Risk of acute myocardial infarction with NSAIDs in real world use: Bayesian meta-analysis of individual patient data

BMJ (Online)

Volumn 357, Issue , 2017, Pages

  Bally, Michèle ^a,b   Dendukuri, Nandini ^c   Rich, Benjamin ^c   Nadeau, Lyne ^c   Helin Salmivaara, Arja ^d   Garbe, Edeltraut ^e   Brophy, James M ^b,c  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b MCGILL UNIVERSITY   (Canada)

^c MCGILL UNIVERSITY HEALTH CENTRE   (Canada)

^d HOSPITAL DISTRICT OF HELSINKI AND UUSIMAA   (Finland)

^e LEIBNIZ INSTITUTE FOR PREVENTION RESEARCH AND EPIDEMIOLOGY BIPS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACETYLSALICYLIC ACID; CELECOXIB; CLOPIDOGREL; CORTICOSTEROID; DICLOFENAC; IBUPROFEN; NAPROXEN; NONSTEROID ANTIINFLAMMATORY AGENT; ROFECOXIB;

ACUTE HEART INFARCTION; ARTICLE; BAYES THEOREM; CARDIOVASCULAR RISK; COMORBIDITY; DOSE RESPONSE; DRUG EXPOSURE; HUMAN; ONSET AGE; OUTCOME ASSESSMENT; POPULATION RESEARCH; PRIORITY JOURNAL; RISK ASSESSMENT; RISK FACTOR; SYSTEMATIC REVIEW; TREATMENT DURATION; TREATMENT WITHDRAWAL; CANADA; EUROPE; META ANALYSIS; MYOCARDIAL INFARCTION;

ANTI-INFLAMMATORY AGENTS, NON-STEROIDAL; BAYES THEOREM; CANADA; DOSE-RESPONSE RELATIONSHIP, DRUG; EUROPE; HUMANS; MYOCARDIAL INFARCTION;

EID: 85019258077     PISSN: 09598146     EISSN: 17561833     Source Type: Journal    
DOI: 10.1136/bmj.j1909     Document Type: Article

References (76)

1. Eichler HG, Abadie E, Breckenridge A, et al. Bridging the efficacy-effectiveness gap: A regulator's perspective on addressing variability of drug response. Nat Rev Drug Discov 2011;10:495-506. doi:10.1038/nrd3501.
2. Rothwell PM. Factors that can affect the external validity of randomised controlled trials. PLoS Clin Trials 2006;1:e9. doi:10.1371/journal.pctr.0010009.
3. Coxib and traditional NSAID Trialists' (CNT) CollaborationBhala N, Emberson J, Merhi A, et al. Vascular and upper gastrointestinal effects of non-steroidal anti-inflammatory drugs: Meta-analyses of individual participant data from randomised trials. Lancet 2013;382:769-79. doi:10.1016/S0140-6736(13)60900-9.
4. Trelle S, Reichenbach S, Wandel S, et al. Cardiovascular safety of non-steroidal anti-inflammatory drugs: Network meta-analysis. BMJ 2011;342:c7086. doi:10.1136/bmj.c7086.
5. Nissen SE, Yeomans ND, Solomon DH, et al. PRECISION Trial Investigators. Cardiovascular Safety of Celecoxib, Naproxen, or Ibuprofen for Arthritis. N Engl J Med 2016;375:2519-29. doi:10.1056/NEJMoa1611593.
6. Gore M, Sadosky A, Leslie D, Tai KS, Seleznick M. Patterns of therapy switching, augmentation, discontinuation after initiation of treatment with select medications in patients with osteoarthritis. Clin Ther 2011;33:1914-31. doi:10.1016/j.clinthera.2011.10.019.
7. Langman M, Kahler KH, Kong SX, et al. Drug switching patterns among patients taking non-steroidal anti-inflammatory drugs: A retrospective cohort study of a general practitioners database in the United Kingdom. Pharmacoepidemiol Drug Saf 2001;10:517-24. doi:10.1002/pds.653.
8. Sift R, van Staa TP, Abenhaim L, Ebner D. A study of the longitudinal utilization and switching-patterns of non-steroidal anti-inflammatory drugs using a pharmacy based approach. Pharmacoepidemiol Drug Saf 1997;6:263-8. doi:10.1002/(SICI)1099-1557(199707)6:4263::AID-PDS2863.0.CO;2-Z.
9. Helin-Salmivaara A, Virtanen A, Vesalainen R, et al. NSAID use and the risk of hospitalization for first myocardial infarction in the general population: A nationwide case-control study from Finland. Eur Heart J 2006;27:1657-63. doi:10.1093/eurheartj/ehl053.
10. Andersohn F, Suissa S, Garbe E. Use of first-A nd second-generation cyclooxygenase-2-selective nonsteroidal antiinflammatory drugs and risk of acute myocardial infarction. Circulation 2006;113:1950-7. doi:10.1161/CIRCULATIONAHA.105.602425.
11. Varas-Lorenzo C, Castellsague J, Stang MR, Perez-Gutthann S, Aguado J, Rodriguez LA. The use of selective cyclooxygenase-2 inhibitors and the risk of acute myocardial infarction in Saskatchewan, Canada. Pharmacoepidemiol Drug Saf 2009;18:1016-25. doi: 10.1002/pds.1815.
12. Herrett E, Thomas SL, Schoonen WM, Smeeth L, Hall AJ. Validation and validity of diagnoses in the General Practice Research Database: A systematic review. Br J Clin Pharmacol 2010;69:4-14. doi:10.1111/j.1365-2125.2009.03537.x.
13. Rawson NS, Downey W, Maxwell CJ, West R. 25 years of pharmacoepidemiologic innovation: The Saskatchewan health administrative databases. J Popul Ther Clin Pharmacol 2011;18:e245-9.
14. Tamblyn R, Lavoie G, Petrella L, Monette J. The use of prescription claims databases in pharmacoepidemiological research: The accuracy and comprehensiveness of the prescription claims database in Québec. J Clin Epidemiol 1995;48:999-1009. doi:10.1016/0895-4356(94)00234-H.
15. Wettermark B, Zoëga H, Furu K, et al. The Nordic prescription databases as a resource for pharmacoepidemiological research-a literature review. Pharmacoepidemiol Drug Saf 2013;22:691-9. doi:10.1002/pds.3457.
16. Hammad TA, McAdams MA, Feight A, Iyasu S, Dal Pan GJ. Determining the predictive value of Read/OXMIS codes to identify incident acute myocardial infarction in the General Practice Research Database. Pharmacoepidemiol Drug Saf 2008;17:1197-201. doi:10.1002/pds.1672.
17. Lambert L, Blais C, Hamel D, et al. Evaluation of care and surveillance of cardiovascular disease: Can we trust medico-administrative hospital dataCan J Cardiol 2012;28:162-8. doi:10.1016/j. cjca.2011.10.005.
18. Levy AR, Tamblyn RM, Fitchett D, McLeod PJ, Hanley JA. Coding accuracy of hospital discharge data for elderly survivors of myocardial infarction. Can J Cardiol 1999;15:1277-82.
19. Pajunen P, Koukkunen H, Ketonen M, et al. The validity of the Finnish Hospital Discharge Register and Causes of Death Register data on coronary heart disease. Eur J Cardiovasc Prev Rehabil 2005;12:132-7.
20. Varas-Lorenzo C, Castellsague J, Stang MR, Tomas L, Aguado J, Perez-Gutthann S. Positive predictive value of ICD-9 codes 410 and 411 in the identification of cases of acute coronary syndromes in the Saskatchewan Hospital automated database. Pharmacoepidemiol Drug Saf 2008;17:842-52. doi:10.1002/pds.1619.
21. Fortier I, Doiron D, Little J, et al. International Harmonization Initiative. Is rigorous retrospective harmonization possible Application of the DataSHaPER approach across 53 large studies. Int J Epidemiol 2011;40:1314-28. doi:10.1093/ije/dyr106.
22. Fortier I, Doiron D, Burton P, Raina P. Invited commentary: Consolidating data harmonization-how to obtain quality and applicabilityAm J Epidemiol 2011;174:261-4, author reply 265-6. doi:10.1093/aje/kwr194.
23. Coca SG, Yusuf B, Shlipak MG, Garg AX, Parikh CR. Long-term risk of mortality and other adverse outcomes after acute kidney injury: A systematic review and meta-analysis. Am J Kidney Dis 2009;53:961-73. doi:10.1053/j.ajkd.2008.11.034.
24. Hall AJ, Stubbs B, Mamas MA, et al. Association between osteoarthritis and cardiovascular disease: Systematic review and meta-analysis. Eur J Prev Cardiol 2016;23:938-46.
25. Han MK, McLaughlin VV, Criner GJ, Martinez FJ. Pulmonary diseases and the heart. Circulation 2007;116:2992-3005. doi:10.1161/CIRCULATIONAHA.106.685206.
26. Meisinger C, Döring A, Löwel H. KORA Study Group. Chronic kidney disease and risk of incident myocardial infarction and all-cause and cardiovascular disease mortality in middle-aged men and women from the general population. Eur Heart J 2006;27:1245-50. doi:10.1093/eurheartj/ehi880.
27. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999;10:37-48. doi:10.1097/00001648-199901000-00008.
28. Sauer BC, Brookhart MA, Roy J, VanderWeele T. A review of covariate selection for non-experimental comparative effectiveness research. Pharmacoepidemiol Drug Saf 2013;22:1139-45. doi:10.1002/pds.3506.
29. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000;11:550-60. doi:10.1097/00001648-200009000-00011.
30. Debray TP, Moons KG, Abo-Zaid GM, Koffijberg H, Riley RD. Individual participant data meta-analysis for a binary outcome: One-stage or two-stagePLoS One 2013;8:e60650. doi:10.1371/journal. pone.0060650.
31. Stewart GB, Altman DG, Askie LM, Duley L, Simmonds MC, Stewart LA. Statistical analysis of individual participant data meta-analyses: A comparison of methods and recommendations for practice. PLoS One 2012;7:e46042. doi:10.1371/journal.pone.0046042.
32. Daniels MJ. A prior for the variance in hierarchical models. Can J Stat 1999;27:567-78doi:10.2307/3316112.
33. Lambert PC, Sutton AJ, Burton PR, Abrams KR, Jones DR. How vague is vague A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med 2005;24:2401-28. doi:10.1002/sim.2112.
34. Spiegelhalter DJ, Abrams KR, Myles JP. Prior Distributions. Bayesian Approaches to Clinical Trials and Health-Care Evaluation.John Wiley & Sons, Ltd, 2004: 139-80doi:10.1002/0470092602.ch5.
35. Higgins JP, Whitehead A. Borrowing strength from external trials in a meta-analysis. Stat Med 1996;15:2733-49. doi:10.1002/(SICI)1097-0258(19961230)15:242733::AID-SIM5623.0.CO;2-0.
36. Varas-Lorenzo C, Riera-Guardia N, Calingaert B, et al. Myocardial infarction and individual nonsteroidal anti-inflammatory drugs meta-analysis of observational studies. Pharmacoepidemiol Drug Saf 2013;22:559-70. doi:10.1002/pds.3437.
37. Little RJA, Rubin DB. Statistical Analysis with Missing Data (2nd Edition). Part 1. Overview and basic approaches. Somerset NJ, USA. 2002.
38. Welton NJ, Sutton AJ, Cooper N, et al. Bayesian methods and WinBUGS. In: Evidence Synthesis for Decision Making in Healthcare.John Wiley & Sons Ltd, 2012: 17-42doi:10.1002/9781119942986.ch2.
39. Plummer M, Stukalov A. Package 'rjags'. Bayesian graphical models using MCMC. October 27, 2014. Available at: Https://cran.r-project. org/web/packages/rjags/rjags.pdf.
40. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 2014. Retrieved from http://www.R-project.org/.
41. Plummer M, Best N, Cowles K, et al. Package 'coda'. Output analysis and diagnostics for Markov Chain Monte Carlo simulations. July 2, 2014. Available at: Https://cran.r-project.org/web/packages/coda/coda.pdf.
42. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci 1992;7:457-511doi:10.1214/ss/1177011136.
43. Raftery AE, Lewis SM. One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Stat Sci 1992;7:493-7doi:10.1214/ss/1177011143.
44. Girvin B, Rafferty T, Stevenson MR, Johnston GD. Uptake of COX-2 selective inhibitors and influence on NSAID prescribing in Northern Ireland. Pharmacoepidemiol Drug Saf 2004;13:153-7. doi:10.1002/pds.866.
45. Helin-Salmivaara A, Huupponen R, Virtanen A, Klaukka T. Adoption of celecoxib and rofecoxib: A nationwide database study. J Clin Pharm Ther 2005;30:145-52. doi:10.1111/j.1365-2710.2005.00627.x.
46. Mamdani M, Rochon P, Laupacis A, Anderson G. Initial patterns of use of COX-2 inhibitors by elderly patients in Ontario: Findings and implications. CMAJ 2002;167:1125-6.
47. Jolani S, Debray TPA, Koffijberg H, van Buuren S, Moons KG. Imputation of systematically missing predictors in an individual participant data meta-analysis: A generalized approach using MICE. Stat Med 2015;34:1841-63. doi:10.1002/sim.6451.
48. Resche-Rigon M, White IR, Bartlett JW, Peters SA, Thompson SG. PROG-IMT Study Group. Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data. Stat Med 2013;32:4890-905. doi:10.1002/sim.5894.
49. Hernán MA, Robins JM. Using Big Data to emulate a target trial when a randomized trial is not available. Am J Epidemiol 2016;183:758-64. doi:10.1093/aje/kwv254.
50. Moride Y, Abenhaim L. Evidence of the depletion of susceptibles effect in non-experimental pharmacoepidemiologic research. J Clin Epidemiol 1994;47:731-7. doi:10.1016/0895-4356(94)90170-8.
51. Debray TP, Moons KG, van Valkenhoef G, et al. GetReal Methods Review Group. Get real in individual participant data (IPD) meta-analysis: A review of the methodology. Res Synth Methods 2015;6:293-309. doi:10.1002/jrsm.1160.
52. Warren FC, Abrams KR, Golder S, Sutton AJ. Systematic review of methods used in meta-analyses where a primary outcome is an adverse or unintended event. BMC Med Res Methodol 2012;12:64. doi:10.1186/1471-2288-12-64.
53. Hernán MA, Hernández-Diáz S, Robins JM. A structural approach to selection bias. Epidemiology 2004;15:615-25. doi:10.1097/01. ede.0000135174.63482.43.
54. Graham DJ, Campen D, Hui R, et al. Risk of acute myocardial infarction and sudden cardiac death in patients treated with cyclo-oxygenase 2 selective and non-selective non-steroidal anti-inflammatory drugs: Nested case-control study. Lancet 2005;365:475-81. doi:10.1016/S0140-6736(05)70270-1.
55. 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. doi:10.1161/01. CIR.0000127578.21885.3E.
56. Schneeweiss S, Glynn RJ, Tsai EH, Avorn J, Solomon DH. Adjusting for unmeasured confounders in pharmacoepidemiologic claims data using external information: The example of COX2 inhibitors and myocardial infarction. Epidemiology 2005;16:17-24. doi:10.1097/01. ede.0000147164.11879.b5.
57. Chan CC, Reid CM, Aw TJ, Liew D, Haas SJ, Krum H. Do COX-2 inhibitors raise blood pressure more than nonselective NSAIDs and placebo An updated meta-analysis. J Hypertens 2009;27:2332-41. doi: 10.1097/HJH.0b013e3283310dc9.
58. Johnson AG, Nguyen TV, Day RO. Do nonsteroidal anti-inflammatory drugs affect blood pressure A meta-analysis. Ann Intern Med 1994;121:289-300. doi:10.7326/0003-4819-121-4-199408150-00011.
59. Ungprasert P, Cheungpasitporn W, Crowson CS, Matteson EL. Individual non-steroidal anti-inflammatory drugs and risk of acute kidney injury: A systematic review and meta-analysis of observational studies. Eur J Intern Med 2015;26:285-91. doi:10.1016/j. ejim.2015.03.008.
60. Collins R, Peto R, MacMahon S, et al. Blood pressure, stroke, coronary heart disease. Part 2, Short-term reductions in blood pressure: Overview of randomised drug trials in their epidemiological context. Lancet 1990;335:827-38. doi:10.1016/0140-6736(90)90944-Z.
61. Steering Committee of the Alzheimer's Disease Anti-inflammatory Prevention Trial (ADAPT). Statement for communication to the FDA Arthritis Advisory Committee and the Drug Safety and Risk Management Advisory Committee. 18 February 2005. Available at: Https://jhuccs1.us/adapt/pdf%20documents/FDA%20ADAPT%20 STATEMENT-web%20posting.pdf. Last accessed December 13, 2015.
62. Bueno H, Bardají A, Patrignani P, Martín-Merino E, Garciá-Rodríguez LA. Spanish Case-Control Study to Assess NSAID-Associated ACS Risk Investigators. Use of non-steroidal antiinflammatory drugs and type-specific risk of acute coronary syndrome. Am J Cardiol 2010;105:1102-6. doi:10.1016/j.amjcard.2009.12.008.
63. Kimmel SE, Berlin JA, Reilly M, et al. Patients exposed to rofecoxib and celecoxib have different odds of nonfatal myocardial infarction. Ann Intern Med 2005;142:157-64. doi:10.7326/0003-4819-142-3-200502010-00005.
64. 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. doi:10.1001/archinte.163.4.481.
65. van der Linden MW, van der Bij S, Welsing P, Kuipers EJ, Herings RM. The balance between severe cardiovascular and gastrointestinal events among users of selective and non-selective non-steroidal anti-inflammatory drugs. Ann Rheum Dis 2009;68:668-73. doi:10.1136/ard.2007.087254.
66. Abraham NS, El-Serag HB, Hartman C, Richardson P, Deswal A. 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. doi:10.1111/j.1365-2036.2007.03292.x.
67. Hippisley-Cox J, Coupland C. Risk of myocardial infarction in patients taking cyclo-oxygenase-2 inhibitors or conventional non-steroidal anti-inflammatory drugs: Population based nested case-control analysis. BMJ 2005;330:1366. doi:10.1136/bmj.330.7504.1366.
68. Baron JA, Sandler RS, Bresalier RS, et al. Cardiovascular events associated with rofecoxib: Final analysis of the APPROVe trial. Lancet 2008;372:1756-64. doi:10.1016/S0140-6736(08)61490-7.
69. Debray TP, Moons KG, van Valkenhoef G, et al. GetReal Methods Review Group. Get real in individual participant data (IPD) meta-analysis: A review of the methodology. Res Synth Methods 2015;6:293-309. doi:10.1002/jrsm.1160.
70. Fisher DJ, Copas AJ, Tierney JF, Parmar MK. A critical review of methods for the assessment of patient-level interactions in individual participant data meta-analysis of randomized trials, guidance for practitioners. J Clin Epidemiol 2011;64:949-67. doi:10.1016/j. jclinepi.2010.11.016.
71. Simmonds MC, Higgins JP. Covariate heterogeneity in meta-analysis: Criteria for deciding between meta-regression and individual patient data. Stat Med 2007;26:2982-99. doi:10.1002/sim.2768.
72. Greenland S. Basic problems in interaction assessment. Environ Health Perspect 1993;101(Suppl 4):59-66. doi:10.1289/ehp.93101s459.
73. Greenland S. Interactions in epidemiology: Relevance, identification, estimation. Epidemiology 2009;20:14-7. doi:10.1097/EDE.0b013e318193e7b5.
74. Taichman DB, Backus J, Baethge C, et al. Sharing clinical trial data: A proposal from the International Committee of Medical Journal Editors. Lancet 2016;387:e9-11. doi:10.1016/S0140-6736(15)01279-9.
75. Abrahamowicz M, Bartlett G, Tamblyn R, du Berger R. Modeling cumulative dose and exposure duration provided insights regarding the associations between benzodiazepines and injuries. J Clin Epidemiol 2006;59:393-403. doi:10.1016/j.jclinepi.2005.01.021.
76. Sylvestre MP, Abrahamowicz M. Flexible modeling of the cumulative effects of time-dependent exposures on the hazard. Stat Med 2009;28:3437-53. doi:10.1002/sim.3701.