Submitted:
24 July 2023
Posted:
25 July 2023
You are already at the latest version
Abstract
Keywords:
1. Introduction
2. Methods
2.1. Statistical Analysis
3. Results
4. Discussion
5. Limitations
Funding
Institutional Board approval
Data Availability Statement
Acknowledgements
Conflicts of Interest
References
- Global Burden of Disease Study 2013 Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015, 386, 743–800. [Google Scholar] [CrossRef]
- Ohira T, Iso H. Cardiovascular disease epidemiology in Asia: An overview. Circ J. 2013, 77, 1646–1652.
- Lozano R, Naghavi M, Foreman K; et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012, 380, 2095–2128. [Google Scholar] [CrossRef] [PubMed]
- Murray CJ, Vos T, Lozano R; et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012, 380, 2197–2223. [Google Scholar] [CrossRef] [PubMed]
- Page RL 2nd, Ghushchyan V, Gifford B; et al. The economic burden of acute coronary syndromes for employees and their dependents: Medical and productivity costs. J Occup Environ Med. 2013, 55, 761–767. [Google Scholar] [CrossRef]
- Johnston SS, Curkendall S, Makenbaeva D; et al. The direct and indirect cost burden of acute coronary syndrome. J Occup Environ Med. 2011, 53, 2–7. [Google Scholar] [CrossRef]
- Zhao Z, Winget M. Economic burden of illness of acute coronary syndromes: Medical and productivity costs. BMC Health Serv Res 2011, 11, 35. [CrossRef]
- Wiegand TJ, Vernetti CM. Nonsteroidal Anti-inflammatory Drug (NSAID) Toxicity. Medscape 2020. Available at:. Available online: https://emedicine.medscape.com/article/816117-print (accessed on 14 July 2020).
- Fanelli A, Ghisi D, Aprile PL, Lapi F. Cardiovascular and cerebrovascular risk with nonsteroidal anti-inflammatory drugs and cyclooxygenase 2 inhibitors: Latest evidence and clinical implications. Ther Adv Drug Saf. 2017, 8, 173–182. [Google Scholar] [CrossRef]
- Bally M, Dendukuri N, Rich B; et al. Risk of acute myocardial infarction with NSAIDs in real world use: Bayesian meta-analysis of individual patient data. BMJ. 2017, 357, j1909. [Google Scholar]
- Park K, Bavry AA. Risk of stroke associated with nonsteroidal anti-inflammatory drugs. Vasc Health Risk Manag. 2014, 10, 25–32. [Google Scholar]
- Zubaid M, Thani KB, Rashed W, et al; Gulf COAST investigators Design and Rationale of Gulf locals with Acute Coronary Syndrome Events (Gulf Coast) Registry. Open Cardiovasc Med J. 2014, 8, 88–93. [Google Scholar] [CrossRef] [PubMed]
- Weintraub WS, Karlsberg RP, Tcheng JE; et al. ACCF/AHA 2011 key data elements and definitions of a base cardiovascular vocabulary for electronic health records: A report of the American College of Cardiology Foundation/American Heart Association Task Force on Clinical Data Standards. J Am Coll Cardiol. 2011, 58, 202–222. [Google Scholar] [CrossRef] [PubMed]
- Panduranga P, Sulaiman K, Al-Zakwani I; et al. Utilization and determinants of in-hospital cardiac catheterization in patients with acute coronary syndrome from the Middle East. Angiology. 2010, 61, 744–750. [Google Scholar] [CrossRef] [PubMed]
- Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 1982, 115, 92–106. [Google Scholar] [CrossRef]
- Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982, 143, 29–36. [Google Scholar] [CrossRef]
- Schink T, Kollhorst B, Varas Lorenzo C; et al. Risk of ischemic stroke and the use of individual non-steroidal anti-inflammatory drugs: A multi-country European database study within the SOS Project. PLoS ONE. 2018, 13, e0203362. [Google Scholar]
- Varga Z, Sabzwari SRA, Vargova V. Cardiovascular Risk of Nonsteroidal Anti-Inflammatory Drugs: An Under-Recognized Public Health Issue. Cureus. 2017, 9, e1144. [Google Scholar]
- Trelle S, Reichenbach S, Wandel S, Hildebrand P, Tschannen B, Villiger PM; et al. Cardiovascular safety of non-steroidal anti-inflammatory drugs: Network meta-analysis. BMJ. 2011, 342, c7086. [Google Scholar] [CrossRef]
- Kearney PM, Baigent C, Godwin J, Halls H, Emberson JR, Patrono C. Do selective cyclo-oxygenase-2 inhibitors and traditional non-steroidal anti-inflammatory drugs increase the risk of atherothrombosis? Meta-analysis of randomised trials. BMJ. 2006, 332, 1302–1308. [Google Scholar] [CrossRef]
- Varas-Lorenzo C, Riera-Guardia N, Calingaert B, Castellsague J, Pariente A, Scotti L; et al. Stroke risk and NSAIDs: A systematic review of observational studies. Pharmacoepidemiol Drug Saf. 2011, 20, 1225–1236. [Google Scholar] [CrossRef]
- Coxib and traditional NSAID Trialists' (CNT) Collaboration, Bhala N, Emberson J, Merhi A, Abramson S, Arber N, Baron JA; 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, 769–779. [Google Scholar] [CrossRef] [PubMed]
- Fitzgerald, GA. Coxibs and cardiovascular disease. N Engl J Med. 2004, 21, 1709–1711. [Google Scholar] [CrossRef] [PubMed]
- Schjerning Olsen AM, Gislason GH, McGettigan P, Fosbøl E, Sørensen R, Hansen ML, Køber L, Torp-Pedersen C, Lamberts M. Association of NSAID use with risk of bleeding and cardiovascular events in patients receiving antithrombotic therapy after myocardial infarction. JAMA. 2015, 313, 805–814. [Google Scholar] [CrossRef]
- Bally M, Dendukuri N, Rich B, Nadeau L, Helin-Salmivaara A, Garbe E, Brophy JM. Risk of acute myocardial infarction with NSAIDs in real world use: Bayesian meta-analysis of individual patient data. BMJ. 2017, 357, j1909. [Google Scholar]
- Helin-Salmivaara A, Virtanen A, Vesalainen R, Grönroos JM, Klaukka T, Idänpään-Heikkilä JE, Huupponen R. 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, 1657–1663. [Google Scholar] [CrossRef] [PubMed]
- Johnsen SP, Larsson H, Tarone RE, McLaughlin JK, Nørgård B, Friis S, Sørensen HT. Risk of hospitalization for myocardial infarction among users of rofecoxib, celecoxib, and other NSAIDs: A population-based case-control study. Arch Intern Med. 2005, 165, 978–984. [Google Scholar] [CrossRef]
| Characteristic, n (%) unless specified otherwise |
All (N = 3,007) |
NSAID use | p-value | ||
|---|---|---|---|---|---|
| No (n = 2,717) |
Yes (n = 290) |
||||
| Demographic | |||||
| Age, mean±SD, years | 62±12 | 62±12 | 63±12 | 0.124 | |
| Female gender | 1,156 (38%) | 1,029 (38%) | 127 (44%) | 0.049 | |
| Educated | 1,410 (47%) | 1,254 (46%) | 156 (54%) | 0.013 | |
| Employed | 647 (22%) | 592 (22%) | 55 (19%) | 0.266 | |
| Married | 2,495 (83%) | 2,252 (83%) | 243 (84%) | 0.696 | |
| BMI, mean±SD, kg/m2 | 29.1±7.0 | 29.1±7.1 | 28.7±6.3 | 0.305 | |
| Smoking (current or prior) | 1,037 (34%) | 939 (35%) | 98 (34%) | 0.794 | |
| Alcohol | 87 (2.9%) | 75 (2.8%) | 12 (4.1%) | 0.183 | |
| Past medical history | |||||
| Prior MI | 1,026 (34%) | 933 (34%) | 93 (32%) | 0.438 | |
| Dyslipidemia | 2,101 (70%) | 1,879 (69%) | 222 (77%) | 0.009 | |
| Premature CAD | 446 (15%) | 405 (15%) | 41 (14%) | 0.726 | |
| Hypertension | 2,433 (81%) | 2,183 (80%) | 250 (86%) | 0.016 | |
| Heart failure | 441 (15%) | 384 (14%) | 57 (20%) | 0.012 | |
| Diabetes mellitus | 1,903 (63%) | 1,738 (64%) | 165 (57%) | 0.018 | |
| Stroke/TIA | 274 (9.1%) | 254 (9.0%) | 29 (10%) | 0.580 | |
| Clinical (parameters) at presentation | |||||
| HR, mean±SD, bpm | 86±21 | 86±21 | 85±22 | 0.229 | |
| SBP, mean±SD, mmHg | 142±28 | 142±28 | 143±29 | 0.519 | |
| DBP, mean±SD, mmHg | 80±16 | 80±16 | 80±17 | 0.800 | |
| Crea, p50 (IQR), µmol/L | 86 (68-113) | 86 (68-113) | 85 (68-104) | 0.772 | |
| LVEF, mean±SD, % | 49±13 | 48±13 | 50±13 | <0.001 | |
| GRACE risk, mean±SD | 130±42 | 130±42 | 130±43 | 0.789 | |
| CRUSADE risk score | 38±15 | 38±15 | 38±15 | 0.735 | |
| Major bleed | 62 (2.1%) | 54 (2.0%) | 8 (2.8%) | 0.380 | |
| Killip class | 0.320 | ||||
| I – no heart failure | 2270 (75%) | 2063 (76%) | 207 (71%) | ||
| II – rales | 457 (15%) | 403 (15%) | 54 (19%) | ||
| III – pulmonary edema | 250 (8.3%) | 224 (8.2%) | 26 (9.0%) | ||
| IV – cardiogenic shock | 30 (1.0%) | 27 (1.0%) | 3 (1.0%) | ||
| Discharged diagnosis* |
<0.001 |
||||
| LBBB MI | 19 (0.7%) | 16 (0.6%) | 3 (1.1%) | ||
| NSTEMI | 1474 (51%) | 1358 (52%) | 116 (41%) | ||
| STEMI | 476 (17%) | 433 (17%) | 43 (15%) | ||
| Unstable angina | 908 (32%) | 789 (30%) | 119 (42%) | ||
| Characteristic, n (%) unless specified otherwise |
All (N = 3,007) |
NSAID use | p-value | |
|---|---|---|---|---|
| No (n = 2,717) |
Yes (n = 290) |
|||
| Prior medications | ||||
| Aspirin | 2,397 (80%) | 2,151 (79%) | 246 (85%) | 0.023 |
| Clopidogrel | 863 (29%) | 762 (28%) | 101 (35%) | 0.015 |
| ACEIs | 1,562 (52%) | 1,437 (53%) | 125 (43%) | 0.002 |
| ARBs | 573 (19%) | 485 (18%) | 88 (30%) | <0.001 |
| Beta blockers | 1,828 (61%) | 1,639 (60%) | 189 (65%) | 0.108 |
| Statins | 2,428 (81%) | 2,186 (80%) | 242 (83%) | 0.219 |
| Other LLDs | 60 (2.0%) | 57 (2.1%) | 3 (1.0%) | 0.273 |
| Oral nitrates | 1,049 (35%) | 670 (42%) | 379 (27%) | <0.001 |
| CCBs | 599 (20%) | 523 (19%) | 76 (26%) | 0.005 |
| H2-receptor antagonists | 410 (14%) | 331 (12%) | 79 (27%) | <0.001 |
| Proton pump inhibitors | 617 (21%) | 527 (19%) | 90 (31%) | <0.001 |
| Discharged medications (N=2,747)* | ||||
| Aspirin | 2,645 (96%) | 2,388 (96%) | 257 (98%) | 0.197 |
| Clopidogrel | 2,009 (73%) | 1,792 (72%) | 217 (83%) | <0.001 |
| ACEIs | 1,795 (65%) | 1624 (65%) | 171 (65%) | 0.907 |
| ARBs | 499 (18%) | 443 (18%) | 56 (21%) | 0.168 |
| Beta blockers | 2,324 (85%) | 2,089 (84%) | 235 (89%) | 0.025 |
| Statins | 2,675 (97%) | 2,414 (97%) | 261 (99%) | 0.050 |
| Other LLDs | 75 (2.7%) | 67 (2.7%) | 8 (3.0%) | 0.745 |
| Oral nitrates | 1,722 (63%) | 1,562 (63%) | 160 (61%) | 0.509 |
| CCBs | 509 (19%) | 462 (19%) | 47 (18%) | 0.770 |
| Dual antiplatelets | 2,705 (98%) | 2,445 (98%) | 260 (99%) | 0.589 |
| 5-drug regimen | 1,427 (52%) | 1,265 (51%) | 162 (62%) | 0.001 |
| H2-receptor antagonists | 582 (21%) | 517 (21%) | 65 (25%) | 0.142 |
| Proton pump inhibitors | 356 (13%) | 315 (13%) | 41 (16%) | 0.183 |
| Outcome | Univariate statistics (NSAID use) | Multivariate logistic regression | ||||||
|---|---|---|---|---|---|---|---|---|
| All (N = 2910) | No (n = 2627) | Yes (n = 283) | p-value | Adj. OR [95% CI] | Adj. p-value | HL | ROC | |
| Stroke/TIA | 143 (4.9%) | 115 (4.4%) | 28 (9.9%) | <0.001 | 2.50 [1.51-4.14] | <0.001 | 0.917 | 0.75 |
| Myocardial infarction | 249 (8.6%) | 223 (8.5%) | 26 (9.2%) | 0.690 | 1.26 [0.80-1.99] | 0.320 | 0.102 | 0.71 |
| All-cause Mortality | 395 (13.6%) | 366 (14.0%) | 29 (10.3%) | 0.086 | 0.79 [0.46-1.34] | 0.383 | 0.075 | 0.78 |
| Re-admissions | 823 (28.3%) | 705 (26.8%) | 118 (41.7%) | <0.001 | 2.09 [1.59-2.74] | <0.001 | 0.665 | 0.61 |
| Total MACE | 1195 (41.1%) | 1052 (40.1%) | 143 (50.5%) | 0.001 | 1.89 [1.44-2.48] | <0.001 | 0.391 | 0.67 |
| Characteristic, mean±SD unless specified otherwise |
LTF (n = 97) 3.2% |
Remaining (n = 2910) 96.8% |
p-value |
|---|---|---|---|
| Demographic | |||
| Age, years | 62±11 | 62±12 | 0.940 |
| Female gender, n (%) | 38 (39%) | 1,118 (38%) | 0.880 |
| BMI, kg/m2 | 29.4±5.5 | 29.1±7.1 | 0.646 |
| Clinical, n (%) | |||
| Prior MI | 32 (33%) | 994 (34%) | 0.811 |
| Hypertension | 81 (84%) | 2,352 (81%) | 0.509 |
| Diabetes mellitus | 69 (71%) | 1,834 (63%) | 0.103 |
| Stroke/TIA | 8 (8.3%) | 266 (9.1%) | 0.764 |
| Presentation, n (%) | |||
| SBP, mmHg | 143±29 | 142±28 | 0.960 |
| DBP, mmHg | 80±17 | 80±16 | 0.832 |
| Killip ≥2, n (%) | 23 (24%) | 714 (25%) | 0.853 |
| GRACE risk score | 132±41 | 130±42 | 0.668 |
| CRUSADE risk score | 39±15 | 38±15 | 0.603 |
| Major bleeding | 3 (3.1%) | 59 (2.0%) | 0.451 |
| Prior PCI | 30 (31%) | 793 (27%) | 0.424 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).