Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Ảnh hưởng của phong cách sống và vị thế kinh tế - xã hội đến mối liên hệ giữa việc sử dụng thuốc chống viêm không steroid và các sự kiện bất lợi lớn về tim mạch: Nghiên cứu kiểu trường hợp-người đối diện
Tóm tắt
Hiện chưa biết liệu những rủi ro tim mạch liên quan đến việc sử dụng thuốc chống viêm không steroid (NSAID) có khác nhau dựa trên phong cách sống và vị thế kinh tế - xã hội hay không. Chúng tôi đã khảo sát mối liên hệ giữa việc sử dụng NSAID và các sự kiện bất lợi lớn về tim mạch (MACE) trong các phân nhóm được xác định bởi phong cách sống và vị thế kinh tế - xã hội. Chúng tôi đã thực hiện một nghiên cứu kiểu trường hợp-người đối diện đối với tất cả những người lớn lần đầu trả lời khảo sát của Đan Mạch từ các cuộc Khảo sát Y tế Quốc gia năm 2010, 2013 hoặc 2017, không có tiền sử bệnh tim mạch, và đã trải qua một MACE từ khi hoàn thành khảo sát cho đến năm 2020. Chúng tôi đã sử dụng phương pháp Mantel-Haenszel để thu được tỷ lệ odds (OR) của mối liên hệ giữa việc sử dụng NSAID (ibuprofen, naproxen hay diclofenac) và MACE (nhồi máu cơ tim, đột quỵ thiếu máu cục bộ, suy tim, hay tử vong do mọi nguyên nhân). Chúng tôi xác định việc sử dụng NSAID và MACE thông qua các cơ sở dữ liệu về sức khỏe quốc gia của Đan Mạch. Chúng tôi đã phân tầng phân tích theo chỉ số khối cơ thể, tình trạng hút thuốc, mức tiêu thụ rượu, mức độ hoạt động thể chất, tình trạng hôn nhân, trình độ học vấn, thu nhập và việc làm. So với nhóm không sử dụng, OR của MACE là 1.34 (khoảng tin cậy 95%: 1.23–1.46) đối với ibuprofen, 1.48 (1.04–2.43) đối với naproxen, và 2.18 (1.72–2.78) đối với diclofenac. Khi so sánh việc sử dụng NSAID với không sử dụng hoặc từng NSAID với nhau, chúng tôi không quan sát thấy sự khác biệt đáng kể nào về OR trong các phân nhóm phong cách sống và vị thế kinh tế - xã hội đối với bất kỳ NSAID nào. So với ibuprofen, diclofenac có liên quan đến nguy cơ MACE tăng trong một số phân nhóm có nguy cơ tim mạch cao, ví dụ, những người thừa cân (OR 1.52, 1.01–2.39) và những người hút thuốc (OR 1.54, 0.96–2.46). Sự gia tăng tương đối về nguy cơ tim mạch liên quan đến việc sử dụng NSAID không bị ảnh hưởng bởi phong cách sống hay vị thế kinh tế - xã hội.
Từ khóa
#Thuốc chống viêm không steroid #rủi ro tim mạch #sự kiện bất lợi lớn về tim mạch #nghiên cứu kiểu trường hợp-người đối diện #phong cách sống #vị thế kinh tế - xã hộiTài liệu tham khảo
Schmidt M, Lamberts M, Olsen AM, et al. Cardiovascular safety of non-aspirin non-steroidal anti-inflammatory drugs: review and position paper by the working group for Cardiovascular Pharmacotherapy of the European Society of Cardiology. Eur Heart J. 2016;37:1015–23. https://doi.org/10.1093/eurheartj/ehv505.
Schmidt M, Pottegård A. Prescriber responsibility, predictors for initiation, and 20-year trends in use of non-aspirin non-steroidal anti-inflammatory drugs in patients with cardiovascular contraindications: a nationwide cohort study. Eur Heart J Cardiovasc Pharmacother. 2020. https://doi.org/10.1093/ehjcvp/pvaa073.
Bonnesen K, Ehrenstein V, Grønkjaer MS, et al. Impact of lifestyle and socioeconomic position on use of non-steroidal anti-inflammatory drugs: a population-based cohort study. Pharmacoepidemiol Drug Saf. 2022. https://doi.org/10.1002/pds.5571.
Schmidt M, Schmidt SAJ, Adelborg K, et al. The Danish health care system and epidemiological research: from health care contacts to database records. Clin Epidemiol. 2019;11:563–91. https://doi.org/10.2147/clep.S179083.
Schmidt M, Pedersen L, Sørensen HT. The Danish Civil Registration System as a tool in epidemiology. Eur J Epidemiol. 2014;29:541–9. https://doi.org/10.1007/s10654-014-9930-3.
Hallas J, Pottegård A. Use of self-controlled designs in pharmacoepidemiology. J Intern Med. 2014;275:581–9. https://doi.org/10.1111/joim.12186.
Christensen AI, Lau CJ, Kristensen PL, et al. The Danish National Health Survey: study design, response rate and respondent characteristics in 2010, 2013 and 2017. Scand J Public Health. 2020. https://doi.org/10.1177/1403494820966534.
Schmidt M, Hallas J, Friis S. Potential of prescription registries to capture individual-level use of aspirin and other nonsteroidal anti-inflammatory drugs in Denmark: trends in utilization 1999–2012. Clin Epidemiol. 2014;6:155–68. https://doi.org/10.2147/clep.S59156.
van Staa TP, Abenhaim L, Leufkens H. A study of the effects of exposure misclassification due to the time-window design in pharmacoepidemiologic studies. J Clin Epidemiol. 1994;47:183–9. https://doi.org/10.1016/0895-4356(94)90023-x.
Sinnott SJ, Polinski JM, Byrne S, et al. Measuring drug exposure: concordance between defined daily dose and days’ supply depended on drug class. J Clin Epidemiol. 2016;69(107–113):20150604. https://doi.org/10.1016/j.jclinepi.2015.05.026.
Pottegård A, Schmidt SAJ, Wallach-Kildemoes H, et al. Data resource profile: the Danish National Prescription Registry. Int J Epidemiol. 2017;46:798–798f. https://doi.org/10.1093/ije/dyw213.
Bhala 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 (London, England). 2013;382(769–779):20130530. https://doi.org/10.1016/s0140-6736(13)60900-9.
Trelle S, Reichenbach S, Wandel S, et al. Cardiovascular safety of non-steroidal anti-inflammatory drugs: network meta-analysis. BMJ (Clin Res Ed). 2011;342:c7086. https://doi.org/10.1136/bmj.c7086.
Schmidt M, Schmidt SA, Sandegaard JL, et al. The Danish National Patient Registry: a review of content, data quality, and research potential. Clin Epidemiol. 2015;7:449–90. https://doi.org/10.2147/clep.S91125.
Sundbøll J, Adelborg K, Munch T, et al. Positive predictive value of cardiovascular diagnoses in the Danish National Patient Registry: a validation study. BMJ Open. 2016;6:e012832. https://doi.org/10.1136/bmjopen-2016-012832.
Johnsen SP, Overvad K, Sørensen HT, et al. Predictive value of stroke and transient ischemic attack discharge diagnoses in the Danish National Registry of Patients. J Clin Epidemiol. 2002;55:602–7. https://doi.org/10.1016/s0895-4356(02)00391-8.
Baadsgaard M, Quitzau J. Danish registers on personal income and transfer payments. Scand J Public Health. 2011;39:103–5. https://doi.org/10.1177/1403494811405098.
Petersson F, Baadsgaard M, Thygesen LC. Danish registers on personal labour market affiliation. Scand J Public Health. 2011;39:95–8. https://doi.org/10.1177/1403494811408483.
Quan H, Li B, Couris CM, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173:676–82. https://doi.org/10.1093/aje/kwq433.
Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130–9. https://doi.org/10.1097/01.mlr.0000182534.19832.83.
Maclure M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133:144–53. https://doi.org/10.1093/oxfordjournals.aje.a115853.
Kubota K, Kelly TL, Sato T, et al. A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies. BMC Med Res Methodol. 2021;21:214. https://doi.org/10.1186/s12874-021-01408-5.
Hallas J, Whitaker H, Delaney JA, et al. The use of active comparators in self-controlled designs. Am J Epidemiol. 2021;190:2181–7. https://doi.org/10.1093/aje/kwab110.
Gardarsdottir H, Souverein PC, Egberts TC, et al. Construction of drug treatment episodes from drug-dispensing histories is influenced by the gap length. J Clin Epidemiol. 2010;63(422–427):20091031. https://doi.org/10.1016/j.jclinepi.2009.07.001.
Khan SS, Ning H, Wilkins JT, et al. Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity. JAMA Cardiol. 2018;3:280–7. https://doi.org/10.1001/jamacardio.2018.0022.
Whitlock G, Lewington S, Sherliker P, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet (London, England). 2009;373(1083–1096):20090318. https://doi.org/10.1016/s0140-6736(09)60318-4.
Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case–control study. Lancet (London, England). 2004;364:937–52. https://doi.org/10.1016/s0140-6736(04)17018-9.
Prescott E, Hippe M, Schnohr P, et al. Smoking and risk of myocardial infarction in women and men: longitudinal population study. BMJ (Clin Res Ed). 1998;316:1043–7. https://doi.org/10.1136/bmj.316.7137.1043.
Chamberlain AM, Agarwal SK, Folsom AR, et al. Smoking and incidence of atrial fibrillation: results from the Atherosclerosis Risk in Communities (ARIC) study. Heart Rhythm. 2011;8(1160–1166):20110315. https://doi.org/10.1016/j.hrthm.2011.03.038.
Mons U, Müezzinler A, Gellert C, et al. Impact of smoking and smoking cessation on cardiovascular events and mortality among older adults: meta-analysis of individual participant data from prospective cohort studies of the CHANCES consortium. BMJ (Clin Res Ed). 2015;350(h1551):20150420. https://doi.org/10.1136/bmj.h1551.
Reynolds K, Lewis B, Nolen JD, et al. Alcohol consumption and risk of stroke: a meta-analysis. JAMA. 2003;289:579–88. https://doi.org/10.1001/jama.289.5.579.
Di Castelnuovo A, Costanzo S, Bagnardi V, et al. Alcohol dosing and total mortality in men and women: an updated meta-analysis of 34 prospective studies. Arch Intern Med. 2006;166:2437–45. https://doi.org/10.1001/archinte.166.22.2437.
Corrao G, Rubbiati L, Bagnardi V, et al. Alcohol and coronary heart disease: a meta-analysis. Addiction (Abingdon, England). 2000;95:1505–23. https://doi.org/10.1046/j.1360-0443.2000.951015056.x.
Blair SN, Kampert JB, Kohl HW 3rd, et al. Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women. JAMA. 1996;276:205–10.
Paffenbarger RS Jr, Hyde RT, Wing AL, et al. Physical activity, all-cause mortality, and longevity of college alumni. N Engl J Med. 1986;314:605–13. https://doi.org/10.1056/nejm198603063141003.
Kershaw KN, Droomers M, Robinson WR, et al. Quantifying the contributions of behavioral and biological risk factors to socioeconomic disparities in coronary heart disease incidence: the MORGEN study. Eur J Epidemiol. 2013;28(807–814):20130914. https://doi.org/10.1007/s10654-013-9847-2.
Méjean C, Droomers M, van der Schouw YT, et al. The contribution of diet and lifestyle to socioeconomic inequalities in cardiovascular morbidity and mortality. Int J Cardiol. 2013;168(5190–5195):20130729. https://doi.org/10.1016/j.ijcard.2013.07.188.
Rosengren A, Subramanian SV, Islam S, et al. Education and risk for acute myocardial infarction in 52 high, middle and low-income countries: INTERHEART case-control study. Heart (Br Card Soc). 2009;95(2014–2022):20091012. https://doi.org/10.1136/hrt.2009.182436.
Woodward M, Peters SA, Batty GD, et al. Socioeconomic status in relation to cardiovascular disease and cause-specific mortality: a comparison of Asian and Australasian populations in a pooled analysis. BMJ Open. 2015;5(e006408):20150317. https://doi.org/10.1136/bmjopen-2014-006408.
Kilander L, Berglund L, Boberg M, et al. Education, lifestyle factors and mortality from cardiovascular disease and cancer. A 25-year follow-up of Swedish 50-year-old men. Int J Epidemiol. 2001;30:1119–26. https://doi.org/10.1093/ije/30.5.1119.
Dupre ME, George LK, Liu G, et al. The cumulative effect of unemployment on risks for acute myocardial infarction. Arch Intern Med. 2012;172:1731–7. https://doi.org/10.1001/2013.jamainternmed.447.
Meneton P, Kesse-Guyot E, Méjean C, et al. Unemployment is associated with high cardiovascular event rate and increased all-cause mortality in middle-aged socially privileged individuals. Int Arch Occup Environ Health. 2015;88(707–716):20141111. https://doi.org/10.1007/s00420-014-0997-7.
Wong CW, Kwok CS, Narain A, et al. Marital status and risk of cardiovascular diseases: a systematic review and meta-analysis. Heart (Br Card Soc). 2018;104(1937–1948):20180619. https://doi.org/10.1136/heartjnl-2018-313005.
Hu B, Li W, Wang X, et al. Marital status, education, and risk of acute myocardial infarction in Mainland China: the INTER-HEART study. J Epidemiol. 2012;22(123–129):20120114. https://doi.org/10.2188/jea.je20100175.
Metzner J, Popp L, Marian C, et al. The effects of COX-2 selective and non-selective NSAIDs on the initiation and progression of atherosclerosis in ApoE−/− mice. J Mol Med (Berlin, Germany). 2007;85:623–33. https://doi.org/10.1007/s00109-007-0162-9.
Aw TJ, Haas SJ, Liew D, et al. Meta-analysis of cyclooxygenase-2 inhibitors and their effects on blood pressure. Arch Intern Med. 2005;165(490–496):20050214. https://doi.org/10.1001/archinte.165.5.IOI50013.
Bonnesen K, Schmidt M. Re-categorization of non-aspirin non-steroidal anti-inflammatory drugs (NSAIDs) according to clinical relevance: abandoning the ‘traditional NSAID’ terminology. Can J Cardiol. 2021. https://doi.org/10.1016/j.cjca.2021.06.014.
Grosser T, Yu Y, Fitzgerald GA. Emotion recollected in tranquility: lessons learned from the COX-2 saga. Annu Rev Med. 2010;61:17–33. https://doi.org/10.1146/annurev-med-011209-153129.
Martín Arias LH, Martín González A, Sanz Fadrique R, et al. Cardiovascular risk of nonsteroidal anti-inflammatory drugs and classical and selective cyclooxygenase-2 inhibitors: a meta-analysis of observational studies. J Clin Pharmacol. 2019;59(55–73):20180911. https://doi.org/10.1002/jcph.1302.
Varas-Lorenzo C, Riera-Guardia N, Calingaert B, et al. Myocardial infarction and individual nonsteroidal anti-inflammatory drugs meta-analysis of observational studies. Pharmacepidemiol Drug Saf. 2013;22:559–70. https://doi.org/10.1002/pds.3437.
Jurek AM, Greenland S, Maldonado G, et al. Proper interpretation of non-differential misclassification effects: expectations vs observations. Int J Epidemiol. 2005;34:680–7. https://doi.org/10.1093/ije/dyi060.
Tricykliske antidepressiva (TCA). https://pro.medicin.dk/Laegemiddelgrupper/Grupper/243030. Accessed 4 Feb 2023.
Hinz B, Cheremina O, Brune K. Acetaminophen (paracetamol) is a selective cyclooxygenase-2 inhibitor in man. FASEB J. 2008;22(383–390):20070920. https://doi.org/10.1096/fj.07-8506com.
Roberts E, Delgado Nunes V, Buckner S, et al. Paracetamol: not as safe as we thought? A systematic literature review of observational studies. Ann Rheum Dis. 2016;75:552–9. https://doi.org/10.1136/annrheumdis-2014-206914.
Zeng C, Rosenberg L, Li X, et al. Sodium-containing acetaminophen and cardiovascular outcomes in individuals with and without hypertension. Eur Heart J. 2022;43:1743–55. https://doi.org/10.1093/eurheartj/ehac059.
MacIntyre IM, Turtle EJ, Farrah TE, et al. Regular acetaminophen use and blood pressure in people with hypertension: the PATH-BP trial. Circulation. 2022;145(416–423):20220207. https://doi.org/10.1161/circulationaha.121.056015.
Kannel WB. Fifty years of Framingham Study contributions to understanding hypertension. J Hum Hypertens. 2000;14:83–90. https://doi.org/10.1038/sj.jhh.1000949.
Ference BA, Ginsberg HN, Graham I, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2017;38:2459–72. https://doi.org/10.1093/eurheartj/ehx144.
Lash TL, Vanderweele TJ, Haneuse S, et al. Modern epidemiology, chapter 15: precision and study size. 4th ed. Philadelphia: Wolters Kluwer; 2021. p. 333–64.
Tomlinson G, Detsky AS. Composite end points in randomized trials: there is no free lunch. JAMA. 2010;303:267–8. https://doi.org/10.1001/jama.2009.2017.
Montori VM, Permanyer-Miralda G, Ferreira-González I, et al. Validity of composite end points in clinical trials. BMJ (Clin Res Ed). 2005;330:594–6. https://doi.org/10.1136/bmj.330.7491.594.
Lauer MS, Topol EJ. Clinical trials–multiple treatments, multiple end points, and multiple lessons. JAMA. 2003;289:2575–7. https://doi.org/10.1001/jama.289.19.2575.
Schmidt M, Sørensen HT, Pedersen L. Diclofenac use and cardiovascular risks: series of nationwide cohort studies. BMJ (Clin Res Ed). 2018;362(k3426):20180904. https://doi.org/10.1136/bmj.k3426.