Atrial fibrillation (AF) is an independent predictor of mortality after acute myocardial infarction (AMI). We analyzed the relation between biomarkers linked to myocardial stretch (NT-pro-brain natriuretic peptide [NT-proBNP]), myocardial damage (Troponin-T [TnT]), and inflammation (high-sensitivity C-reactive protein [hs-CRP]) and new-onset AF during AMI to identify patients at high risk for AF. In a prospective multicenter registry of AMI patients (from the Translational Research Investigating Underlying disparities in recovery from acute Myocardial infarction: Patients’ Health status registry), we measured NT-proBNP, TnT, and hs-CRP in patients without a history of AF (n = 2,370). New-onset AF was defined as AF that occurred during the index hospitalization. Hierarchical multivariate logistic regression models were used to determine the association of biomarkers with new-onset AF, after adjusting for other covariates. New-onset AF was documented in 114 patients with AMI (4.8%; mean age 58 years; 32% women). For each twofold increase in NT-proBNP, there was an 18% increase in the rate of AF (odds ratio [OR] 1.18, 95% confidence interval [CI] 1.03 to 1.35; p <0.02). Similarly, for every twofold increase in hs-CRP, there was a 15% increase in the rate of AF (OR 1.15, 95% CI 1.02 to 1.30; p = 0.02). TnT was not independently associated with new-onset AF (OR 0.96, 95% CI 0.85 to 1.07; p = 0.3). NT-proBNP and hs-CRP were independently associated with new in-hospital AF after MI, in both men and women, irrespective of race. Our study suggests that markers of myocardial stretch and inflammation, but not the amount of myocardial necrosis, are important determinants of AF in the setting of AMI.
Because acute myocardial infarction (AMI) is commonly associated with elevated left ventricular (LV) filling pressures, myocardial necrosis, and a generalized inflammatory state, NT-pro-brain natriuretic peptide (NT-proBNP), Troponin-T (TnT), and high-sensitivity C-reactive protein (hs-CRP) have proved useful in predicting prognosis and clinical outcome after AMI. It is plausible that NT-proBNP, TnT, and hs-CRP would represent distinct pathophysiologic pathways to develop new-onset atrial fibrillation (AF) in patients with AMI. We therefore examined the association of NT-proBNP, TnT, and hs-CRP with new-onset AF in hospitalized patients with AMI to potentially identify those patients at high risk of developing new-onset AF in the setting of an AMI.
Methods
All patients were participants in the Translational Research Investigating Underlying disparities in recovery from acute Myocardial infarction: Patients’ Health status registry, a multicenter prospective cohort of patients with AMI from 24 United States hospitals. Details of the study design have been described previously. Briefly, participants were admitted with AMI from April 2005 to December 2008, were ≥18 years of age, had elevated troponin levels within 24 hours of hospital admission, and demonstrated other supportive evidence of AMI, including at least one of the following: prolonged (>20 minutes) ischemic signs or symptoms, at least 1 electrocardiogram with ST elevation or ST depression in ≥2 consecutive leads, or other clinical evidence of AMI. Patients were excluded if they were transferred to the recruiting institution from another facility >24 hours after their original presentation, refused or were unable to provide informed consent, or did not speak English or Spanish. In addition, in a predefined biomarkers substudy of Translational Research Investigating Underlying disparities in recovery from acute Myocardial infarction: Patients’ Health status, patients from each site donated blood for central analysis of lipid profile, genetics, and biomarkers. For the purpose of this analysis, we only included patients from this substudy of Translational Research Investigating Underlying disparities in recovery from acute Myocardial infarction: Patients’ Health status (n = 2,370), with measurement of all 3 biomarkers (NT-proBNP, TnT, and hs-CRP).
Chart abstraction was performed and patients were interviewed by a trained personnel within 72 hours of admission to collect information on sociodemographic factors, medical history, clinical presentations, in-hospital events, and treatments. In addition, baseline venous blood samples were drawn at enrollment within 72 hours of admission. NT-proBNP, TnT, and hs-CRP analyses were performed at a central laboratory (Clinical Research Laboratories, Lenexa, Kansas) using standardized reagents (Roche Diagnostics, Indianapolis, Indiana). The Institutional Review Board at each participating institution approved the study, and all patients provided informed consent.
The primary outcome was the development of new-onset AF during the AMI hospitalization. AF was considered to be present if AF or atrial flutter was identified on a 12-lead electrocardiogram or was seen for >30 seconds on a rhythm strip at AMI presentation or during the AMI hospitalization. Patients with a history of AF or atrial flutter (n = 212) were excluded.
All data were entered at each participating institution and transmitted to the national data coordinating center using a web-based data entry interface that provided range checking, logic, and consistency checks at the time of data entry. These were supplemented with more complex interform quality checks and queries sent to each site for resolution before analysis.
Demographic, clinical, treatment, and biomarker characteristics between patients with AF and those with non-AF myocardial infarction (MI) were compared using Student t test for normally distributed continuous variables, Wilcoxon rank sum test for skewed variables, and chi-square or Fisher’s exact tests for categorical variables.
To identify characteristics that were independently associated with the development of new-onset AF, we first compared unadjusted median biomarker levels and biomarker levels in deciles between groups using Wilcoxon rank sum tests. Next, the effects of biomarker levels on risk of AF were estimated using multivariate hierarchical logistic regression models, accounting for clustering within site. Because of the presence of a skewed distribution of the biomarkers, NT-proBNP, TnT, and hs-CRP data were analyzed as log-transformed variables. The selection of covariates in multivariate models was based on both a priori and empirical considerations. First, important baseline covariates that are known to influence biomarkers were included in the analyses. Second, variables that differed significantly by AF occurrence on a bivariate basis (p <0.05) were considered as potential confounders. Log-transformed biomarker regression estimates were multiplied by log 2 to get the interpretation of a twofold multiplicative increase and exponentiated to obtain odds ratios. The resulting estimates reflect the relative increase in risk of AF per twofold increase in the corresponding biomarker. The retained covariates included patient demographics (age, gender, race), medical history (smoking, diabetes, congestive heart failure, hypertension, and chronic lung disease), previous coronary heart disease (previous AMI, coronary artery bypass grafting, and percutaneous intervention), type of AMI (ST elevation MI versus non-ST elevation MI), clinical severity (LV systolic function, systolic blood pressure, renal function, and Killip class), and the use of primary percutaneous intervention. In addition, interactions of gender and race with the biomarkers were tested. All tests for statistical significance were 2-tailed with an α level of 0.05.
In addition, we conducted sensitivity analyses by including the variables that were found to be significantly different between groups in univariate analysis (β blockers, calcium channel blockers, and antiplatelet agents on arrival). The effect size of the biomarkers did not change.
All analyses were conducted using SAS software, release 9.2 (SAS Institute, Cary, North Carolina) and R version 2.11.1 (Vienna, Austria).
Results
A total of 2,370 patients with AMI were included in the analysis. New-onset AF was documented in 114 patients with AMI (4.8%; mean age 58 years; 32% women). Of these, only 19 (16.7%) had new-onset AF on admission electrocardiography and the remainder developed it during their hospitalization.
Medical history and clinical data, stratified by new-onset AF, are listed in Table 1 . Patients with AMI who developed new-onset AF were more likely to be older, Caucasian, and to have a higher prevalence of diabetes, hypertension, chronic lung disease, and chronic kidney disease. They were less likely to smoke. There was no significant association between AF and higher Killip class, LV ejection fraction (EF), ST elevation myocardial infarction, previous congestive heart failure, or previous coronary heart disease. Patients with new AF were also more likely to receive β blockers, calcium channel blockers, and antiplatelet agents on arrival and as likely to receive reperfusion and revascularization therapies as those without new-onset AF ( Table 1 ).
| Characteristics | In-Hospital AF or Flutter | p | |
|---|---|---|---|
| Yes, n = 114 (%) | No, n = 2,256 (%) | ||
| Sociodemographic factors | |||
| Age (mean yrs ± SD) | 64.6 ± 13.2 | 57.5 ± 11.9 | <0.001 |
| Men | 87 (76.3) | 1,531 (67.9) | 0.059 |
| Caucasian | 88 (77.2) | 1,506 (66.8) | 0.021 |
| Body mass index | 30.4 ± 6.2 | 29.8 ± 6.6 | 0.396 |
| Current smoker | 28 (24.8) | 977 (43.6) | <0.001 |
| Family history of CHD | 87 (77.0) | 1,661 (74.5) | 0.555 |
| Previous angina pectoris | 16 (14.0) | 290 (12.9) | 0.714 |
| Previous CHD (MI or PCI or CABG) | 30 (26.3) | 682 (30.2) | 0.374 |
| Previous CVA | 9 (7.9) | 98 (4.3) | 0.075 |
| Diabetes mellitus | 48 (42.1) | 688 (30.5) | 0.009 |
| Chronic kidney disease | 13 (11.4) | 138 (6.1) | 0.024 |
| Chronic lung disease | 16 (14.0) | 145 (6.4) | 0.002 |
| Hypertension | 85 (74.6) | 1,462 (64.8) | 0.033 |
| Chronic heart failure | 11 (9.6) | 162 (7.2) | 0.323 |
| Dyslipidemia | 54 (47.4) | 1,057 (46.9) | 0.914 |
| Peripheral vascular disease | 7 (6.1) | 92 (4.1) | 0.330 |
| Alcohol abuse | 13 (11.4) | 235 (10.4) | 0.737 |
| Clinical characteristics at admission | |||
| Presenting heart rate (beats/min) | 82.3 ± 22.5 | 82.5 ± 21.4 | 0.915 |
| Presenting heart rate ≥100 (beats/min) | 20 (17.5) | 432 (19.2) | 0.661 |
| LV EF ≥40% | 91 (79.8) | 1,844 (81.8) | 0.600 |
| STEMI | 43 (37.7) | 1,011 (44.8) | 0.137 |
| Killip class on arrival, III or IV | 2 (1.8) | 47 (2.1) | 1.000 |
| Presenting systolic blood pressure ≥100 mm Hg | 109 (96.5) | 2,124 (94.7) | 0.401 |
| Treatment | |||
| Fibrinolytic | 6 (5.3) | 131 (5.8) | 0.808 |
| Glycoprotein IIb/IIIa blocker | 66 (57.9) | 1,411 (62.5) | 0.318 |
| Antiplatelet | 67 (58.8) | 1,539 (68.2) | 0.035 |
| Anticoagulant | 104 (91.2) | 2,024 (89.7) | 0.603 |
| Antithrombin | 2 (1.8) | 99 (4.4) | 0.234 |
| In-hospital revascularization | 87 (76.3) | 1,639 (72.7) | 0.391 |
| β blocker on arrival | 53 (46.5) | 670 (29.7) | <0.001 |
| Calcium channel blocker on arrival | 25 (21.9) | 296 (13.1) | 0.007 |
| Antiplatelet on arrival | 58 (50.9) | 927 (41.1) | 0.039 |
| Aspirin on arrival | 56 (49.1) | 880 (39.0) | 0.031 |
In unadjusted analyses, patients with new-onset AF had significantly higher levels of NT-proBNP and hs-CRP than those without AF ( Table 2 and Figure 1 ). There were no statistically significant differences between the level of TnT between patients with and without new-onset AF.
| Biomarkers | In-Hospital AF or Flutter | p | |
|---|---|---|---|
| Yes, n = 114 | No, n = 2,256 | ||
| CRP (median [IQR]) ∗ | 3.5 (1.1–7.7) | 1.9 (0.7–4.3) | <0.001 |
| NT-proBNP (median [IQR]) ∗ | 1756.0 (825.0–4603.0) | 966.0 (418.5–2227.5) | <0.001 |
| TnT (median [IQR]) ∗ | 1.1 (0.3–2.6) | 0.9 (0.3–2.1) | 0.242 |
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