Cardiovascular disease (CVD) can be detected and quantified by analysis of the electrocardiogram (ECG); however the effects of smoking and smoking cessation on the ECG have not been characterized.
Standard 12-lead ECGs were performed at baseline and 3 years after subjects enrolled in a prospective, randomized, placebo-controlled clinical trial of smoking cessation pharmacotherapies. ECGs were interpreted using the Minnesota Code ECG Classification. The effects of (i) smoking burden on the prevalence of ECG findings at baseline, and (ii) smoking and smoking cessation on ECG changes after 3 years were investigated by multivariable and multinomial regression analyses.
At baseline, 532 smokers were (mean [SD]) 43.3 (11.5) years old, smoked 20.6 (7.9) cigarettes/day, with a smoking burden of 26.7 (18.6) pack-years. Major and minor ECG criteria were identified in 87 (16.4%) and 131 (24.6%) of subjects, respectively. After adjusting for demographic data and known CVD risk factors, higher pack-years was associated with major ECG abnormalities (p = 0.02), but current cigarettes/day (p = 0.23) was not. After 3 years, 42.9% of subjects were abstinent from smoking. New major and minor ECG criteria were observed in 7.2% and 15.6% of subjects respectively, but in similar numbers of abstinent subjects and continuing smokers (p>0.2 for both). Continuing smokers showed significant reduction in current smoking (–8.4 [8.8] cigarettes/day, p<0.001) compared to baseline.
In conclusion, major ECG abnormalities are independently associated with lifetime smoking burden. After 3 years, smoking cessation was not associated with a decrease in ECG abnormalities, although cigarettes smoked/day decreased among continuing smokers.
Citation: Gepner AD, Piper ME, Leal MA, Asthana A, Fiore MC, et al. (2013) Electrocardiographic Changes Associated with Smoking and Smoking Cessation: Outcomes from a Randomized Controlled Trial. PLoS ONE 8(4): e62311. doi:10.1371/journal.pone.0062311
Editor: Marco Idzko, University Hospital Freiburg, Germany
Received: December 13, 2012; Accepted: March 20, 2013; Published: April 23, 2013
Copyright: © 2013 Gepner et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by the National Institutes of Health (P50 DA019706, K05 CA139871, and a T32 HL 07936 Ruth L. Kirschstein National Research Service Award from the National Heart Lung and Blood Institute to the University of Wisconsin-Madison Cardiovascular Research Center). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Cigarette smoking contributes to the loss of over 5 million life-years annually in the United States . Nearly 20% of all coronary heart disease deaths can be attributed to smoking , . Smoking, therefore, is one of the most important modifiable risk factors for cardiovascular disease (CVD) and myocardial infarction .
Cigarette smoking leads to hypoxemia and endothelial dysfunction which accelerate atherosclerotic changes, increasing smokers’ risk for CVD , . The 12-lead electrocardiogram (ECG) is a routine, inexpensive tool for assessment of CVD and CVD risk in both clinical and research settings, and ECG changes powerfully predict future CVD events , . Large, population-based studies have described a higher prevalence of smoking in subjects with ECG abnormalities and demonstrated that ECG abnormalities are associated with increased CVD events and death , . Although the strong relation between smoking and CVD is well-established , , there are relatively few data that prospectively described ECG changes following a smoking cessation attempt. Furthermore, most of the existing data about CVD and smoking is from older cohorts that are not representative of today’s smokers, who tend to be more overweight, are more likely to be female, and to have lower socioeconomic status . To our knowledge, no studies have described the prevalence of ECG abnormalities in a younger group of cigarette smokers or their associations with smoking burden. The primary aim of this study was to describe the prevalence and magnitude of resting ECG abnormalities among smokers and to explore the relation between smoking burden and ECG abnormalities. A secondary aim was to determine if successful smoking cessation reduced, or if continued smoking increased the incidence of ECG abnormalities that indicate structural or conduction system function, after three years.
The institutional review board at the University of Wisconsin School of Medicine and Public Health approved this study. It was conducted according to the principles expressed in the Declaration of Helsinki. All subjects provided written informed consent.
Study Participants and Design
All subjects agreed to participate in a randomized, double blind, placebo-controlled trial to evaluate the efficacy of smoking cessation pharmacotherapies and natural history of continued smoking and smoking cessation (clinicaltrials.gov registration no. NCT00332644) . Specific recruitment strategies have been described previously . This article describes a pre-specified secondary analysis of the data from the baseline and year 3 (final) study visits regarding the effects of smoking and smoking cessation on ECG changes at baseline and 3 years after the target quit date.
Major inclusion criteria were: age ≥18 years, smoking ≥10 cigarettes/day (cpd), expired carbon monoxide (CO) level >9 ppm, and stated motivation to quit smoking. Exclusion criteria have been reported previously ; the major reasons for exclusion were blood pressure (BP) >160/100 mmHg, myocardial infarction within the previous 4 weeks, heavy alcohol use, history of seizure or serious head injury, use of contraindicated medications, and current pregnancy or breast-feeding .
Subjects were recruited from communities in and around Madison and Milwaukee, Wisconsin from January, 2005 to June, 2007 . Only subjects recruited from Madison, Wisconsin had resting ECGs obtained prior to an exercise stress test that was used to evaluate functional capacity . Of those participants, 541 had a resting ECG available for coding; 3 did not include a subject ID and 6 could not be coded due to tracing artifact. Similarly, of the 370 subjects at 3 years of follow up, 355 ECGs were available for coding and 1 could not be coded due to tracing artifact. The baseline clinical trial visits included measurement of anthropometric data, fasting laboratory tests, completion of validated questionnaires, interviews, and resting 12-lead ECGs. Smoking burden was evaluated by current cigarette smoking in cigarettes per day (cpd) and pack-years (current cpd * years smoked). Recent smoke exposure was measured by an exhaled CO level, which reflects smoking efficiency and recent smoke exposure. Smoking status (abstinence or continued smoking) was assessed by self-reported 7-day point-prevalence abstinence using a smoking calendar and the timeline follow-back method, and then confirmed by exhaled CO levels of <10 ppm (Micro-3 Smokerlyzer, Bedfont Scientific, Williamsburg, VA) . Fasting blood samples were obtained by venipuncture and refrigerated.
Participants had a 12-lead ECG recorded in the resting supine position using a standardized acquisition procedure at baseline and 3 years after a target quit day. Electrocardiograms were reviewed by 2 independent readers (ADG, MAL) and coded according to the Minnesota Code (MC) criteria . Any discordant results were reviewed and adjudicated by a board certified cardiologist (JHS). All ECG reviewers were blinded to smoking status. The QRS axis and intervals (RR, PR, QRS, QT, and corrected QT) were measured manually and recorded. ECG abnormalities were categorized as major or minor, using similar criteria as used by the pooling project . Major ECG abnormalities included major Q waves (MC 1-1, 1-2), significant ST segment depression (MC 4-1, 4-2), deep T wave inversion (MC 5-1, 5-2), complete or type II second-degree atrioventricular block (MC 6-1, 6-2), complete left or right bundle branch block, or intraventricular block (MC 7-1,7-2,7-4), atrial fibrillation/flutter (8-3), left ventricular hypertrophy (3-1) and right or left atrial enlargement (9-3, 9-6). Minor ECG abnormalities included borderline Q waves (MC 1-3), borderline ST segment depression (MC 4-3), moderate T wave inversion (MC 5-3), first-degree AV block (MC 6-3), type I second degree heart block (MC 6-2-3), incomplete bundle branch block (MC 7-3, 7-6), frequent premature beats (MC 8-1, 8-2), high voltage QRS (MC 3-1, 3-2), and axis deviation (MC 2-1, 2-2). ECGs without major or minor abnormalities were considered normal. ECG abnormalities for each subject were tallied and reported at baseline and at year 3. New ECG abnormalities were defined as ECG abnormalities at year 3 that were not present at baseline. Suppression codes, as detailed by the MC criteria, were used to eliminate incompatible codes within each ECG.
All analyses were conducted using SPSS Statistics 20.0 software (SPSS Inc, Chicago, Illinois). Means (standard deviations) and ranges were determined for the subject characteristics and smoking intensity parameters in Table 1. Two-tailed t-tests or chi-square tests were used to compare differences in anthropomorphic data, physiological data, demographics, and smoking parameters between continuing smokers and abstainers at year 3 (Table 2). Multivariable logistic regression models were constructed to determine associations between ECG abnormalities, at baseline and at 3 years after the target quit date, with measures of smoking burden. These models controlled for baseline age, sex, race, weight, body-mass index, glucose, low-density lipoprotein cholesterol, educational status, use of lipid-lowering medications and use of antihypertensive medications. Multinomial regression models were used to evaluate progression and regression of ECG changes in smokers and abstainers.
Table 1. Subject Characteristics at Baseline and Year 3.doi:10.1371/journal.pone.0062311.t001
Subject characteristics at baseline and year 3 are provided in Table 1. At baseline, resting ECGs were obtained in 532 current smokers. Of these subjects, 63% were women, 95% were white and 3.2% were African-American. They were (mean [SD]) 43.3 (11.5) years old and smoked 20.6 (7.9) cpd with a smoking burden of 26.7 (18.6) pack-years. Blood pressure (119.6 [13.9]/75.8[8.9] mmHg) and fasting lipid values were normal. At baseline, only 25 (4.7%) of the subjects were on antihypertensive therapy and only 15 (2.8%) of the subjects were using lipid-lowering medications (Table 1). Major and Minor ECG abnormalities at baseline and year 3 are shown in Figures 1 and 2. At baseline, 87 subjects (16.4%) had at least one major ECG abnormality and 131 (24.6%) had at least one minor abnormality. The most prevalent major ECG abnormalities at baseline were major Q waves (MC 1-1, 1-2), observed in 41 (7.7%) subjects, deep T-wave inversion (MC 5-1, 5-2) in 17 (3.1%) subjects, and atrial enlargement (MC 9-3, 9-6) in 18 (3.3%) subjects. The most frequent minor ECG abnormalities at baseline were axis deviation (MC 2-1, 2-2) in 58 (10.9%) subjects, moderate T-wave inversion (MC 5-3) in 34 (6.4%) subjects, incomplete bundle branch block (MC 7-3, 7-6) in 22 (4.1%) subjects, 1st degree atrioventricular block (MC 6-3) in 19 (3.6%) subjects and borderline Q waves (MC 1-3) in 18 (3.6%) of subjects.
Figure 1. Prevalence of Major and Minor ECG Abnormalities.
The percentage of subjects with major and minor ECG abnormalities at baseline (blue) and at year 3 (red). Year 3 percentages include both continued smokers and those who abstained. All p-values for comparisons between baseline and year 3 ECG abnormalities were non-significant (<0.05). ECG = electrocardiogram. *Subjects may have had more than one major and/or minor ECG abnormalities. Each abnormality was tallied separately.doi:10.1371/journal.pone.0062311.g001
Figure 2. (A). Prevalence of Specific Major ECG Abnormalities. (B). Prevalence of Specific Minor ECG Abnormalities.
Figure 2A shows percentages of the specific major ECG abnormalities and figure 2B shows percentages of the specific minor ECG abnormalities at baseline (blue) and at year 3 (red). Year 3 percentages include both continued smokers and those who abstained. All p-values for comparisons between baseline and year 3 ECG abnormalities were non-significant (<0.05). BBB = Bundle Branch Block; AV = Atrioventricular.doi:10.1371/journal.pone.0062311.g002
ECG Abnormalities and their Relationship with Smoking Parameters
Greater pack-years was strongly associated with major ECG abnormalities (p = 0.001; OR = 1.21, 95% CI = 1.07–1.36 per 10 pack-years), however cpd (p = 0.150; OR = 1.22; 95% CI = 0.92–1.61 per 10 cpd) and exhaled CO (p = 0.379; OR = 1.07, 95% CI = 0.92–1.25 per 10 ppm) were not. After adjusting for age, sex, race, education, body-mass index, fasting glucose, and lipids, greater pack-years remained associated with major ECG criteria (p = 0.016; OR = 1.23, 95% CI = 1.03–1.47 per 10 pack-years). After adjusting for confounders, there were no significant associations with minor ECG abnormalities and smoking burden with respect to cpd (p = 0.46; OR = 1.11, 95% CI = 0.84–1.45 per 10 cpd), exhaled CO (p = 0.52; OR = 1.05; 95% CI = 0.92–1.21 per 10 ppm) or pack-years (p = 0.70; OR = 1.03; 95% CI = 0.88–1.20 per 10 pack-years). Results were similar for the unadjusted analyses. To ensure that the association between pack-years and major ECG changes was not simply due to atrial enlargement (MC 9-3, 9-6) since this was included as a major criteria in the pre-specified analysis plan but not the pooling project , the analyses were performed including atrial enlargement as minor ECG criteria. Still, major ECG abnormalities were significantly associated with pack-years (p = 0.001; OR = 1.21, 95% CI = 1.08–1.36 per 10 pack-years) and minor criteria were not (p = 0.14; OR = 1.08, 95% CI = 0.98–1.19 per 10 pack-years).
Longitudinal Changes in ECG Abnormalities after a Quit Attempt
Of the 354 subjects with ECGs at year 3, abstinence from smoking was confirmed in 153 (43%) participants by CO level <10 ppm. As is often observed in longitudinal smoking cessations studies , , there was moderate attrition after 3 years with 119 (34%) subjects not returning for that visit. Several important differences between the baseline and year 3 data were observed amongst those completing follow-up. Subjects who followed up at year 3 were more likely to be white (p = 0.004) and on average, were 3.6 kg heavier (p<0.001) and had 2.9 cm larger waist circumferences (p<0.001). Additionally, those who followed up had fasting glucose and hemoglobin A1C levels that were 4.5 mg/dL and 0.26% higher respectively (both p<0.001). Systolic and diastolic blood pressures were 4.3 and 7.0 mmHg lower in those who followed up (both p<0.001), and a higher percentage were being treated with antihypertensive medications (p<0.001). A higher percentage of subjects who were followed up at year 3 were on lipid-lowering therapy (p<0.001), although there was not a significant difference in LDL-cholesterol levels (Table 1).
Major Q waves (MC 1-1, 1-2) also were the most frequent major ECG abnormality at year 3 (22 [6.2%] subjects), followed by atrial enlargement (MC 9-3, 9-6; 15 [4.2%] subjects) and deep T wave inversion (MC 5-1, 5-2; 8 [2.3%] subjects) (Figure 2A). Axis deviation (MC 2-1, 2-2; 40 [11.3%] subjects) and moderate t-wave inversion (MC 5-3; 16 [4.5%] subjects) remained the most common minor ECG changes at year 3 (Figure 2B). New major and minor ECG criteria were observed in 7.2% and 15.6% of subjects respectively, but in a similar number of abstinent subjects and continuing smokers (p>0.2 for both, Table 2). Abstinent subjects gained significantly more weight than subjects who continued to smoke over the 3 year follow up period (5.95 [9.2] kg vs. 1.74 [7.8] kg, p<0.001). Compared to baseline, continuing smokers at year 3 smoked significantly fewer cigarettes (-8.4 [8.8] cpd) (p<0.001). After adjusting for baseline pack-years, multinomial regression models revealed no differences in progression (p = 0.727; OR = 1.17, 95% CI = 0.48–2.82) or regression (p = 0.081; OR = 2.26, 95% CI = 0.91–5.47) of major ECG abnormalities in smokers and abstainers. Similarly, for minor ECG abnormalities, there were no differences in progression (p = 0.665; OR = 1.16, 95% CI = 0.59–2.31 or regression (p = 0.610; OR = 1.20, 95% CI = 0.60–2.41) between smokers and abstainers at year 3. Additionally, no sex or racial differences were observed.
In the general population, major and minor ECG changes predict increased mortality , . Individuals who smoke are more likely to have ECG findings consistent with ischemic heart disease , structural heart disease , and cardiac rhythm disorders . Despite, on average, being nearly a decade younger than participants in population-based studies, the smokers in our study had notably more major ECG abnormalities (especially major Q waves) than has been observed in the general population , –. Participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study, a large epidemiological study that reported a cross-sectional evaluation of ECG abnormalities in subjects of a comparable age to those in our cohort, had 60% fewer major Q wave findings (MC 1-1 and 1-2) . Only 14% of comparable CARDIA subjects were current smokers, indicating that smokers have a higher prevalence of major Q wave findings . Indeed, in CARDIA, active smokers had a higher prevalence of major and minor Q-waves than non-smokers, though no information regarding the smoking burden of CARDIA subjects was reported . In larger epidemiological studies that evaluated ECG abnormalities, the majority of subjects studied were one to two decades older that those in our study. In those studies, a minority of subjects were active smokers , , , ,  or smokers were excluded from the analyses . The ECG abnormalities in younger smokers have not been well-described and are in need of further study. Considering that age is one of the strongest predictors of 10-year CVD risk  the age-independent association between baseline major Q-waves and smoking burden in pack-years supports the idea that increased lifetime smoking burden increases CVD risk and that increased CVD risk related to long-term smoking can be detected by the 12-lead ECG. There was no independent relationship between ECG abnormalities and cpd at baseline, however sudden cardiac death in smokers also has been associated with the overall duration of smoking, but not cpd .
While there were fewer minor ECG abnormalities in those who quit smoking, we did not observe a significant difference in major or minor ECG findings at year 3. This is not unexpected since Q-waves, our most common major finding, are indicative of myocardial scar which is unlikely to resolve during our 3 year follow up period . It is possible that the length of follow up was inadequate to detect differences in ECG progression or regression. Still, prior studies have shown that with improved blood pressure control, ECG abnormalities can regress within 2–5 years , . Longitudinal data regarding ECG changes after a quit attempt have not been described previously. Also, continuing smokers in our study were smoking significantly fewer cigarettes than they were at baseline and this group gained less weight then abstainers, which would be expected to attenuate the effects of smoking on ECG abnormalities. Additionally, there was a significant increase in use of antihypertensive medications and lipid-lowering therapy at year 3, compared to baseline. Treatment of known CVD risk factors could decrease the likelihood of developing ECG abnormalities at year 3 regardless of smoking status. For example, the prevalence of ECG abnormalities suggestive of left ventricular strain, such as T-wave inversion and LVH, slightly decreased at year 3 in both groups. This is more likely due to improvements in blood pressure rather than changes in smoking , .
Because this was a randomized clinical trial of smoking cessation interventions, there are no nonsmoking controls for comparison. In smoking cessation studies, it is very common for subjects who relapse to drop out or miss follow-up visits , . In our study, 34% of subjects did not have a 3-year ECG, which is consistent with the 30–43% one year drop-out rates reported in recent clinical trials of smoking cessation pharmacotherapies , . There were differences in participants that did not return for follow up with regard to age, and race which may introduce selection bias. Though, those who did not attend follow-up smoked a similar number of cigarettes/day at baseline , . The three year follow up period in this study may be too short to detect progression or regression of some ECG changes , . Finally, although ECG changes are associated with increased mortality, hard CVD outcomes including death, were not assessed in this study population.
Even in relatively young adults, major ECG abnormalities are independently associated with smoking burden. Minor ECG abnormalities were not associated with current or past smoking burden. After 3 years, quitting smoking was not associated with a decrease in ECG abnormalities; however those who abstained gained significantly more weight and continuing smokers smoked fewer cpd than at the inception of the study. Longer follow-up likely is needed to identify the effects of quitting versus continued smoking on the ECG and CVD risk.
The authors would like to thank the staff of the UW Center for Tobacco Research and Intervention and the UW Atherosclerosis Imaging Research Program, as well as all of the participants in this study.
Conceived and designed the experiments: ADG MCF TBB JHS. Performed the experiments: MEP MCF TBB. Analyzed the data: ADG MEP MAL JHS. Contributed reagents/materials/analysis tools: MEP AA. Wrote the paper: ADG JHS.
- 1. (2008) Smoking-attributable mortality, years of potential life lost, and productivity losses–United States, 2000–2004. MMWR Morb Mortal Wkly Rep 57: 1226–1228.
- 2. Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, et al. (2009) Heart disease and stroke statistics–2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 119: e21–181. doi: 10.1161/circulationaha.108.191261
- 3. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, et al. (2004) Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 364: 937–952. doi: 10.1016/s0140-6736(04)17018-9
- 4. FitzGerald GA, Oates JA, Nowak J (1988) Cigarette smoking and hemostatic function. Am Heart J 115: 267–271. doi: 10.1016/0002-8703(88)90648-5
- 5. Johnson HM, Gossett LK, Piper ME, Aeschlimann SE, Korcarz CE, et al. (2010) Effects of smoking and smoking cessation on endothelial function: 1-year outcomes from a randomized clinical trial. J Am Coll Cardiol 55: 1988–1995. doi: 10.1016/s0735-1097(10)61436-2
- 6. Crow RS, Prineas RJ, Hannan PJ, Grandits G, Blackburn H (1997) Prognostic associations of Minnesota Code serial electrocardiographic change classification with coronary heart disease mortality in the Multiple Risk Factor Intervention Trial. Am J Cardiol 80: 138–144. doi: 10.1016/s0002-9149(97)00307-x
- 7. Auer R, Bauer DC, Marques-Vidal P, Butler J, Min LJ, et al. (2012) Association of major and minor ECG abnormalities with coronary heart disease events. JAMA 307: 1497–1505. doi: 10.1001/jama.2012.434
- 8. Liao YL, Liu KA, Dyer A, Schoenberger JA, Shekelle RB, et al. (1988) Major and minor electrocardiographic abnormalities and risk of death from coronary heart disease, cardiovascular diseases and all causes in men and women. J Am Coll Cardiol 12: 1494–1500. doi: 10.1016/s0735-1097(88)80016-0
- 9. De Bacquer D, Martins Pereira LS, De Backer G, De Henauw S, Kornitzer M (1994) The predictive value of electrocardiographic abnormalities for total and cardiovascular disease mortality in men and women. Eur Heart J 15: 1604–1610.
- 10. Ambrose JA, Barua RS (2004) The pathophysiology of cigarette smoking and cardiovascular disease: an update. J Am Coll Cardiol 43: 1731–1737. doi: 10.1016/j.jacc.2003.12.047
- 11. Waters D, Lesperance J, Gladstone P, Boccuzzi SJ, Cook T, et al. (1996) Effects of cigarette smoking on the angiographic evolution of coronary atherosclerosis. A Canadian Coronary Atherosclerosis Intervention Trial (CCAIT) Substudy. CCAIT Study Group. Circulation 94: 614–621. doi: 10.1161/01.cir.94.4.614
- 12. Gepner AD, Piper ME, Johnson HM, Fiore MC, Baker TB, et al. (2011) Effects of smoking and smoking cessation on lipids and lipoproteins: outcomes from a randomized clinical trial. Am Heart J 161: 145–151. doi: 10.1016/j.ahj.2010.09.023
- 13. Piper ME, Smith SS, Schlam TR, Fiore MC, Jorenby DE, et al. (2009) A randomized placebo-controlled clinical trial of 5 smoking cessation pharmacotherapies. Arch Gen Psychiatry 66: 1253–1262. doi: 10.1001/archgenpsychiatry.2009.142
- 14. Asthana A, Piper ME, McBride PE, Ward A, Fiore MC, et al.. (2012) Long-term effects of smoking and smoking cessation on exercise stress testing: three-year outcomes from a randomized clinical trial. Am Heart J 163: 81–87 e81.
- 15. Prineas RJ, Crow RS, Zhang Z-m (2009) The Minnesota Code Manual of Electrocardiographic Findings: Springer. 344 p.
- 16. Relationship of blood pressure, serum cholesterol, smoking habit, relative weight and ECG abnormalities to incidence of major coronary events: final report of the pooling project. The pooling project research group. J Chronic Dis 31: 201–306. doi: 10.1016/0021-9681(78)90073-5
- 17. Jorenby DE, Hays JT, Rigotti NA, Azoulay S, Watsky EJ, et al. (2006) Efficacy of varenicline, an alpha4beta2 nicotinic acetylcholine receptor partial agonist, vs placebo or sustained-release bupropion for smoking cessation: a randomized controlled trial. JAMA 296: 56–63. doi: 10.1001/jama.296.1.56
- 18. Gonzales D, Rennard SI, Nides M, Oncken C, Azoulay S, et al. (2006) Varenicline, an alpha4beta2 nicotinic acetylcholine receptor partial agonist, vs sustained-release bupropion and placebo for smoking cessation: a randomized controlled trial. JAMA 296: 47–55. doi: 10.1517/14656522.214.171.12499
- 19. De Bacquer D, De Backer G, Kornitzer M, Blackburn H (1998) Prognostic value of ECG findings for total, cardiovascular disease, and coronary heart disease death in men and women. Heart 80: 570–577.
- 20. Nabipour I, Amiri M, Imami SR, Jahfari SM, Nosrati A, et al. (2008) Unhealthy lifestyles and ischaemic electrocardiographic abnormalities: the Persian Gulf Healthy Heart Study. East Mediterr Health J 14: 858–868.
- 21. (1983) U.S. Department of Health and Human Services. The Health Consequences of Smoking: Cardiovascular Disease. A Report of the Surgeon General. In: U.S. Department of Health and Human Services PHS, Office on Smoking and Health, editor. DHHS Publication No (PHS) 84–50204.
- 22. Benjamin EJ, Levy D, Vaziri SM, D’Agostino RB, Belanger AJ, et al. (1994) Independent risk factors for atrial fibrillation in a population-based cohort. The Framingham Heart Study. JAMA 271: 840–844. doi: 10.1001/jama.1994.03510350050036
- 23. De Bacquer D, De Backer G, Kornitzer M (2000) Prevalences of ECG findings in large population based samples of men and women. Heart 84: 625–633. doi: 10.1136/heart.84.6.625
- 24. Cedres BL, Liu K, Stamler J, Dyer AR, Stamler R, et al. (1982) Independent contribution of electrocardiographic abnormalities to risk of death from coronary heart disease, cardiovascular diseases and all causes. Findings of three Chicago epidemiologic studies. Circulation 65: 146–153. doi: 10.1161/01.cir.65.1.146
- 25. Machado DB, Crow RS, Boland LL, Hannan PJ, Taylor HA Jr, et al. (2006) Electrocardiographic findings and incident coronary heart disease among participants in the Atherosclerosis Risk in Communities (ARIC) study. Am J Cardiol 97: 1176–1181. doi: 10.1016/j.amjcard.2005.11.036
- 26. Walsh JA, 3rd, Prineas R, Daviglus ML, Ning H, Liu K, et al. (2010) Prevalence of electrocardiographic abnormalities in a middle-aged, biracial population: Coronary Artery Risk Development in Young Adults study. J Electrocardiol 43: 385 e381–389.
- 27. Lloyd-Jones DM, Walsh JA, Prineas RJ, Ning H, Liu K, et al. (2009) Association of electrocardiographic abnormalities with coronary artery calcium and carotid artery intima-media thickness in individuals without clinical coronary heart disease (from the Multi-Ethnic Study of Atherosclerosis [MESA]). Am J Cardiol 104: 1086–1091. doi: 10.1016/j.amjcard.2009.05.060
- 28. Zhang ZM, Prineas RJ, Eaton CB (2010) Evaluation and comparison of the Minnesota Code and Novacode for electrocardiographic Q-ST wave abnormalities for the independent prediction of incident coronary heart disease and total mortality (from the Women's Health Initiative). Am J Cardiol 106: 18–25 e12.
- 29. Vitelli LL, Crow RS, Shahar E, Hutchinson RG, Rautaharju PM, et al. (1998) Electrocardiographic findings in a healthy biracial population. Atherosclerosis Risk in Communities (ARIC) Study Investigators. Am J Cardiol 81: 453–459. doi: 10.1016/s0002-9149(97)00937-5
- 30. Berger JS, Jordan CO, Lloyd-Jones D, Blumenthal RS (2010) Screening for cardiovascular risk in asymptomatic patients. J Am Coll Cardiol 55: 1169–1177. doi: 10.1016/j.jacc.2009.09.066
- 31. Wachtell K, Okin PM, Olsen MH, Dahlof B, Devereux RB, et al. (2007) Regression of electrocardiographic left ventricular hypertrophy during antihypertensive therapy and reduction in sudden cardiac death: the LIFE Study. Circulation 116: 700–705. doi: 10.1161/circulationaha.106.666594
- 32. Bolli P, Burkart F, Vesanen K, Baker JL, Pinto M, et al. (1987) Electrocardiographic changes during antihypertensive therapy in the International Prospective Primary Prevention Study in Hypertension. Hypertension 9: III69–74. doi: 10.1161/01.hyp.9.6_pt_2.iii69
- 33. de Simone G, Okin PM, Gerdts E, Olsen MH, Wachtell K, et al. (2009) Clustered metabolic abnormalities blunt regression of hypertensive left ventricular hypertrophy: the LIFE study. Nutr Metab Cardiovasc Dis 19: 634–640. doi: 10.1016/j.numecd.2008.12.012