix, AZ, USA: KR-MED, 2008.
44. Postmarketing Safety Reports for Human Drug and Biological Products; Electronic Submission Requirements. Proposed Rule. Federal Register 74 (21 August 2009), 42184-220.
45. Ryan PB, Powell GE, Patishall EN, et al. Performance of Screening Multiple Observational Databases for Active Drug Safety Surveillance. Providence, RI, USA: International Society of Pharmacoepidemiology, 2009.
46. Powell G, Ryan PB, Patishall E. Comparison of Quantative Signal Detection Using Observational and Spontaneous Adverse Event Data. Brighton, UK: International Society of Pharmacoepidemiology, 2010:49.
47. Beach K, Le HV, Powell G, et al. Performance of a Semi-Automated Process for Estimation of Risk using Observational Databases, 2. Providence, RI, USA: International Society of Pharmacoepidemiology, 2009.
48. Mera R, Beach KJ, Powell G, et al. Semi-automated risk estimation using large databases: quinolones and clostridium difficile associated diarrhea. Pharmacoepidemiol Drug Saf 2010;19:610-17.
49. Brown JS, Kulldorff M, Petronis KR, et al. Early adverse drug event signal detection within population-based health networks using sequential methods: key methodologic considerations. Pharmacoepidemiol Drug Saf 2009;18:226-34.
50. Brown JS, Kulldorff M, Chan KA, et al. Early detection of adverse drug events within population-based health networks: application of sequential testing methods. Pharmacoepidemiol Drug Saf 2007;16:1275-84.
51. Norén G, Bate A, Hopstadius J, et al. Temporal pattern discovery for trends and transient effects: its application to patient records. Paper presented at: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, Nevada, USA, 2008.
52. Curtis JR, Cheng H, Delzell E, et al. Adaptation of Bayesian data mining algorithms to longitudinal claims data: coxib safety as an example. Med Care 2008;46:969-75.
53. Ryan PB, Welebob E, Hartzema AG, et al. Surveying US observational data sources and characteristics for drug safety needs. Pharmaceut Med 2010;24:1-8.