The Effect of Anxiety and Depression on Symptoms Attributed to Atrial Fibrillation TIFFANY S. THOMPSON, M.S.N., A.G.N.P.-C.,* DEBRA J. BARKSDALE, PH.D., F.N.P-B.C.,* SAMUEL F. SEARS, PH.D.,† JOHN PAUL MOUNSEY, M.D., PH.D.,‡ IRION PURSELL, R.N.,‡ and ANIL K. GEHI, M.D.‡ From the *School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; †Departments of Psychology and Cardiovascular Sciences, East Carolina University, Greenville, North Carolina; and ‡Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
Background: Symptoms attributed to atrial fibrillation (AF) are nonspecific, and it remains unclear what influences perception of symptoms. Anxiety or depression may be important in modulating perception of AF symptoms. However, few longitudinal studies have addressed this effect. Methods: A total of 378 patients with AF completed anxiety and depression severity questionnaires as well as AF symptom and frequency severity questionnaires. Patients were offered treatment strategies including catheter ablation or antiarrhythmic or rate-controlling medications. Patients were followed at 3-month intervals and completed follow-up questionnaires including repeat assessment of anxiety, depression, and AF symptoms. A method of generalized estimating equations was used for longitudinal analyses. Results: Analysis revealed that increased anxiety or depression was associated with increased AF symptom severity (AFSS), after adjusting for potential confounders. In both unadjusted and adjusted follow-up analyses, antiarrhythmic drug therapy or catheter ablation reduced AFSS (P < 0.001). However, none of anxiety severity, depression severity, or the perception of AF frequency severity improved significantly with AF treatment. Conclusions: Our results extend previous studies demonstrating that anxiety and depression are associated with worsened AFSS. Antiarrhythmic drug therapy or catheter ablation reduces AFSS but does not affect depression and anxiety symptoms. To achieve more comprehensive AF symptom relief, treatment of both AF and psychological comorbidities may be beneficial. (PACE 2014; 37:439–446) atrial fibrillation, depression, anxiety, quality of life
Introduction Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its prevalence is growing rapidly as the population ages.1 AF is associated with increased morbidity and mortality and has been shown to increase risk of stroke, heart failure, and to reduce quality of life.2 The treatment of AF places a significant burden on the U.S. healthcare system with an estimated annual cost of over six billion dollars.3 Symptoms attributed Dr. Sears has served as a consultant to Medtronic and has or has had research grants from Medtronic. These funds were directed to East Carolina University. Dr. Sears also has received speaker honorarium from Medtronic, Boston Scientific, St Jude Medical, and Biotronik. No other disclosures to report. Address for reprints: Anil K. Gehi, M.D., Cardiac Electrophysiology, Division of Cardiology, CB 7075, 160 Dental Circle, 6025 Burnett-Womack Bldg, Chapel Hill, NC 27599-7075. Fax: 919966-4366; e-mail: [email protected] Received May 13, 2013; revised August 8, 2013; accepted September 13, 2013. doi: 10.1111/pace.12292
to AF include fatigue, palpitations, shortness of breath, lightheadedness, and chest pain.4 Multiple studies have evaluated the negative impact of psychological distress on health outcomes in coronary artery disease, heart failure, and myocardial infarction.5 However, it remains unclear precisely what influences AF symptom perception and what effect psychological comorbidities may have on AF symptoms and quality of life. Several studies have demonstrated a strong correlation between AF symptoms and psychological comorbidities, such as anxiety and depression.6–9 However, only a single longitudinal study has investigated the relationship between psychological comorbidities and AF symptoms after treatment of persistent AF by direct current cardioversion.9 Further study into the directionality of the association between anxiety and depression and AF symptom severity (AFSS) is clearly necessary. Understanding the directionality of the relationship between depression and anxiety and AFSS may allow for a more targeted treatment approach in patients with AF and may
©2013, The Authors. Journal compilation ©2013 Wiley Periodicals, Inc. PACE, Vol. 37
April 2014
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ultimately lead to better patient outcomes. To help understand the interaction of psychological comorbidities and AF symptoms, we designed the present longitudinal analysis to evaluate whether the treatment of AF has an impact on AFSS as well as severity of anxiety and depression. To examine these relationships, we collected data on AFSS, depression, and anxiety in the Symptom Mitigation in AtRial FibrillaTion (SMART) longitudinal study of patients with AF. The aims of the study were to (1) establish the association of depression and anxiety on perceived AF symptom and frequency severity adjusting for important potential confounders and (2) to assess the effect of AF treatment strategies (rate control, antiarrhythmic drug therapy, or catheter ablation) on both perceived AF symptom and frequency severity and symptoms of depression or anxiety over time.
(HADS-A = 8–10), or probable anxiety (HADSA ≥ 11).8 The anxiety subset of the HADS-A demonstrated high internal consistency with a Cronbach’s alpha coefficient of 0.829, as well as high concurrent validity.11 Depression was measured using the Patient Health Questionnaire (PHQ-9), a 9-item tool to measure severity of depression over the previous 2 weeks (range 0–27).12 The PHQ-9 was chosen over other depression screening tools as it is simple to use and performs best when compared with the structured clinical interview for Diagnostic and Statistical Manual of Mental Disorders-IV as the standard criterion.13 Depression severity was categorized as none to minimal (PHQ-9 score = 0–3), mild to moderate (PHQ-9 = 4–9), and severe (PHQ-9 ≥ 10).14 The PHQ-9 questionnaire also has demonstrated high internal consistency with a Cronbach’s alpha coefficient of 0.83.14
Methods Characteristics of Participants The SMART study is a single-center prospective cohort study of patients with AF measuring AF symptoms and health outcomes. Details of the SMART study have been previously described.8 In brief, participants were enrolled through outpatient electrophysiology clinics at the University of North Carolina Chapel Hill when referred for management of AF. Participants were excluded if they were planning to move from the local area within 3 years of enrollment or were less than 18 years. The appropriate institutional review board approved the study and all participants provided written informed consent. Upon enrollment, participants completed a baseline questionnaire of general demographic information and measures of physical and psychological health. In addition, all participants underwent routine laboratory studies, electrocardiogram, and transthoracic echocardiogram. Between September 2009 and January 2012, 450 participants were enrolled. Of these patients, 378 completed anxiety, depression, and AFSS questionnaires and were included in this substudy.
AF Symptom and Frequency Severity
Anxiety and Depression The primary predictor variables in this study were current anxiety and depression. Anxiety severity was measured using the anxiety subset of the hospital anxiety and depression scale (HADS-A) containing seven items scored 0–3 based on recent symptoms.10 The sum of each question totaled to measure generalized anxiety, with higher scores equating to more anxiety (range 0–21). Anxiety severity was categorized as normal (HADS-A = 0–7), possible anxiety
440
AFSS was measured using the University of Toronto AF Symptom Severity Scale designed to measure symptom severity taking into account symptom duration, frequency, and severity, as well as disease burden. The symptom severity subscale assesses seven individual symptoms attributed to AF, scored on a 5-point Likert scale. Higher scores on the 0–35 scale indicated more severe symptoms.15 The symptom frequency subscale assesses frequency and duration by asking the questions: “How often on average does your irregular heart rhythm (atrial fibrillation) occur?” and “How long on average do the episodes of irregular heart rhythm last?” Responses are normalized and combined into a frequency score (range 2–20) with higher scores indicating more severe AF frequency. The AFSS scale has demonstrated a Cronbach’s alpha coefficent of 0.72 for symptom severity.16 Other Participant Characteristics Additional demographics included in the questionnaire were age, gender, marital status, and education. Education level was determined by asking participants their highest level of education completed with choices of no formal schooling, 5th grade or less, 6th to 8th grade, 9th to 11th grade, high school graduate or equivalent, some college/vocational school, college degree, or graduate degree. Responses were then categorized as less than high school, high school grad, some college, or college or graduate degree for analysis. Marital status was determined by asking if the participant was married or in a permanent partnership, widowed, separated, divorced, or never married. Choosing yes, no, retired, or student when asked, “Do you work in a paid job”
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determined employment status. Other participant characteristics, including medical comorbidities and medications, were obtained by questionnaire and review of the patient’s electronic health record. Intervention and Follow-Up Treatment strategies were determined based on patient preference and physician recommendation. As patients were referred for management of AF, those with symptomatic AF were offered antiarrhythmic drug therapy. Those with symptomatic AF who had failed antiarrhythmic drug therapy were offered catheter ablation. Asymptomatic patients were generally treated with rate-controlling medications alone. Oral anticoagulation and antiplatelet therapies were used when necessary based on individual risk for stroke. Details of the radiofrequency catheter ablation procedure are described in a previous study.17 Patients were generally followed at 3-month intervals until deemed stable and appropriate to return to the care of their primary healthcare provider. Participants completed follow-up questionnaires including assessment of anxiety, depression, and AFSS at each follow-up visit. Statistical Analysis Participant characteristics were analyzed using measures of central tendency. Frequency was calculated for categorical variables and means and standard deviations were calculated for continuous variables. The predictor variables (anxiety and depression) were analyzed both as a continuous variable and categorized as none/minimal, mild/moderate, or severe for ease of interpretation. Bivariate analyses were performed to assess which baseline characteristics may be associated with significant AFSS (≥ 20, the highest quartile of AFSS score in the cohort) using Student’s ttests for continuous variables and χ 2 analyses for categorical variables. Next, bivariate and multivariate analyses of the association of depression or anxiety with the outcome variables perceived AFSS and perceived AF frequency severity at baseline were performed. Bivariate analyses were performed using Pearson’s correlation coefficient when analyzing depression and anxiety severity as a continuous variable and one-way analysis of variance when analyzing depression and anxiety severity as a categorical variable. Important potential confounders included in the analysis based on prior literature included age, gender, education level, and βblocker use.18–20 Multivariate analysis was performed using analyses of covariance adjusting for the aforementioned variables.
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Finally, follow-up data were used to determine the effect of AF treatment (rate control vs antiarrhythmic drug therapy vs catheter ablation) on perceived AFSS, perceived AF frequency severity, depression severity, and anxiety severity over time. For this analysis, short-term follow-up was defined as a follow-up date 90 (± 20) days after treatment and long-term follow-up was defined as a follow-up date >270 days after treatment. Each outcome variable was modeled as a time-varying variable. The method of generalized estimating equations (GEE) was used to analyze the effect of treatment on repeated measurements on the same subject over time.21 For this method, we used GEE with a Gaussian regression model including an interaction term between time (month) and treatment group. The presence of a significant interaction indicated a significant difference in the outcome over time by treatment group. The GEE model was run with and without adjustment for potential confounders. All analyses were performed with STATA, version 11 (StataCorp LP, College Station, TX, USA). Statistical tests were two-tailed, with P < 0.05 considered significant. Results Of the total 450 patients enrolled, 378 (84%) completed the PHQ-9 questionnaire, the HADSA questionnaire, and the AFSS questionnaire, and were included in this substudy. Baseline characteristics of the patient population are presented in Table I. The majority of participants in the study were older (mean age 61.7), male (66.2%), married or with a permanent partner (73.1%), and had at least some college education (74.3%). Comorbidities in the population included hypertension (57.7%), diabetes (19.1%), heart failure (14.1%), and coronary artery disease (15.1%). Over half of the study population was on a β-blocker (59.1%), whereas other medications were angiotensin-converting enzyme inhibitors (ACE) and angiotensin receptor blockers (ARB) (40.3%), statins (39.1%), calcium channel blockers (24.9%), digoxin (10.6%), and antiarrhythmic medications (48.0%). The majority of the 378 participants who completed the HADS questionnaire fell into the category of normal anxiety (82.4%), whereas 12.4% of participants had possible anxiety and 5.2% had probable anxiety (Table II). Of the 378 participants who completed the PHQ-9 questionnaire, 43.7% had no to minimal depression, 39.4% had mild to moderate depression, and 16.9% had severe depression (Table II). Bivariate analyses (Table I) were performed to assess which baseline participant characteristics were associated with the highest quartile of the primary outcome variable, AFSS. Younger
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Table I. Baseline Demographic Data by AF Symptom Severity (AFSS); N = 378
Characteristic Age (mean ± SD) %Male Marital Status Married or permanent partner Widowed Separated Divorced Never married Education Less than high school High school grad Some college College or grad degree Comorbidities Heart failure Diabetes Hypertension Coronary artery disease Smoker Medications ACE/ARB Statin β-blocker Calcium channel blocker Digoxin Antiarrhythmic AF persistent (vs paroxysmal) PHQ9 HADS
Overall
Not Severe Symptoms (AFSS < 19) (N = 295)
Severe Symptoms (AFSS ≥ 20) (N = 83)
61.6 (13.3) 66.2%
62.4 (0.7) 70.8%
58.7 (1.5) 49.4%
73.1% 6.9% 1.5% 12.5% 5.9%
75.2% 6.2% 1.6% 11.1% 5.9%
65.9% 9.4% 1.2% 17.7% 5.9%
4.9% 20.8% 32.4% 41.9%
3.6% 18.7% 29.2% 48.5%
9.5% 28.6% 44.1% 17.9%
14.1% 19.1% 57.7% 15.1% 7.9%
13.7% 18.7% 56.1% 14.0% 6.5%
15.8% 20.7% 63.2% 19.4% 13.0%
40.3% 39.1% 59.1% 24.9% 10.6% 48.0% 57.6% 5.4 (5.2) 4.4 (3.6)
41.0% 37.8% 56.6% 24.4% 12.0% 48.5% 57.3% 4.2 (0.2) 3.8 (0.2)
37.7% 43.7% 68.2% 27.1% 5.8% 46.0% 58.6% 9.8 (0.6) 6.5 (0.5)
P Value 0.012*
Background: Symptoms attributed to atrial fibrillation (AF) are nonspecific, and it remains unclear what influences perception of symptoms. Anxiety or depression may be important in modulating perception of AF symptoms. However, few longitudinal studies have addressed this effect. Methods: A total of 378 patients with AF completed anxiety and depression severity questionnaires as well as AF symptom and frequency severity questionnaires. Patients were offered treatment strategies including catheter ablation or antiarrhythmic or rate-controlling medications. Patients were followed at 3-month intervals and completed follow-up questionnaires including repeat assessment of anxiety, depression, and AF symptoms. A method of generalized estimating equations was used for longitudinal analyses. Results: Analysis revealed that increased anxiety or depression was associated with increased AF symptom severity (AFSS), after adjusting for potential confounders. In both unadjusted and adjusted follow-up analyses, antiarrhythmic drug therapy or catheter ablation reduced AFSS (P < 0.001). However, none of anxiety severity, depression severity, or the perception of AF frequency severity improved significantly with AF treatment. Conclusions: Our results extend previous studies demonstrating that anxiety and depression are associated with worsened AFSS. Antiarrhythmic drug therapy or catheter ablation reduces AFSS but does not affect depression and anxiety symptoms. To achieve more comprehensive AF symptom relief, treatment of both AF and psychological comorbidities may be beneficial. (PACE 2014; 37:439–446) atrial fibrillation, depression, anxiety, quality of life
Introduction Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its prevalence is growing rapidly as the population ages.1 AF is associated with increased morbidity and mortality and has been shown to increase risk of stroke, heart failure, and to reduce quality of life.2 The treatment of AF places a significant burden on the U.S. healthcare system with an estimated annual cost of over six billion dollars.3 Symptoms attributed Dr. Sears has served as a consultant to Medtronic and has or has had research grants from Medtronic. These funds were directed to East Carolina University. Dr. Sears also has received speaker honorarium from Medtronic, Boston Scientific, St Jude Medical, and Biotronik. No other disclosures to report. Address for reprints: Anil K. Gehi, M.D., Cardiac Electrophysiology, Division of Cardiology, CB 7075, 160 Dental Circle, 6025 Burnett-Womack Bldg, Chapel Hill, NC 27599-7075. Fax: 919966-4366; e-mail: [email protected] Received May 13, 2013; revised August 8, 2013; accepted September 13, 2013. doi: 10.1111/pace.12292
to AF include fatigue, palpitations, shortness of breath, lightheadedness, and chest pain.4 Multiple studies have evaluated the negative impact of psychological distress on health outcomes in coronary artery disease, heart failure, and myocardial infarction.5 However, it remains unclear precisely what influences AF symptom perception and what effect psychological comorbidities may have on AF symptoms and quality of life. Several studies have demonstrated a strong correlation between AF symptoms and psychological comorbidities, such as anxiety and depression.6–9 However, only a single longitudinal study has investigated the relationship between psychological comorbidities and AF symptoms after treatment of persistent AF by direct current cardioversion.9 Further study into the directionality of the association between anxiety and depression and AF symptom severity (AFSS) is clearly necessary. Understanding the directionality of the relationship between depression and anxiety and AFSS may allow for a more targeted treatment approach in patients with AF and may
©2013, The Authors. Journal compilation ©2013 Wiley Periodicals, Inc. PACE, Vol. 37
April 2014
439
THOMPSON, ET AL.
ultimately lead to better patient outcomes. To help understand the interaction of psychological comorbidities and AF symptoms, we designed the present longitudinal analysis to evaluate whether the treatment of AF has an impact on AFSS as well as severity of anxiety and depression. To examine these relationships, we collected data on AFSS, depression, and anxiety in the Symptom Mitigation in AtRial FibrillaTion (SMART) longitudinal study of patients with AF. The aims of the study were to (1) establish the association of depression and anxiety on perceived AF symptom and frequency severity adjusting for important potential confounders and (2) to assess the effect of AF treatment strategies (rate control, antiarrhythmic drug therapy, or catheter ablation) on both perceived AF symptom and frequency severity and symptoms of depression or anxiety over time.
(HADS-A = 8–10), or probable anxiety (HADSA ≥ 11).8 The anxiety subset of the HADS-A demonstrated high internal consistency with a Cronbach’s alpha coefficient of 0.829, as well as high concurrent validity.11 Depression was measured using the Patient Health Questionnaire (PHQ-9), a 9-item tool to measure severity of depression over the previous 2 weeks (range 0–27).12 The PHQ-9 was chosen over other depression screening tools as it is simple to use and performs best when compared with the structured clinical interview for Diagnostic and Statistical Manual of Mental Disorders-IV as the standard criterion.13 Depression severity was categorized as none to minimal (PHQ-9 score = 0–3), mild to moderate (PHQ-9 = 4–9), and severe (PHQ-9 ≥ 10).14 The PHQ-9 questionnaire also has demonstrated high internal consistency with a Cronbach’s alpha coefficient of 0.83.14
Methods Characteristics of Participants The SMART study is a single-center prospective cohort study of patients with AF measuring AF symptoms and health outcomes. Details of the SMART study have been previously described.8 In brief, participants were enrolled through outpatient electrophysiology clinics at the University of North Carolina Chapel Hill when referred for management of AF. Participants were excluded if they were planning to move from the local area within 3 years of enrollment or were less than 18 years. The appropriate institutional review board approved the study and all participants provided written informed consent. Upon enrollment, participants completed a baseline questionnaire of general demographic information and measures of physical and psychological health. In addition, all participants underwent routine laboratory studies, electrocardiogram, and transthoracic echocardiogram. Between September 2009 and January 2012, 450 participants were enrolled. Of these patients, 378 completed anxiety, depression, and AFSS questionnaires and were included in this substudy.
AF Symptom and Frequency Severity
Anxiety and Depression The primary predictor variables in this study were current anxiety and depression. Anxiety severity was measured using the anxiety subset of the hospital anxiety and depression scale (HADS-A) containing seven items scored 0–3 based on recent symptoms.10 The sum of each question totaled to measure generalized anxiety, with higher scores equating to more anxiety (range 0–21). Anxiety severity was categorized as normal (HADS-A = 0–7), possible anxiety
440
AFSS was measured using the University of Toronto AF Symptom Severity Scale designed to measure symptom severity taking into account symptom duration, frequency, and severity, as well as disease burden. The symptom severity subscale assesses seven individual symptoms attributed to AF, scored on a 5-point Likert scale. Higher scores on the 0–35 scale indicated more severe symptoms.15 The symptom frequency subscale assesses frequency and duration by asking the questions: “How often on average does your irregular heart rhythm (atrial fibrillation) occur?” and “How long on average do the episodes of irregular heart rhythm last?” Responses are normalized and combined into a frequency score (range 2–20) with higher scores indicating more severe AF frequency. The AFSS scale has demonstrated a Cronbach’s alpha coefficent of 0.72 for symptom severity.16 Other Participant Characteristics Additional demographics included in the questionnaire were age, gender, marital status, and education. Education level was determined by asking participants their highest level of education completed with choices of no formal schooling, 5th grade or less, 6th to 8th grade, 9th to 11th grade, high school graduate or equivalent, some college/vocational school, college degree, or graduate degree. Responses were then categorized as less than high school, high school grad, some college, or college or graduate degree for analysis. Marital status was determined by asking if the participant was married or in a permanent partnership, widowed, separated, divorced, or never married. Choosing yes, no, retired, or student when asked, “Do you work in a paid job”
April 2014
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determined employment status. Other participant characteristics, including medical comorbidities and medications, were obtained by questionnaire and review of the patient’s electronic health record. Intervention and Follow-Up Treatment strategies were determined based on patient preference and physician recommendation. As patients were referred for management of AF, those with symptomatic AF were offered antiarrhythmic drug therapy. Those with symptomatic AF who had failed antiarrhythmic drug therapy were offered catheter ablation. Asymptomatic patients were generally treated with rate-controlling medications alone. Oral anticoagulation and antiplatelet therapies were used when necessary based on individual risk for stroke. Details of the radiofrequency catheter ablation procedure are described in a previous study.17 Patients were generally followed at 3-month intervals until deemed stable and appropriate to return to the care of their primary healthcare provider. Participants completed follow-up questionnaires including assessment of anxiety, depression, and AFSS at each follow-up visit. Statistical Analysis Participant characteristics were analyzed using measures of central tendency. Frequency was calculated for categorical variables and means and standard deviations were calculated for continuous variables. The predictor variables (anxiety and depression) were analyzed both as a continuous variable and categorized as none/minimal, mild/moderate, or severe for ease of interpretation. Bivariate analyses were performed to assess which baseline characteristics may be associated with significant AFSS (≥ 20, the highest quartile of AFSS score in the cohort) using Student’s ttests for continuous variables and χ 2 analyses for categorical variables. Next, bivariate and multivariate analyses of the association of depression or anxiety with the outcome variables perceived AFSS and perceived AF frequency severity at baseline were performed. Bivariate analyses were performed using Pearson’s correlation coefficient when analyzing depression and anxiety severity as a continuous variable and one-way analysis of variance when analyzing depression and anxiety severity as a categorical variable. Important potential confounders included in the analysis based on prior literature included age, gender, education level, and βblocker use.18–20 Multivariate analysis was performed using analyses of covariance adjusting for the aforementioned variables.
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Finally, follow-up data were used to determine the effect of AF treatment (rate control vs antiarrhythmic drug therapy vs catheter ablation) on perceived AFSS, perceived AF frequency severity, depression severity, and anxiety severity over time. For this analysis, short-term follow-up was defined as a follow-up date 90 (± 20) days after treatment and long-term follow-up was defined as a follow-up date >270 days after treatment. Each outcome variable was modeled as a time-varying variable. The method of generalized estimating equations (GEE) was used to analyze the effect of treatment on repeated measurements on the same subject over time.21 For this method, we used GEE with a Gaussian regression model including an interaction term between time (month) and treatment group. The presence of a significant interaction indicated a significant difference in the outcome over time by treatment group. The GEE model was run with and without adjustment for potential confounders. All analyses were performed with STATA, version 11 (StataCorp LP, College Station, TX, USA). Statistical tests were two-tailed, with P < 0.05 considered significant. Results Of the total 450 patients enrolled, 378 (84%) completed the PHQ-9 questionnaire, the HADSA questionnaire, and the AFSS questionnaire, and were included in this substudy. Baseline characteristics of the patient population are presented in Table I. The majority of participants in the study were older (mean age 61.7), male (66.2%), married or with a permanent partner (73.1%), and had at least some college education (74.3%). Comorbidities in the population included hypertension (57.7%), diabetes (19.1%), heart failure (14.1%), and coronary artery disease (15.1%). Over half of the study population was on a β-blocker (59.1%), whereas other medications were angiotensin-converting enzyme inhibitors (ACE) and angiotensin receptor blockers (ARB) (40.3%), statins (39.1%), calcium channel blockers (24.9%), digoxin (10.6%), and antiarrhythmic medications (48.0%). The majority of the 378 participants who completed the HADS questionnaire fell into the category of normal anxiety (82.4%), whereas 12.4% of participants had possible anxiety and 5.2% had probable anxiety (Table II). Of the 378 participants who completed the PHQ-9 questionnaire, 43.7% had no to minimal depression, 39.4% had mild to moderate depression, and 16.9% had severe depression (Table II). Bivariate analyses (Table I) were performed to assess which baseline participant characteristics were associated with the highest quartile of the primary outcome variable, AFSS. Younger
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Table I. Baseline Demographic Data by AF Symptom Severity (AFSS); N = 378
Characteristic Age (mean ± SD) %Male Marital Status Married or permanent partner Widowed Separated Divorced Never married Education Less than high school High school grad Some college College or grad degree Comorbidities Heart failure Diabetes Hypertension Coronary artery disease Smoker Medications ACE/ARB Statin β-blocker Calcium channel blocker Digoxin Antiarrhythmic AF persistent (vs paroxysmal) PHQ9 HADS
Overall
Not Severe Symptoms (AFSS < 19) (N = 295)
Severe Symptoms (AFSS ≥ 20) (N = 83)
61.6 (13.3) 66.2%
62.4 (0.7) 70.8%
58.7 (1.5) 49.4%
73.1% 6.9% 1.5% 12.5% 5.9%
75.2% 6.2% 1.6% 11.1% 5.9%
65.9% 9.4% 1.2% 17.7% 5.9%
4.9% 20.8% 32.4% 41.9%
3.6% 18.7% 29.2% 48.5%
9.5% 28.6% 44.1% 17.9%
14.1% 19.1% 57.7% 15.1% 7.9%
13.7% 18.7% 56.1% 14.0% 6.5%
15.8% 20.7% 63.2% 19.4% 13.0%
40.3% 39.1% 59.1% 24.9% 10.6% 48.0% 57.6% 5.4 (5.2) 4.4 (3.6)
41.0% 37.8% 56.6% 24.4% 12.0% 48.5% 57.3% 4.2 (0.2) 3.8 (0.2)
37.7% 43.7% 68.2% 27.1% 5.8% 46.0% 58.6% 9.8 (0.6) 6.5 (0.5)
P Value 0.012*
