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Birmingham/Atlanta VA Geriatric Research Education and Clinical Center, Atlanta VA Medical Center, Decatur, GeorgiaDepartment of Medicine, Emory University, Atlanta, Georgia
Section on Cardiology, Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North CarolinaEpidemiological Cardiology Research Center (EPICARE), Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
It is unclear if patients who have atrial fibrillation (AF) have a greater fall risk compared with those in the general population and if falls increase mortality beyond that observed in AF. A total of 24,117 (mean age 65 ± 9.3 years; 55% women; 38% black) participants from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study were included. AF was identified from baseline electrocardiogram data and by self-reported history. Falls were considered present if participants reported ≥2 falls within the year before the baseline examination. Logistic regression was used to examine the relationship between prevalent AF and falls. Cox regression was used to examine the risk of death in those with AF and falls, separately and in combination, compared with those without either condition. A total of 2,007 participants (8.3%) had baseline AF and 1,655 (6.7%) reported falls. A higher prevalence of falls was reported in those with AF (n = 209; 10%) than those without AF (n = 1,446; 6.5%; p <0.0001). After adjustment for fall risk factors, AF was significantly associated with falls (odds ratio 1.22, 95% confidence interval [CI] 1.04 to 1.44). Compared with no history of AF or falls, the concomitant presence of AF and falls (hazard ratio [HR] 2.12, 95% CI 1.64 to 2.74) was associated with a greater risk of death than AF (HR 1.44, 95% CI 1.28 to 1.62) or falls (HR 1.61, 95% CI 1.42 to 1.82). In conclusion, patients with AF are more likely to report a history of falls in REGARDS. Additionally, participants with AF who report falls have an increased risk of death than those with either condition in isolation.
Atrial fibrillation (AF), the most common sustained arrhythmia encountered in clinical practice, disproportionately affects older adults with a prevalence reaching 9% in this population.
Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study.
SAFE-T Investigators Quality of life and exercise performance in patients in sinus rhythm versus persistent atrial fibrillation: a Veterans Affairs Cooperative Studies Program Substudy.
Decreased exercise tolerance in AF potentially predisposes to conditions associated with falls, such as impaired mobility and decreased muscle strength.
This would suggest that AF possibly is associated with an increased fall risk, but this hypothesis has not been explored. Additionally, those with AF who fall possibly represent a population more likely to experience adverse outcomes and a greater mortality risk. Therefore, the purpose of this analysis was to examine the cross-sectional association between AF and falls in the REasons for Geographic And Racial Differences in Stoke (REGARDS) study and also to determine whether the combination of AF and falls is associated with a greater mortality risk compared with either condition in isolation.
Methods
Details of REGARDS have been published previously.
Briefly, REGARDS was designed to identify causes of regional and racial disparities in stroke mortality. The study population over sampled blacks and persons residing in the stroke belt (North Carolina, South Carolina, Georgia, Alabama, Mississippi, Tennessee, Arkansas, and Louisiana) from January 2003 to October 2007. A total of 30,239 participants were recruited from a commercially available list of residents using postal mailings and telephone data. Demographic information and medical histories were obtained using a computer-assisted telephone interview (CATI) system that was conducted by trained interviewers. Additionally, a brief in-home physical examination was performed 3 to 4 weeks after the telephone interview. During the in-home visit, trained staff collected information regarding medications, blood and urine samples, and a resting electrocardiogram.
For the purpose of this analysis, participants were excluded with data anomalies (n = 56), missing follow-up data (n = 490), missing AF data (n = 691), and missing baseline characteristics (n = 4,885). A total of 24,117 (mean age 65 ± 9.3 years; 55% women; 38% black) participants were included in the final analysis.
Fall history was self-reported during the CATI surveys. Consistent with recent guidelines, subjects were classified as having a positive fall history if they reported ≥2 falls within the year before the CATI survey.
Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society Summary of the Updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons.
AF was identified in study participants from baseline electrocardiogram data and also by self-reported history of a physician diagnosis during the CATI surveys. The electrocardiograms were read and coded at a central reading center (Epidemiological Cardiology Research Center, Wake Forest School of Medicine, Winston-Salem, North Carolina) by electrocardiographers who were blind to other REGARDS data. Self-reported AF was defined as an affirmative response to the following question: “Has a physician or a health professional ever told you that you had atrial fibrillation?”
All-cause mortality was assessed by semiannual telephone follow-up and contact with proxies provided by the participant on recruitment. Subsequently, the date of death was confirmed through linkage with the Social Security and National Death Index or by death certificates. Mortality data were complete through March 31, 2014.
Age, gender, race, income, education, exercise habits, alcohol use, and smoking status were self-reported. Annual household income was dichotomized at $20,000. Education was categorized into “high school or less” or “some college or more.” Cognition was assessed over the telephone using the 6-item screener, which evaluates global cognitive function.
Scores range from 0 to 6, with lower scores indicating worse cognition, and cognitive impairment was defined as a score ≤4. The presence of depressive symptoms was defined as a score of ≥4 on the 4-item Center for Epidemiologic Studies Depression Scale.
Low scores are typical of someone who experiences many limitations in physical activities, including bathing or dressing, whereas high scores represent someone who is able to perform these types of activities without limitations. Scores below the age- and sex-specific twenty-fifth percentile were used to define impaired mobility.
Exercise was dichotomized at ≥4 times per week and <4 times per week. Smoking was defined as ever (e.g., current and former) or never smoker. Alcohol use was classified by the number of drinks per week using the following criteria: none, moderate (1 to 2 drinks/day for men and 1 drink/day for women), and heavy (>2 drinks/day for men and >1 drink/day for women). Fasting blood samples were obtained and assayed for serum glucose, total cholesterol, and high-density lipoprotein (HDL) cholesterol. Diabetes was defined as a fasting glucose level ≥126 mg/dl (or a nonfasting glucose ≥200 mg/dl in those failing to fast) or self-reported diabetes medication use. The current use of aspirin and antihypertensive medications was self-reported. The use of warfarin and benzodiazepines was ascertained during the in-home visit by pill bottle review. After the participant rested for 5 minutes in a seated position, blood pressure was measured using a sphygmomanometer. Two values were obtained following a standardized protocol and averaged. Using baseline electrocardiogram data, left ventricular hypertrophy was defined by the Sokolow–Lyon Criteria.
Coronary heart disease was ascertained by self-reported history of myocardial infarction, coronary artery bypass grafting, coronary angioplasty or stenting, or if evidence of previous myocardial infarction was present on the baseline electrocardiogram. Baseline stroke was ascertained by participant self-reported history. Cardiovascular disease was the composite of baseline coronary heart disease and stroke.
Categorical variables were reported as frequency and percentage, whereas continuous variables were reported as mean ± standard deviation. Statistical significance for categorical variables was tested using the chi-square method and the Wilcoxon rank sum procedure for continuous variables. Logistic regression was used to compute odds ratios (OR) and 95% confidence intervals (CI) for the association between AF and fall history at baseline. Multivariate models were adjusted for factors known to influence falls: Model 1 included age, sex, and race; Model 2 included Model 1 covariates plus body mass index, cognitive impairment, mobility impairment, alcohol consumption, exercise habits, diabetes, antihypertensive medications, and benzodiazepine use.
Subgroup analyses were performed by age (dichotomized at 65 years), sex (male vs female), race (black vs white), cognitive impairment, mobility impairment, and benzodiazepine use using a stratification technique and comparing models with and without interaction terms. We also examined the associations between falls, AF, and all-cause mortality using the following groups: no AF + no falls (reference group), no AF + falls, AF + no falls, and AF + falls. Incidence rates per 1,000 person-years were calculated for each group. Kaplan–Meier estimates were used to compute the survival probability for each category and the differences in estimates were compared using the log-rank procedure.
Follow-up time was defined as the time from the in-home visit until death, loss to follow-up, or the end of follow-up (March 31, 2014). Cox regression was used to compute hazard ratios (HR) and 95% CI for the association between the aforementioned categories and all-cause mortality. Multivariate models were adjusted using the following models: Model 1 adjusted for age, sex, race, education, income, and geographic region; Model 2 included covariates in Model 1 with the addition of systolic blood pressure, HDL cholesterol, total cholesterol, body mass index, diabetes, antihypertensive medications, warfarin, lipid-lowering therapies, left ventricular hypertrophy, and cardiovascular disease. A sensitivity analysis was performed to determine if participants with AF and falls have an increased risk of mortality compared with those who have AF alone and also in those with falls alone. Statistical significance for all comparisons including interactions was defined as p <0.05. SAS version 9.3 (Cary, North Carolina) was used for all analyses.
Results
A total of 2,007 participants (8.3%) had AF and 1,655 (6.7%) reported falls at baseline. Falls were more likely to be reported in those with AF (n = 209; 10%) than those without AF (n = 1,446; 6.5%; p <0.0001). Baseline characteristics by AF are shown in Table 1.
Statistical significance for categorical variables was tested using the chi-square method and for continuous variables the Wilcoxon rank sum was used.
No (n=22,110)
Yes (n=2,007)
Age, mean (SD) (years)
64 (9.3)
68 (9.6)
<0.0001
Men
10,013 (45%)
943 (47%)
0.14
Black
8,903 (40%)
697 (35%)
<0.0001
Region
Stroke buckle
4,604 (21%)
458 (23%)
Stroke belt
7,670 (35%)
687 (34%)
Non-belt
9,836 (44%)
862 (43%)
0.10
Education, high school or less
8,138 (37%)
836 (42%)
<0.0001
Annual income, <$20,000
3,650 (17%)
433 (22%)
<0.0001
Exercise, ≥4 times per week
6,709 (30%)
539 (27%)
0.0011
Alcohol use
Heavy
897 (4.1%)
66 (3.3%)
Moderate
7,628 (35%)
626 (31%)
None
13,585 (61%)
1,315 (66%)
0.0012
Cognitive impairment
1,279 (5.8%)
126 (6.3%)
0.37
Depressive symptoms
2,156 (9.8%)
309 (15%)
<0.0001
Mobility impairment
3,725 (17%)
580 (29%)
<0.0001
Ever smoker
11,860 (54%)
1,169 (58%)
<0.0001
Diabetes
4,421 (20%)
502 (25%)
<0.0001
Systolic blood pressure, mean (SD) (mm Hg)
127 (17)
128 (18)
0.020
Body mass index, mean (SD) (kg/m2)
29 (6.1)
29 (6.4)
0.49
Total cholesterol, mean (SD) (mg/L)
192 (40)
185 (41)
<0.0001
HDL-cholesterol, mean (SD) (mg/L)
52 (16)
50 (16)
<0.0001
Aspirin
9,489 (43%)
1,032 (51%)
<0.0001
Antihypertensive medications
11,414 (52%)
1,322 (66%)
<0.0001
Lipid-lowering medications
7,195 (33%)
851 (42%)
<0.0001
Warfarin
390 (1.8%)
435 (22%)
<0.0001
Benzodiazepines
1,078 (4.9%)
167 (8.3%)
<0.0001
Left ventricular hypertrophy
2,146 (9.7%)
208 (10%)
0.34
Cardiovascular disease
4,271 (19%)
830 (41%)
<0.0001
HDL = high-density lipoprotein; SD = standard deviation.
∗ Statistical significance for categorical variables was tested using the chi-square method and for continuous variables the Wilcoxon rank sum was used.
After adjustment for demographics and fall risk factors, AF was significantly associated with falls (Table 2). The association between AF and falls did not differ when stratified by age, gender, race, cognitive impairment, mobility impairment, and benzodiazepine use (Table 3).
Table 2Association of atrial fibrillation with falls (N=24,117)
Model 2 adjusted for Model 1 covariates with the addition of body mass index, cognitive impairment, mobility impairment, alcohol consumption, exercise habits, diabetes, antihypertensive medications, and benzodiazepine use.
OR (95%CI)
P-value
No Atrial Fibrillation
1.0
-
1.0
-
Atrial Fibrillation
1.64 (1.40, 1.91)
<0.0001
1.21 (1.03, 1.43)
0.018
CI = confidence interval; OR = odds ratio.
∗ Model 1 adjusted for age, sex, and race.
† Model 2 adjusted for Model 1 covariates with the addition of body mass index, cognitive impairment, mobility impairment, alcohol consumption, exercise habits, diabetes, antihypertensive medications, and benzodiazepine use.
Model 2 adjusted for Model 1 covariates with the addition of body mass index, cognitive impairment, mobility impairment, alcohol consumption, exercise habits, diabetes, antihypertensive medications, and benzodiazepine use.
† Model 2 adjusted for Model 1 covariates with the addition of body mass index, cognitive impairment, mobility impairment, alcohol consumption, exercise habits, diabetes, antihypertensive medications, and benzodiazepine use.
Over a median follow-up of 7.6 years, a total of 3,092 (13%) deaths occurred. A higher death rate was observed for those with AF + falls (51.2 per 1,000 person-years) compared with those with AF + no falls (34.4 per 1,000 person-years), no AF + falls (29.8 per 1,000 person-years), and those with no AF + no falls (15.6 per 1,000 person-years). The unadjusted survival curves are shown in Figure 1.
Figure 1Unadjusted survival by atrial fibrillation and falls∗. ∗Kaplan–Meier estimates are statistically different (log-rank p <0.0001). AF = atrial fibrillation.
In a Cox regression analysis adjusted for demographics, cardiovascular risk factors, and potential confounders, a greater risk of death was observed for those with AF + falls compared with either condition in isolation (Table 4). Additionally, falls were associated with an increased risk of death in those with AF (n = 2,007; HR 1.42, 95% CI 1.08 to 1.87) and AF was associated with increased mortality in those who reported falls (n = 1,655; HR 1.47, 95% CI 1.09 to 1.99).
Table 4Association of atrial fibrillation, falls, and all-cause mortality (N=24,117)
Model 2 adjusted for Model 1 covariates with the addition of systolic blood pressure, HDL cholesterol, total cholesterol, body mass index, diabetes, antihypertensive medications, warfarin, lipid-lowering therapies, left ventricular hypertrophy, and cardiovascular disease.
HR (95%CI)
P-value
No AF + No Falls
2,332/18,332
1.0
-
1.0
-
No AF + Falls
286/1,446
1.85 (1.63, 2.09)
<0.0001
1.61 (1.42, 1.82)
<0.0001
AF + No Falls
411/1,798
1.80 (1.61, 1.99)
<0.0001
1.44 (1.28, 1.62)
<0.0001
AF + Falls
63/209
2.88 (2.24, 3.69)
<0.0001
2.12 (1.64, 2.74)
<0.0001
AF = atrial fibrillation; CI = confidence interval; HDL = high-density lipoprotein cholesterol; HR = hazard ratio.
∗ Model 1 adjusted for age, sex, race, education, income, and geographic region.
† Model 2 adjusted for Model 1 covariates with the addition of systolic blood pressure, HDL cholesterol, total cholesterol, body mass index, diabetes, antihypertensive medications, warfarin, lipid-lowering therapies, left ventricular hypertrophy, and cardiovascular disease.
In this analysis, AF was associated with a history of falls independent of several fall risk factors. Additionally, a greater mortality risk was observed for participants with AF who reported falls compared with those with either condition in isolation. To our knowledge, we are the first to describe the relation between AF and falls and the increased mortality in this population.
A recent systematic review has identified several fall risk factors in older adults that include the following: mobility impairment, previous falls, visual impairment, depression, advanced age, female sex, low body mass index, cognitive impairment, diabetes, and the use of antihypertensive and antianxiety medications.
Notably, arrhythmias such as AF have not been previously described as potential fall risk factors. The findings of this analysis suggest that AF potentially increase one's fall risk. Additionally, most reports have focused on people aged ≥65 years. However, physical immobility and chronic disease have been identified as fall risk factors in adults as young as 55 years and demonstrate that falling is not limited to the elderly.
Therefore, our data support that falls are not limited to subjects who are >65 years and suggest that AF is a condition with an increased fall risk independent of age.
AF is associated with several conditions that also are associated with falls. For example, advanced age, diabetes, depression, and cognitive decline are commonly found in individuals with AF.
Therefore, it is plausible that the association between AF and falls is explained by these conditions and AF merely represents a marker for subjects who are likely to fall. However, our results remained statistically significant after adjustment for several fall risk factors. Alternative explanations are related to the medications used to treat AF and the symptoms (e.g., presyncope) associated with failure of rate- and rhythm-control therapies. Additionally, subjects with AF have been observed to have decreased exercise tolerance which possibly leads to deconditioning and impaired mobility.
SAFE-T Investigators Quality of life and exercise performance in patients in sinus rhythm versus persistent atrial fibrillation: a Veterans Affairs Cooperative Studies Program Substudy.
Data from REGARDS have shown that falls are independently associated with an increased mortality risk, and similar observations have been reported in patients with hemodialysis.
Our results confirm that falls are associated with an inherent mortality risk and identify a subpopulation of AF with decreased survival. The increased mortality in those who fall has been explained by underlying conditions that are linked with functional impairment.
That is to say, subjects who fall have conditions that increase their mortality, and falls likely are consequences of the impaired mobility that accompanies such chronic conditions. Additional explanations include the possible increased bleeding risk associated with anticoagulation. Unfortunately, bleeding complications were not ascertained in our study population. However, a systematic review has suggested that the bleeding risk associated with falls in patients with AF who receive anticoagulation therapies is much smaller than what is perceived by clinicians.
Nonetheless, although we were unable to identify the exact cause of death, we have identified a subpopulation of AF with increased mortality. Further research is needed to explore why patients with AF who fall have an increased risk of death compared with those who do not fall.
More than 1/3 of community-living adults ≥65 years fall each year.
However, our findings suggest that patients with AF are more likely to report falls and those with both conditions have an increased mortality. Several interventions have been implemented to reduce the risk of falls, including the discontinuation of psychoactive medications and exercise programs to increase strength and functional mobility.
Our results should be interpreted in the context of several limitations. Falls and certain cases of AF were ascertained by self-reported history and subjected our analyses to misclassification bias. Similarly, several baseline characteristics were self-reported. Additionally, we were unable to determine the cause of death among study participants, and it is unclear if the increased mortality observed in participants with AF who reported falls was related to the falls or chronic diseases that are highly prevalent in this population. Furthermore, although we adjusted for potential confounders, we acknowledge that residual confounding remains a possibility.
Acknowledgment
The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at http://www.regardsstudy.org. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health.
Disclosures
The authors have no conflicts of interest to report.
References
Go A.S.
Hylek E.M.
Phillips K.A.
Chang Y.
Henault L.E.
Selby J.V.
Singer D.E.
Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study.
Quality of life and exercise performance in patients in sinus rhythm versus persistent atrial fibrillation: a Veterans Affairs Cooperative Studies Program Substudy.
Funding: This research project was supported by a cooperative agreement U01 NS041588 from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Service.
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