Author + information
- Received June 28, 2017
- Revision received July 9, 2017
- Accepted July 11, 2017
- Published online September 4, 2017.
- Justin B. Echouffo-Tcheugui, MD, PhDa,
- Peter Shrader, MAb,
- Laine Thomas, PhDb,
- Bernard J. Gersh, MBChB, DPhilc,
- Peter R. Kowey, MDd,
- Kenneth W. Mahaffey, MDe,
- Daniel E. Singer, MDf,
- Elaine M. Hylek, MD, MPHg,
- Alan S. Go, MDh,
- Eric D. Peterson, MD, MPHb,
- Jonathan P. Piccini, MD, MHSb and
- Gregg C. Fonarow, MDi,∗ ()
- aDepartment of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- bDuke University Medical Center and Duke Clinical Research Institute, Durham, North Carolina
- cDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
- dLankenau Institute for Medical Research, Wynnewood, Pennsylvania
- eStanford Center for Clinical Research (SCCR), Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California
- fDepartment of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- gDepartment of Medicine, Division of General Internal Medicine, Boston University School of Medicine, Boston, Massachusetts
- hDivision of Research, Kaiser Permanente of Northern California, Oakland, California
- iDepartment of Medicine, Division of Cardiology/Ahmanson-UCLA Cardiomyopathy Center, University of California, Los Angeles, Los Angeles, California
- ↵∗Address for correspondence:
Dr. Gregg C. Fonarow, Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan-UCLA Medical Center, 10833 LeConte Avenue, Room 47–123 CHS, Los Angeles, California 90095.
Background Diabetes is a well-established risk factor for thromboembolism in patients with atrial fibrillation (AF), but less is known about how diabetes influences outcomes among AF patients.
Objectives This study assessed whether symptoms, health status, care, and outcomes differ between AF patients with and without diabetes.
Methods The cohort study included 9,749 patients from the ORBIT-AF (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation) registry, a prospective, nationwide, outpatient registry of patients with incident and prevalent AF. Outcomes included symptoms, health status, and AF treatment, as well as 2-year risk of death, hospitalization, thromboembolic events, heart failure (HF), and AF progression.
Results Patients with diabetes (29.5%) were younger, more likely to have hypertension, chronic kidney disease, HF, coronary heart disease, and stroke. Compared to patients without diabetes, patients with diabetes also had a lower Atrial Fibrillation Effects on Quality of Life score of 80 (interquartile range [IQR]: 62.5 to 92.6) versus 82.4 (IQR: 67.6 to 93.5; p = 0.025) and were more likely to receive anticoagulation (p < 0.001). Diabetes was associated with higher mortality risk, including overall (adjusted hazard ratio [aHR]: 1.63; 95% confidence interval [CI]: 1.04 to 2.56, for age <70 years vs. aHR: 1.25; 95% CI: 1.09 to 1.44, for age ≥70 years) and cardiovascular (CV) mortality (aHR: 2.20; 95% CI: 1.22 to 3.98, for age <70 years vs. 1.24; 95% CI: 1.02 to 1.51 for age ≥70 years). Diabetes conferred a higher risk of non-CV death, sudden cardiac death, hospitalization, CV hospitalization, and non-CV and nonbleeding-related hospitalization, but no increase in risks of thromboembolic events, bleeding-related hospitalization, new-onset HF, and AF progression.
Conclusions Among AF patients, diabetes was associated with worse AF symptoms and lower quality of life, and increased risk of death and hospitalizations, but not thromboembolic or bleeding events.
Atrial fibrillation (AF) is the most common clinically significant arrhythmia (1,2) and is associated with increased morbidity, mortality, and health care costs (3–5). Diabetes is a common comorbid illness in patients with AF; at least 1 in 7 patients with AF have diabetes (6). Diabetes has been shown to increase the risk of incident AF (7), as it promotes maladaptive and profibrillatory structural (mediated by oxidative stress, advanced glycosylation end products, and connective tissue changes), electromechanical, and autonomic nervous system changes (8).
Diabetes is also a risk factor for ischemic stroke in patients with AF not receiving anticoagulation (9–11). Consequently, diabetes has been included in the stroke risk scoring systems (CHADS2 and CHA2DS2-VASc), which are used to guide anticoagulation therapy in AF patients (12,13). However, the extent to which diabetes influences outcomes of AF other than thromboembolic events, including mortality, hospitalization, heart failure (HF), and AF progression, as well as the use of AF targeted therapies, is unclear. Despite the commonality of diabetes and AF, this has not been a topic of previous investigation. Such investigation may allow for a better understanding of the natural history, symptom burden, concomitant risks, and clinical outcomes of patients with both AF and diabetes (8).
Using the framework of the ORBIT-AF (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation) registry, we examined the association of diabetes and outcomes of AF. We hypothesized that diabetes will be associated with greater symptoms, more disease progression, and worse cardiovascular (CV) outcomes in patients with AF.
Data source and study population
Our study cohort was formed from the ORBIT-AF registry, which is an observational, prospective, quality improvement program. The rationale and design of ORBIT-AF have been previously described in detail (14). In brief, the registry enrolled adults older than 18 years with electrocardiographic evidence of AF. Patients were excluded if they were diagnosed as having AF secondary to an easily reversible condition or if they had a life expectancy <6 months. Patients were enrolled from heterogeneous practices across the United States, including internal medicine, neurology, cardiology, and electrophysiology clinics. Data were collected by various health care providers, including internists, primary care physicians, neurologists, cardiologists, and electrophysiologists. The patient’s medical record was entered into an online case report form, and patients were to be followed every 6 months for 2 to 3 years. All participants gave informed consent, and all study sites had approval from an institutional review board for inclusion in the ORBIT-AF registry.
We identified 10,137 patients with AF from a total of 176 U.S. clinic practices enrolled between June 29, 2010, and August 9, 2011. We excluded those patients without information on follow-up (n = 388), leaving a final study sample of 9,749 patients.
Diabetes mellitus was defined by previous medical history or new clinical diagnosis during the enrollment visit.
Patients were followed at 6-month intervals for up to 3 years. Outcomes assessed at follow-up were all-cause mortality, CV death, non-CV death, hospitalization (all-cause, CV-related, bleeding-related, and non-CV and nonbleeding-related), stroke or non-central nervous system systemic embolism or transient ischemic attack (TIA), incident new-onset HF, AF progression, bleeding events, AF symptoms, health status, and use of targeted therapies. AF progression was defined as either: 1) progression from paroxysmal AF at baseline to either persistent or permanent AF reported at any subsequent follow-up visit; or 2) progression from persistent AF at baseline to permanent AF reported at any subsequent follow-up visit (15,16). Paroxysmal AF was defined as recurrent AF episodes that terminate spontaneously within 7 days; persistent AF as recurrent AF that is sustained for more than 7 days; and permanent AF as AF in which the presence of the AF is accepted. The assessment of the use of targeted therapies included the management strategy (rate control, rhythm control), medication use (warfarin, non-vitamin K oral anticoagulants [OACs], aspirin, clopidogrel, beta-blockers, calcium-channel blockers, digoxin, amiodarone, sotalol, dofetilide, propafenone, flecainide), and procedures (catheter ablation for AF or atrial flutter, atrioventricular node ablation cardioversion).
We compared patient (demographic and clinical) and hospital characteristics, as well as proportional use of different AF management strategies between patients with and without diabetes. Categorical variables are presented as count and proportion, and differences were tested using the Pearson chi-square test. Continuous variables are presented as median (interquartile range [IQR]), and differences between groups were tested using the Wilcoxon rank sum test.
The event rate for each outcome was estimated both overall and by diabetes status, with rates presented as the number of events per 100 patient-years of follow-up. We used Cox proportional hazards survival models to test the association between diabetes and each clinical outcome. For AF progression, we estimated the frequency and percentage of AF progression, and a logistic regression model was used to estimate the odds ratio (OR) for the association between diabetes and AF progression. All models used a robust variance estimate to account for correlation within sites. Covariates for the multivariable modeling were chosen based on their clinical relevance or known association with diabetes and outcome(s). A common set of variables was used in models for adjustment and is listed in the Online Appendix. Adjusted associations for outcomes are displayed as hazard ratio (HR) or OR with corresponding 95% confidence interval (CI). We assessed the influence of age, race (white vs. nonwhite), use of anticoagulants, renal function (as defined by estimated glomerular filtration rate), type of AF (paroxysmal vs. nonparoxysmal), history of ablation procedure, and pulse pressure on the relationship between diabetes and outcomes, by testing the interaction of each of these factors with diabetes. In models, all continuous variables were tested for linearity with each outcome, and any nonlinear associations are accounted for using linear splines. Missing covariate data in the regression analysis were handled by multiple imputation using Markov Chain Monte Carlo and regression methods. Final estimates and associated standard errors reflect the combined analysis over 5 imputed datasets.
Analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, North Carolina). All p values were based on 2-sided tests and were considered statistically significant at p < 0.05.
The final study sample from ORBIT-AF contained 9,749 patients from 174 practices, among whom 2,874 (29.5%) had diabetes, with median follow-up of 2.78 years (IQR: 1.95 to 3.00 years) and mean follow-up of 2.41 ± 0.75 years. The proportion of surviving patients by period of follow-up are 6.8% with <1 year of follow-up, 12.5% with <1.5 years of follow-up, 17.6% with <1.8 years of follow-up, and 28.9% with <2.0 years of follow-up.
Baseline clinical and demographic characteristics of the study participants by diabetes status are given in Table 1. Patients with diabetes were more likely to be younger, male, from an ethnic minority (Hispanic and black), a smoker, and have a higher body mass index and a history of comorbidities, with the largest differences from nondiabetic patients observed for hypertension, obesity, hyperlipidemia, chronic kidney disease or dialysis, chronic pulmonary obstructive disease, obstructive sleep apnea, coronary artery disease, cerebrovascular disease, and HF. Patients with diabetes had a higher stroke risk compared to those without diabetes as estimated by media CHADS2 (3 vs. 2; p < 0.001) and CHA2DS2-VASc scores (5 vs. 4; p < 0.001). Only 0.7% of patients with diabetes had a CHA2DS2-VASc score of 1 compared to 12.4% of patients without diabetes (p < 0.001). Patients with diabetes also had a significantly elevated bleeding risk no matter which scoring system was used (Table 1).
Symptoms, functional status, and quality of life
In the ORBIT-AF cohort, patients with diabetes had greater functional impairment as reflected by higher European Heart Rhythm Association scores (Table 1). Across the AF symptom checklist, patients with diabetes less often reported palpitations (29.2% vs. 33.6%; p < 0.001) or syncope (3.3% vs. 4.9%; p < 0.001), but they reported significantly more dyspnea upon exertion (29.7% vs. 26.6%; p = 0.002) or at rest (11.1% vs. 9.8%; p = 0.06), exercise intolerance (11.9% vs. 9.2%; p < 0.001), and fatigue (27.9% vs. 25.6%; p = 0.02). There were no differences between the diabetes and nondiabetes groups in terms of lightheadedness or dizziness and chest tightness or discomfort.
Regarding health-related quality of life (QOL) (Table 1), patients with diabetes had a slightly lower overall median Atrial Fibrillation Effect on Quality-of-Life scores compared to those without diabetes (80.6; IQR: 62.5 to 92.6 vs. 82.4; IQR: 67.6 to 93.6; p = 0.03). This difference was mainly driven by the daily activities domain score, as the symptoms and treatment concern domain scores were similar. These differences persisted at 12- and 24-month follow-up.
AF management and outcomes
Patients with diabetes had persistent and permanent AF or a combination of persistent or permanent AF (47.9% vs. 43.6%) more frequently than did those without diabetes (p < 0.001) (Table 1), but they were less likely to have undergone cardioversion or ablation (Table 2). The use of rate-control strategies was higher among people with diabetes compared to those without diabetes contrary to use of rhythm-control therapies, which was lower (Table 2). Among rate-control agents, the use of beta-blockers (67.8% vs. 62.8%; p < 0.001), calcium-channel blockers (31.8% vs. 29.8%; p = 0.05), and digoxin (29.0% vs. 21.3%; p < 0.001) was more prevalent among people with diabetes. For rhythm-control therapies, flecainide was less commonly used among people with diabetes (1.0% vs. 3.7%; p < 0.001). The overall use of anticoagulants (warfarin or dabigatran) was significantly greater among people with diabetes. The use of warfarin only was higher among those with diabetes compared to those without diabetes (74.3% vs. 70.4%; p < 0.001), but use of dabigatran was similar in the 2 groups. Aspirin was more commonly used among people with diabetes (46.5% vs. 43.4%; p = 0.006).
The incidence rates for all outcomes during follow-up are given in Table 3. Over the 2-year period, mortality rates were higher among people with diabetes compared to those without diabetes. The rates of CV death, non-CV death, and sudden cardiac death (SCD) also were higher among patients with diabetes than in those without diabetes (Table 3, Central Illustration, Figure 1, and Online Figures 1 to 12).
There was a significant interaction between diabetes and age for all-cause death (p = 0.025) and CV mortality (p = 0.002) outcomes. After multivariable adjustment, diabetes was associated with a higher risk of mortality, both among those age <70 years (adjusted hazard ratio [aHR]: 1.63; 95% CI: 1.04 to 2.56) and among those age ≥70 years (aHR: 1.25; 95% CI: 1.09 to 1.44) (Table 3). Diabetes was also related to a higher risk of CV death, more so among those <70 years than in those ≥70 years, as well as an increased risk of non-CV death or SCD. Diabetes was similarly associated with a higher risk of all-cause (aHR: 1.15; 95% CI: 1.09 to 1.22), CV (aHR: 1.13; 95% CI: 1.05 to 1.22) and non-CV and nonbleeding-related hospitalizations (aHR: 1.19; 95% CI: 1.10 to 1.30). However, diabetes was not associated with a higher risk of thromboembolic events (including stroke, TIA, and non-central nervous system embolism) or hospitalization related to bleeding (Table 3). Also, there was no association between diabetes and incident new-onset HF (aHR: 1.08; 95% CI: 0.80 to 1.47) or AF progression (adjusted OR: 0.96; 95% CI: 0.85 to 1.08). OAC use modified the association of diabetes and all-cause hospitalization (p for interaction = 0.018). Diabetes was more strongly related to all-cause hospitalization (aHR: 1.21; 95% CI: 1.12 to 1.29) among those using an OAC than among those not using OAC (aHR: 0.98; 95% CI: 0.85 to 1.13). Among patients with diabetes, there were no significant differences in outcomes of AF between patients using both OAC and antiplatelet therapy and those taking an OAC alone (Online Table 1).
There was a significant interaction of diabetes and pulse pressure (≤60 mm Hg vs. >60 mm Hg) for the following outcomes (Online Table 2): new-onset HF (p < 0.001), CV hospitalization (p = 0.015), bleeding hospitalization (p = 0.029), and non-CV or nonbleeding hospitalization (p = 0.002).
We examined outcomes of AF in patients with diabetes in a large, real-world population of 9,749 patients in the ORBIT-AF registry. Overall, 30% of patients had diabetes. Key findings included the following: use of anticoagulation and rate-control strategies was significantly greater among patients with diabetes, but this was not the case for rhythm-control strategies (including pharmacotherapy and cardioversion and catheter-based ablation procedures); patients with diabetes had more AF symptoms and worse health status; those with diabetes had a significantly higher risk of death (overall, CV, and non-CV death) and of hospitalization, but similar risk of thromboembolic events (with close to 80% of patients receiving anticoagulation); and patients with diabetes experienced more new-onset HF and AF progression.
Although the association of diabetes and incident AF has been reported in several community-based (7,17) and hospital-based studies (18), a limited number of studies have evaluated the association between diabetes and clinical outcomes among individuals with AF (19–21). The present study demonstrated that, even after extensive covariate adjustment, diabetes is independently associated with an increased risk of all-cause and CV mortality in community-based patients with AF. These findings were consistent with previous studies, which also suggested that diabetes among patients with nonvalvular AF is associated with an increased risk of all-cause mortality (19,20), including among patients already on anticoagulation (21). This suggests that the influence of diabetes on outcomes of AF might extend beyond its relation to thromboembolic events, as evidenced by the increased overall, CV, and SCD risks in our data.
Diabetes was also associated with increased use of targeted therapies, as well as with higher risk of CV and all-cause hospitalization. In contrast, diabetes in the setting of contemporary management, including OAC in approximately 80% of patients, was not associated with increased risk of stroke (19,20). Our findings of a lack of difference in thromboembolic events between patients with and without diabetes were consistent with previous reports in patients receiving anticoagulation (19). The latter was also observed in clinical trials of newer anticoagulants, in which stroke risk did not differ between those with or without diabetes (22,23). The lack of association of diabetes with the risk of stroke may be partially explained by the inclusion of diabetes in the stroke risk calculators (CHADS2 and CHA2DS2-VASc). Indeed, virtually all the patients with AF and diabetes had CHA2DS2-VASc >1 and thus were eligible for OAC based on current guidelines. It is possible, however, that diabetic patients with AF present at a younger age and are more symptomatic and therefore are more likely to receive these evidence-based disease-modifying therapies. Yet the design of the present study, which analyzed diabetes as a single cohort, may not fully capture the totality of the effect of abnormal glucose tolerance on the risk of stroke. Indeed, previous studies have suggested that the degree of blood glucose (as measured by glycosylated hemoglobin [HbA1c]) (24) and diabetes duration are determinants of the risk of stroke and thromboembolic events among patients with AF (10,11).
The finding of a greater burden of permanent and persistent AF, severe to disabling symptoms, and worse QOL among patients with diabetes was consistent with previous findings. There have been studies suggesting that the burden of AF-related symptoms is higher among patients with diabetes (25). The lower frequency of rhythm-control strategies among patients with diabetes could be explained by a greater prevalence of persistent and permanent AF, whereas ablation is more likely to be performed in the setting of paroxysmal AF and would typically occur in relatively young subjects without CV risk factors. Conversely, we observed a greater use of rate-control medications in patients with diabetes, which may parallel the use of anticoagulants.
To our knowledge, our study was the first of its kind to report on the influence of diabetes on the occurrence of SCD among patients with AF. The increase in the risk of SCD may be related to the fact that diabetes and AF potentiate their respective individual effects. It is well known that AF and diabetes independently increase the risk of SCD in the general population (26,27).
The lack of association between diabetes and AF progression contrasted somewhat with previous studies suggesting that elevated glucose levels may contribute to the persistence of AF in general (28) or to the recurrence of AF after ablation (29). The HATCH score (which is based on hypertension, age ≥75 years, TIA or stroke, chronic obstructive pulmonary disease, and HF) for predicting progression from paroxysmal to permanent AF did not include diabetes (16). Furthermore, there is no direct evidence showing that maintenance of well controlled blood glucose, in accordance with guidelines, would be beneficial. However, the ARREST-AF (Aggressive Risk Factor Reduction Study for Atrial Fibrillation and Implications for the Outcome of Ablation) trial suggested that this may be the case, as it showed that a strategy of aggressive modification of several risk factors, including weight loss and improved glycemic control, was associated with an almost 5-fold increased odds of arrhythmia-free survival after ablation (30). It is possible that the degree of blood glucose control and the duration of diabetes matter, as these would influence left atrial remodeling. Unfortunately, we did not have data on these aspects of diabetes. Diabetes may confer a specific pathophysiological substrate that would theoretically aggravate AF and predispose to worse thromboembolic outcomes. This substrate includes structural (nonenzymatic glycation and connexin-mediated fibrosis), electrical (intra-atrial conduction), and autonomic changes to the left atrium (8,31). However, we did not observe an increase in the risk of AF progression or poor thromboembolic outcomes with diabetes. It is possible that the risk of thromboembolic events was mitigated by the use of OAC therapy, as diabetes is systematically included in thromboembolic events risk prediction tools.
Our study provided significant complementary information on the association of diabetes with AF symptoms, health status, and clinical outcomes of AF. In addition to information on the mortality risk among patients with AF, we also provided information on hospitalization, bleeding, and AF progression as well as onset of HF rates. Previous outcome studies of patient with diabetes and AF were limited by incomplete adjustment for confounding factors, relatively small sample sizes, and a shorter follow-up period (1 year) (19,20). These studies have also lacked the depth and diversity of our study population with respect to race, age, or sex; in addition, the other studies did not always characterize outcomes other than death and stroke risk (19,20). Our findings highlighted the importance of diabetes management in patients with AF and emphasized the importance of using proven therapies that can reduce CV events and mortality in patients with diabetes, such as statins and certain diabetes medications. The management of comorbid diabetes and AF presents unique challenges, especially in the context of the emergence of new diabetes and AF therapies, with a potential impact on the outcomes of AF or diabetes. However, opportunities to improve outcomes in patients with diabetes have emerged, with recently tested therapies (sodium-glucose cotransporter-2 inhibitors) having shown clear and important benefits in terms of CV events (including HF) and CV mortality (32). Diabetes among patients with AF may be associated with higher expenditures, so integrating management of AF and diabetes might improve functional outcomes and reduce costs.
The strengths of our study include a large nationwide cohort, a standardized methodology for data collection, and the examination of AF subtypes, regular follow-up, detailed information on comorbid illness, and several clinically relevant outcomes. However, there were limitations to our study. First, reliance on clinical practice data for diabetes ascertainment might have missed patients without a pre-hospitalization history of diabetes and who were not screened during their hospitalization for diabetes, especially as up to one-fourth of all persons with diabetes in the United States are undiagnosed (33). Diabetes might have been underreported by using clinically diagnosed and documented diabetes rather than using strict laboratory criteria. Likewise, we did not ascertain cases of diabetes that developed during study follow-up. The consequence of underestimation of diabetes frequency would be to bias the influence of diabetes on the outcomes of AF toward the null.
Second, the main endpoint for this analysis was all-cause mortality; the causes of death are not known because we did not have access to this information (especially the CV causes). However, differences in overall and CV-related hospitalization rates pointed to a potentially high contribution of CV diseases to deaths. Third, we did not have information on the degree of glycemic control (as represented by HbA1c) or duration of diabetes, which might influence outcomes. The levels of HbA1c are associated with stroke risk among those with AF and improve the predictive accuracy for stroke in diabetic patients with AF (24). The duration of diabetes might also be important for assessing the risk of ischemic stroke among patients who have diabetes and AF (10,11). We did not have information on the medications used for glycemic control (insulin or noninsulin-based therapies), which may influence AF outcomes, especially given that some medication classes (the thiazolidinediones) may be related to better AF outcomes (34).
Fourth, the ORBIT-AF registry relied on voluntary participation from sites and patients. Thus, the results might not be generalizable to all U.S. patients, particularly those who are managed in different clinical settings compared with that of the ORBIT-AF participants. Fifth, the follow-up duration in our study (2 years) might not reveal the longer-terms effects of diabetes. Furthermore, given the average age of our participants (>70 years) and that younger people with diabetes might be at greater risk for developing AF (35), there may have been a selection bias related to the enrollment process. However, it is important to bear in mind that age is a significant risk factor for AF. Finally, we may have had a limited power to detect a difference in some of the outcomes, and, despite appropriate adjustment for confounders in our models, residual confounding may have persisted, potentially affecting our findings.
Among patients with AF in this nationwide cohort, the prevalence of diabetes mellitus was 30%, emphasizing the importance of diabetes screening in patients diagnosed with AF. Patients with AF and diabetes had more symptoms and worse health status along with higher mortality and higher frequency of hospitalization than patients without diabetes. Future studies are warranted to explore ways to mitigate this mounting problem, which could exponentially worsen in the years to come given the growing diabetes epidemic.
COMPETENCY IN MEDICAL KNOWLEDGE: In patients with AF, concurrent diabetes influences the burden of AF-related symptoms, risk of hospitalization, and cardiovascular outcomes.
TRANSLATIONAL OUTLOOK: Future efforts to improve clinical outcomes for patients with AF should recognize that diabetes contributes to adverse outcomes beyond thromboembolic events, but more research is needed to understand the mechanisms by which diabetes effects these outcomes.
For a list of covariates for multivariable modeling as well as supplemental tables and figures, please see the online version of this article.
This project was supported in part by cooperative agreement 1U19 HS021092 from the Agency of Healthcare Research and Quality. The Outcomes Registry for Better Informed Treatment of Atrial Fibrillation is sponsored by Janssen Scientific Affairs LLC, Raritan, New Jersey. Dr. Piccini has received grants from the Agency for Healthcare Research and Quality and Janssen Pharmaceuticals; has received consulting fees from Bristol-Myers Squibb/Pfizer and Johnson & Johnson; has received research support from ARCA Biopharma, Boston Scientific, GE Healthcare, and Johnson & Johnson/Janssen Scientific Affairs; and has received consultancy fees from Forest Laboratories, Janssen Scientific Affairs, Pfizer/Bristol-Myers Squibb, Spectranetics, and Medtronic. Dr. Fonarow has served as a consultant for Novartis, Amgen, Bayer, Gambro, Medtronic, and Janssen; is a member of the GWTG steering committee; and is supported by the Ahmanson Foundation (Los Angeles, California). Dr. Gersh has received personal fees from Mount Sinai–St. Luke’s, Boston Scientific Corp., Teva Pharmaceuticals, Janssen Scientific Affairs, St. Jude Medical, Janssen Research & Development LLC, Duke Clinical Research Institute, Duke University, Kowa Research Institute Inc., Sirtex Medical Ltd., Baxter Healthcare Corp., Cardiovascular Research Foundation, Medtronic, Xenon Pharmaceuticals, Cipla Ltd., Thrombosis Research Institute, and Armetheon Inc. Dr. Hylek has received personal fees from Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Daiichi-Sankyo, Janssen, Medtronic, Pfizer, and Portola. Dr. Kowey has served as a consultant/advisory board member for Boehringer Ingelheim, Bristol-Myers Squibb, Johnson & Johnson, Portola, Merck, Sanofi, and Daiichi-Sankyo. Dr. Mahaffey has received research grants from Afferent, Amgen, AstraZeneca, Daiichi, Ferring, Google (Verily), Johnson & Johnson, Medtronic, Merck, Novartis, Sanofi, and St. Jude; has served as a consultant or provided other services for Ablynx, AstraZeneca, BAROnova, Bio2 Medical, Boehringer Ingelheim, Bristol-Myers Squibb, Cardiometabolic Health Congress, Cubist, Eli Lilly, Elsevier, Epson, GlaxoSmithKline, Johnson & Johnson, Merck, Mt Sinai, Myokardia, Novartis, Oculeve, Portola, Radiomeer, Springer Publishing, The Medicines Company, Theravance, UCSF, Vindico, and WebMD; and has equity in BioPrint Fitness. Dr. Peterson has received personal fees from Boehringer Ingelheim, Sanofi, AstraZeneca, Valeant, and Bayer; grants and personal fees from Janssen; and research support from Eli Lilly & Co. and Janssen Scientific Affairs. Dr. Singer has served as a consultant/advisory board for Boehringer Ingelheim, Bristol-Myers Squibb, CVS Health, Merck, Johnson & Johnson, Medtronic, and Pfizer; and has received research grants from Boehringer Ingelheim, Bristol-Myers Squibb, and Medtronic. Dr. Go has received a research grant to his institution from iRhythm Technologies. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- atrial fibrillation
- adjusted hazard ratio
- confidence interval
- glycosylated hemoglobin
- heart failure
- hazard ratio
- interquartile range
- oral anticoagulant
- odds ratio
- quality of life
- sudden cardiac death
- transient ischemic attack
- Received June 28, 2017.
- Revision received July 9, 2017.
- Accepted July 11, 2017.
- 2017 American College of Cardiology Foundation
- Wolf P.A.,
- Abbott R.D.,
- Kannel W.B.
- Benjamin E.J.,
- Blaha M.J.,
- Chiuve S.E.,
- et al.
- Kim M.H.,
- Johnston S.S.,
- Chu B.-C.,
- Dalal M.R.,
- Schulman K.L.
- Piccini J.P.,
- Hammill B.G.,
- Sinner M.F.,
- et al.
- Plitt A.,
- McGuire D.K.,
- Giugliano R.P.
- Overvad T.F.,
- Skjøth F.,
- Lip G.Y.H.,
- et al.
- Ashburner J.M.,
- Go A.S.,
- Chang Y.,
- et al.
- Lip G.Y.H.,
- Nieuwlaat R.,
- Pisters R.,
- Lane D.A.,
- Crijns H.J.G.M.
- Holmqvist F.,
- Kim S.,
- Steinberg B.A.,
- et al.
- de Vos C.B.,
- Pisters R.,
- Nieuwlaat R.,
- et al.
- Staszewsky L.,
- Cortesi L.,
- Baviera M.,
- et al.
- Huang B.,
- Yang Y.,
- Zhu J.,
- et al.
- Gómez-Outes A.,
- Lagunar-Ruíz J.,
- Terleira-Fernández A.-I.,
- Calvo-Rojas G.,
- Suárez-Gea M.L.,
- Vargas-Castrillón E.
- Sandhu R.K.,
- Conen D.,
- Tedrow U.B.,
- et al.
- Pathak R.K.,
- Middeldorp M.E.,
- Lau D.H.,
- et al.
- Goudis C.A.,
- Korantzopoulos P.,
- Ntalas I.V.,
- Kallergis E.M.,
- Liu T.,
- Ketikoglou D.G.
- Liu B.,
- Wang J.,
- Wang G.