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Published in final edited form as: Arch Gerontol Geriatr. 2011 Jul 6;55(1):85–90. doi: 10.1016/j.archger.2011.05.027

Association between smoking and outcomes in older adults with atrial fibrillation

Pushkar P Pawar a, Linda G Jones b,a, Margaret Feller a, Jason L Guichard a, Marjan Mujib a, Mustafa I Ahmed a, Brita Roy a, Toufiqur Rahman a, Inmaculada B Aban a, Thomas E Love c, Michel White d, Wilbert S Aronow e, Gregg C Fonarow f, Ali Ahmed a,b,*
PMCID: PMC3358565  NIHMSID: NIHMS303128  PMID: 21733581

Abstract

Tobacco smoking is a risk factor for atrial fibrillation (AF), but little is known about the impact of smoking in patients with AF. Of the 4060 patients with recurrent AF in the Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) trial, 496 (12%) reported having smoked during the past two years. Propensity scores for smoking were estimated for each of the 4060 patients using a multivariable logistic regression model and were used to assemble a matched cohort of 487 pairs of smokers and nonsmokers, who were balanced on 46 baseline characteristics. Cox and logistic regression models were used to estimate the associations of smoking with all-cause mortality and all-cause hospitalization, respectively, during over 5 years of follow-up. Matched participants had a mean age of 70 ± 9 years (± S.D.), 39% were women, and 11% were non-white. All-cause mortality occurred in 21% and 16% of matched smokers and nonsmokers, respectively (when smokers were compared with nonsmokers, hazard ratio = HR = 1.35; 95% confidence interval = 95% CI = 1.01–1.81; p = 0.046). Unadjusted, multivariable-adjusted and propensity-adjusted HR (95% CI) for all-cause mortality associated with smoking in the pre-match cohort were: 1.40 (1.13–1.72; p = 0.002), 1.45 (1.16–1.81; p = 0.001), and 1.39 (1.12–1.74; p = 0.003), respectively. Smoking had no association with all-cause hospitalization (when smokers were compared with nonsmokers, odds ratio = OR = 1.21; 95% CI = 0.94–1.57, p = 0.146). Among patients with AF, a recent history of smoking was associated with an increased risk of all-cause mortality, but had no association with all-cause hospitalization.

Keywords: Atrial fibrillation, Smoking, Mortality, Propensity score

1. Introduction

Tobacco smoking is a risk factor for cardiovascular disease (CVD) and continuing smoking is associated with poor outcomes in those with CVD (Aronow, 1971, 1978; Aronow et al., 1974; Suskin et al., 2001; Yatsuya et al., 2010). AF is also common and is associated with poor outcomes (Aronow et al., 1989). Smoking has also been described as a risk factor for AF (Goette et al., 2007; Heeringa et al., 2008). However, little is known about the impact of smoking on outcomes in AF. The objective of the current study is to evaluate the association between smoking and outcomes in the patients with AF.

2. Subjects and methods

2.1. Data source and patients

The AFFIRM was a randomized clinical trial to compare two treatment strategies in patients with AF, the details of which have been described before (The AFFIRM Investigators, 2002; Wyse et al., 2002). Briefly, 4060 patients with recurrent AF were enrolled between November 1995 and October 1999 from 213 centers and were randomized to either rate control (n = 2027) or rhythm control (n = 2033) groups. We used a public-use copy of the AFFIRM data obtained from the National Heart, Lung and Blood Institute, which also funded the study. Of the 4060 AFFIRM participants, 496 (12%) self-reported a baseline history of smoking during the past two years. Data on demographic and clinical variables including past medical history, medication use, and symptoms during AF were also collected at baseline.

2.2. Outcomes

The primary outcomes for the current analysis is all-cause mortality during about 6 years of follow-up, also the primary end point of AFFIRM. A secondary outcome was all-cause hospitalization. All events were centrally adjudicated by committees blinded to randomization.

2.3. Propensity score matching

We used propensity score matching to assemble a cohort of smokers and non-smokers who would be balanced on all measured baseline characteristics (Rosenbaum and Rubin, 1983; Rubin, 2001). We estimated propensity scores for smoking for each of the 4060 patients using a non-parsimonious multivariable logistic regression model (Ahmed et al., 2006a,b, 2007, 2008; Ahmed and Aronow, 2008; Alper et al., 2009; Ekundayo et al., 2009a,b; Wahle et al., 2009, Bowling et al., 2010). In the model, we used baseline smoking as the dependent variable, and 46 other baseline characteristics displayed in Figure 1 were entered as covariates. Using a 1 to 1 greedy matching algorithm, we were able to match 487 (98% of the 496) smokers with 487 non-smokers who had similar propensity scores. Pre- and post-match absolute standardized differences are presented as Love plots (Figure 1) (Ahmed et al., 2006a,b, 2007, 2008; Ahmed and Aronow, 2008; Alper et al., 2009; Ekundayo et al., 2009; Wahle et al., 2009; Bowling et al., 2010). An absolute standardized difference of 0% on a covariate indicates no residual bias for that covariate, and values < 10% suggest inconsequential bias (Normand, 2001; Austin, 2008).

Figure 1.

Figure 1

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Absolute standardized differences between baseline characteristics of smoker versus non-smoker older atrial fibrillation patients, before and after propensity score matching

2.4. Statistical analysis

The baseline characteristics of patients with and without a recent history of smoking were compared using Pearson’s Chi-square and Wilcoxon rank-sum tests for the pre-match cohort and McNemar’s test and paired sample t-tests for the post-match cohort as appropriate. The association of smoking with mortality was estimated using Kaplan–Meier survival analysis and Cox regression analysis. We repeated our analysis in the full pre-match cohort of 4060 participants using three different approaches: unadjusted, propensity score adjusted and multivariable adjusted. The multivariable model was adjusted for all covariates used in the propensity score model. Finally, we conducted subgroup analyses and tested interactions to determine any heterogeneity in the association between smoking and all-cause mortality. A formal sensitivity analysis was conducted to quantify the degree of a hidden bias that could invalidate our main conclusions (Rosenbaum, 2002). Due to lack of detailed time to event data for all-cause hospitalization, we used logistic regression to estimate the association of smoking with all-cause hospitalization. The mean follow-up time for smokers and nonsmokers was similar (3.4 years each). All tests were two-tailed, and a p =< 0.05 was considered to be statistically significant. SPSS for Windows (Version 18) was used for data analysis.

3. Results

3.1. Patient characteristics

Matched participants had a mean age of 70 ± 9 years, 39% were women, and 11% were non-white. Before matching, compared with nonsmokers, smokers were younger, more likely to be female, and were also more likely to have peripheral arterial disease and pulmonary disease (Table 1). These and other baseline imbalances were balanced after matching (Table 1 and Figure 1).

Table 1.

Baseline patient characteristics by history of smoking before and after propensity matching, mean ± S.D., n (%)

Variable Before propensity matching After propensity matching
Nonsmokers (n = 3564) Smokers (n = 496) p Nonsmokers (n = 487) Smoker (n = 487) p
Age, years 70 ± 8 65 ± 8 <0.001 65 ± 9 65 ± 8 1.000
Female 1472 (41) 122 (25) <0.001 122 (25) 120 (25) 0.935
Non-whites 392 (11) 69 (14) 0.055 69 (14) 66 (14) 0.852
NYHAa Class II or higher 306 (9) 65 (13) 0.001 63 (13) 64 (13) 1.000
Randomization to rate control 1778 (50) 255 (51) 0.525 244 (50) 253 (52) 0.614
Past medical history
 Hypertension 2531 (71) 345 (70) 0.503 331 (68) 337 (69) 0.735
 Coronary artery disease 1339 (38) 212 (43) 0.026 214 (44) 204 (42) 0.566
 Heart failure 783 (22) 156 (32) <0.001 145 (30) 149 (31) 0.822
 Diabetes mellitus 709 (20) 104 (21) 0.575 107 (22) 101 (21) 0.698
 Cerebrovascular events 464 (13) 78 (16) 0.097 80 (16) 76 (16) 0.789
 Pulmonary disease 453 (13) 138 (28) <0.001 126 (26) 130 (27) 0.813
 Valvular heart disease 439 (12) 65 (13) 0.618 68 (14) 62 (13) 0.651
 Symptomatic bradycardia 253 (7) 30 (6) 0.389 30 (6) 30 (6) 1.000
 Peripheral arterial disease 215 (6) 67 (14) <0.001 63 (13) 62 (13) 1.000
 Hepatic/renal disease 201 (6) 30 (6) 0.713 28 (6) 29 (6) 1.000
 Cardioversion 1464 (41) 220 (44) 0.165 214 (44) 214 (44) 1.000
 Coronary bypass surgery 457 (13) 51 (10) 0.109 53 (11) 51 (11) 0.914
 Pacemaker implantation 225 (6) 25 (5) 0.269 23 (5) 25 (5) 0.885
 Hospitalization for arrhythmia 1642 (46) 253 (51) 0.039 253 (52) 247 (51) 0.752
Symptoms during atrial fibrillationb
 Fatigue 1982 (56) 284 (57) 0.489 274 (56) 280 (58) 0.745
 Dyspnea 1896 (53) 307 (62) <0.001 288 (59) 299 (61) 0.499
 Palpitation 1741 (49) 267 (54) 0.038 250 (51) 259 (53) 0.615
 Dizziness 1200 (34) 192 (39) 0.027 192 (39) 189 (39) 0.897
 Chest pain 833 (23) 137 (28) 0.038 142 (29) 129 (27) 0.397
 Diaphoresis 702 (20) 132 (27) <0.001 133 (27) 128 (26) 0.778
 Leg swelling 671 (19) 105 (21) 0.214 104 (21) 103 (21) 1.000
 Orthopnea 513 (14) 96 (19) 0.004 75 (15) 93 (19) 0.145
 Paroxysmal nocturnal dyspnea 254 (7) 54 (11) 0.003 46 (9) 50 (10) 0.744
 Panic 360 (10) 67 (14) 0.020 75 (15) 67 (14) 0.519
 Syncope 152 (4) 15 (3) 0.192 17 (4) 15 (3) 0.860
 Other symptoms 370 (10) 43 (9) 0.237 34 (7) 43 (9) 0.321
Medications
 Warfarin 3020 (85) 414 (84) 0.464 401 (82) 407 (84) 0.656
 Digoxin 1863 (52) 290 (59) 0.010 269 (55) 284 (58) 0.342
 Beta-blocker 1521 (43) 206 (42) 0.629 200 (41) 200 (41) 1.000
 Diuretics 1510 (42) 222 (45) 0.313 221 (45) 216 (44) 0.799
 ACEc inhibitors 1355 (38) 226 (46) 0.001 209 (43) 219 (45) 0.565
 Diltiazem 1083 (30) 169 (34) 0.096 173 (36) 165 (34) 0.642
 Verapamil 372 (10) 53 (11) 0.866 54 (11) 53 (11) 1.000
 Aspirin 944 (27) 137 (28) 0.592 127 (26) 134 (28) 0.669
 Lipid lowering agents 796 (22) 117 (24) 0.531 114 (23) 116 (24) 0.937
 Nitrate 656 (18) 106 (21) 0.113 95 (20) 101 (21) 0.693
 Heparin 608 (17) 109 (22) 0.007 101 (21) 105 (22) 0.811
Heart rate per minute 73 ± 14 74 ± 15 0.081 74 ± 15 74 ± 15 0.619
Systolic blood pressure, mm Hg 135 ± 19 132 ± 19 <0.001 131 ± 18 132 ± 19 0.672
Diastolic blood pressure, mm Hg 76 ± 10 76 ±10 0.148 76 ±10 76 ± 10 0.842
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a

NYHA—New York Heart Association.

b

Symptoms associated with clinical diagnosis of atrial fibrillation.

c

ACE—angiotensin-converting enzyme.

3.2. Association of age and mortality

All-cause mortality occurred in 21% and 16% of matched smokers and nonsmokers, respectively (when smokers were compared with nonsmokers, hazard ratio = HR = 1.35; 95% confidence interval (CI) = 1.01–1.81; p = 0.046; Table 2 and Figure 2). In the absence of a hidden confounder, a sign-score test for matched data with censoring provides strong evidence (p < 0.0001) that non-smokers clearly outlived smokers. A hidden covariate that would be a near- perfect predictor of mortality could potentially explain away this association if it also increases the odds ratio = (OR) of smoking by 28.5%. The association between smoking and mortality was homogeneous across various subgroups of patients (Figure 3). Unadjusted, multivariable-adjusted and propensity-adjusted HR (95% CI) for all-cause mortality associated with smoking in the pre-match cohort were: 1.40 (1.13–1.72; p = 0.002), 1.45 (1.16–1.81; p = 0.001), and 1.39 (1.12–1.74; p = 0.003), respectively (Table 2). Smoking had no association with all-cause hospitalization (when smokers were compared with nonsmokers, OR = 1.21; 95% CI = 0.94– 1.57; p = 0.146).

Table 2.

Association of history of smoking with all cause mortality

All-cause mortality Events (%)
Absolute risk difference (%)a Hazard ratio (95%CI) p
Nonsmokers Smokers
Pre-match n = 3564 n = 496
 Unadjusted 560 (16%) 106 (21%) + 5 1.40 (1.13–1.72) 0.002
 Multivariable-adjusted --- --- -- 1.45 (1.16–1.81) 0.001
 Propensity-adjusted --- --- --- 1.39 (1.12–1.74) 0.003
Post-match n = 487 n = 487
 Propensity-matched 78 (16%) 103 (21%) + 5 1.35 (1.01–1.81) 0.046
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a

Absolute risk differences were calculated by subtracting the events in non-smokers from those in smokers.

Figure 2.

Figure 2

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Kaplan-Meier plot for all-cause mortality (HR=hazard ratio; CI=confidence interval)

Figure 3.

Figure 3

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Association of recent smoking history with all-cause mortality in subgroups of propensity score-matched participants in the AFFIRM (HR = hazard ratio; CI = confidence interval)

4. Discussion

4.1. Key findings

Findings from the current study demonstrate that among patients with recurrent AF, a history of recent smoking was associated with increased all-cause mortality but had no association with all-cause hospitalization. The increased risk of death without an associated increased risk of hospitalization suggests that smoking-associated mortality may have been sudden in nature. Findings from our study suggest that in patients with AF, smoking continues to have an adverse effect on mortality. These findings are important as AF is common among older adults and is one of the few CVD with increasing prevalence.

4.2. Explanation of study findings

The observed association between smoking and mortality may be explained by residual or unmeasured confounding or it may imply a true association. The magnitude of the unadjusted association between smoking and mortality was rather similar to that observed after multivariable risk adjustment and propensity matching suggesting that despite many imbalances in measured baseline covariates between smokers and nonsmokers, they played minimal confounding roles. This is probably due to fact that the confounders were equally shared by smokers and non-smokers. For example, compared to smokers, nonsmokers were older and compared to nonsmokers, smokers were more likely to be male and have heart failure. It is also possible that the observed association between smoking and mortality may be due to an unmeasured confounder. However, findings from our sensitivity analysis suggest that this association was rather insensitive to an unmeasured confounder. The consistent finding of a strong, significant and rather insensitive association suggests that smoking may have an intrinsic association with mortality in patients with AF.

4.3. Literature comparison

Smoking is known to be associated with poor prognosis in patients with established CVD (Aronow, 1971; Aronow et al., 1998; Suskin et al., 2001). Smoking has been shown to inflict damage on the cardiovascular system through diverse mechanisms that include platelet dysfunction, hypercoagulative effect, lipid peroxidation, increased myocardial oxygen demand and vasomotor dysfunction (Ambrose and Barua, 2004). Smoking has also been shown to be associated with myocardial ischemia, ventricular arrhythmias, and sudden cardiac death (Davis et al., 1984; Hayano et al., 1990; Kinoshita et al., 2009).

4.4. Clinical and public health implications

Although we had no data on cause-specific mortality or hospitalization, the lack of an association between smoking and hospitalization despite a strong association with all-cause mortality suggests that smoking-related deaths may have been sudden in nature thus precluding hospitalization. Although the prevalence of coronary artery disease (CAD) was balanced in the post-match cohort, it is possible that smokers had more severe CAD or for longer duration. Interestingly, findings from our subgroup analysis suggest that the effect of smoking was rather homogeneous regardless of a history of CAD. Findings from our subgroup analyses also suggest that smoking had no association with mortality in the patients with AF who were receiving beta-blockers or were randomized to rate control strategy. Nearly 70% of the patients in the rate control group received beta-blockers at any time (vs. 50% for those in the rhythm control group) (Wyse et al., 2002). Although there was no significant interaction, these findings may have clinical implications as smoking-associated hemodynamic changes have been shown to be mediated through adrenergic mechanisms, which has been shown to be prevented by adrenergic blockade (Cryer et al., 1976; Hung et al., 1995).

4.5. Limitations

There are a few limitations to our study, which should be acknowledged. We had no data on the number of pack-years of smoking or passive smoking exposure. It is also possible that some of smokers may have quit during follow-up while some non-smokers became smokers. However, this regression dilution may have underestimated the true associations observed in our study (Clarke et al., 1999).

5. Conclusion

In the patients with AF, a recent history of smoking was associated with increased mortality but had no association with hospitalization. Patients with AF should be informed of these risks, encouraging smokers to quit and nonsmokers to maintain abstinence.

Acknowledgments

The AFFIRM is conducted and supported by the NHLBI in collaboration with the AFFIRM Investigators. This manuscript was prepared using a limited access dataset obtained from the NHLBI and does not necessarily reflect the opinions or views of the AFFIRM or the NHLBI.

Dr. A. Ahmed is supported by the National Institutes of Health through grants (R01-HL085561 and R01-HL097047) from the National Heart, Lung, and Blood Institute and a generous gift from Ms. Jean B. Morris of Birmingham, Alabama.

Footnotes

Conflict of interest statement: None.

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