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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 8  |  Issue : 2  |  Page : 84-88

Cognitive impairment in patients with atrial fibrillation without stroke


1 Department of Neurology, Christian Medical College, Ludhiana, Punjab, India
2 Department of Cardiology, Christian Medical College, Ludhiana, Punjab, India
3 Department of Medicine, Division of Neurology, University of Alberta, Edmonton, AB, Canada

Date of Submission24-Jul-2019
Date of Decision18-Oct-2019
Date of Acceptance25-Oct-2019
Date of Web Publication27-Oct-2021

Correspondence Address:
Mahesh Pundlik Kate
Department of Medicine, Division of Neurology, University of Alberta Hospital, Edmonton, AB T6G 2B7
Canada
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/cjhr.cjhr_81_19

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  Abstract 


Background: Vascular dementia is the second leading cause of dementia worldwide; however, the causation is multifactorial and may be preventable. There is increasing evidence that atrial fibrillation (AF) is independently correlated with cognitive decline. Assessing cognition in an outpatient setting is challenging. Gait speed may be able to transcend language in assessing cognition. We aim to assess cognitive impairment in patients with AF without known history of stroke with gait speed. Methods: This was a prospective, observational study of patients attending cardiology outpatient department. Patients were screened for a history of valvular or nonvalvular AF. Controls were patients without AF. Patients underwent structured interview, Montreal cognitive assessment (MoCA), and gait velocity assessment. Gait velocity and MoCA scores were compared in control and cases using Student's t-test. Results: A total of 189 patients were consented; 88 cases with AF and 101 controls. Mean ± standard deviation age was 60 ± 12 years. The median (interquartile range) gait velocity in patients with AF and nonAF was similar (0.80 [0.65–0.93] m/s vs. 0.80 [0.65–0.93] m/s, P = 0.708). The mean MoCA scores in patients with AF and without AF were also similar (17.38 ± 5.66 vs. 18.36 ± 5.30, P = 0.229). A cutoff value of <0.80 m/s had sensitivity of 66% and specificity of 61.4% to diagnose dementia. Conclusion: There is a high occurrence of cognitive deficits in patients with and without AF visiting a cardiology outpatient clinic. Future studies are needed to target this group of the patient to reduce the burden of vascular dementia.

Keywords: Atrial fibrillation, cognition, dementia, gait velocity


How to cite this article:
Jaison V, Sharma S, Khatter H, Calton R, Pandian JD, Kate MP. Cognitive impairment in patients with atrial fibrillation without stroke. CHRISMED J Health Res 2021;8:84-8

How to cite this URL:
Jaison V, Sharma S, Khatter H, Calton R, Pandian JD, Kate MP. Cognitive impairment in patients with atrial fibrillation without stroke. CHRISMED J Health Res [serial online] 2021 [cited 2021 Dec 2];8:84-8. Available from: https://www.cjhr.org/text.asp?2021/8/2/84/329452




  Introduction Top


Vascular dementia is the second leading cause of dementia worldwide; however, the causation is multifactorial and may be preventable.[1] The incidence of dementia and atrial fibrillation (AF) increases with age. There is increasing evidence that AF is independently correlated with cognitive decline. Stroke due to AF may cause cognitive decline and dementia by strategic infarct or recurrent stroke.[2] However, the proposed mechanisms of cognitive decline in patients with AF without previous history of stroke are hemodynamic changes, silent infarcts, and associated inflammation.[3] AF is associated with decrease in cardiac output and resulting cerebral hypoperfusion.[4] AF is also associated with hypertension and ischemic heart disease. It is associated with increased incidence of silent infarction, and silent infarcts are independently associated with cognitive decline.[5],[6] Various inflammatory biomarkers are at increased levels in patients with AF which are associated with pro-thrombotic state; however, their role in cognitive decline remains to be elucidated.[7],[8]

Assessing cognition is challenging in India as most of the cognitive assessment tools have been developed in English. In India, this problem is further compounded as there are more than 12 major languages. These tools will have to be developed or validated in all those languages, which is rather cumbersome. Furthermore, due to increased burden of patients per doctor, the proposed tool should be of short duration and should be administered easily with minimal training.

Motoric cognitive risk syndrome criteria use gait speed and subjective cognitive symptoms to identify at-risk patients. It has been validated in a multi-country study.[9] It proposes that patients with slow gait are likely to develop further cognitive decline and dementia. Thus, gait velocity is able to transcend the language barrier. We aim to assess cognitive impairment in patients with AF without known history of stroke with gait speed.


  Methods Top


Study design and patients

This was a prospective, observational study. Patients attending the cardiology outpatient department (OPD) at a tertiary care hospital were screened for a history of valvular and nonvalvular AF. Patients >18 years of age, previously diagnosed to have AF and willing to participate were enrolled. The patients were asked if they had difficulty in walking on level ground or taking a flight of stairs. If they had difficulty they were excluded. In addition if patients had active arthritis or arthropathy, weakness of limb/s, fracture of lower limb and radiculopathy pain, they were excluded. Patients with the past history of dyspnea on exertion (New York Heart Association Class >1), stroke, history of recent infection, headache, depression, delirium, aphasia, history of hospitalization for any reason in the past 3 months were excluded from the study.

This study was conducted after the Institutional Research Committee and Institutional Ethics Committee Approval, and informed consent was obtained from all patients who underwent a structured interview. Demographic data, risk factor profile (hypertension, diabetes mellitus [DM], coronary artery disease [CAD], valvular heart disease, and rheumatic heart disease), CHA2DS2-VASc score, HAS-BLED score, and ongoing medications were abstracted on a detailed pro forma.[10] The CHA2DS2-VASc score to assess the risk of stroke and systemic thromboembolism in patients with AF was recorded. All patients were being treated according to the standard of care.

Cognition assessment

Cognition was assessed using Montreal cognitive assessment (MoCA).[11] MoCA scale assess different domains including visuospatial, executive, naming, memory, attention, language, abstraction, and orientation. The maximum score is 30 and minimum score is 0. A trained study nurse administered the MoCA. The recommended cutoff for the diagnosis of minimal cognitive impairment using MoCA is <26. The recommended cutoff for the diagnosis of dementia is <22. To adjust for education <12 years MoCA allows one point to be added to the total score.[11],[12]

Gait velocity was assessed using 4-min walk test.[9] A trained examiner administered the test and assessed the distance traveled in 4 min. The patients were taken to an isolated corridor 11.5 m in length and 5 feet wide and asked to walk back and forth at a “comfortable pace neither too fast nor too slow.” The gait speed was calculated as distance traveled (in m) over 4 min divided by 4 min (240 s). Beck depression inventory and fatigue severity scale were also administered.[13],[14] The functional disability was assessed using the Modified Rankin Scale.

Echocardiographic parameters

All patients had undergone electrocardiography and transthoracic echocardiography as part of AF workup.

Statistical analysis

The primary outcome measure was gait velocity. In the descriptive analysis, continuous variables were expressed as mean ± standard deviation or median (interquartile range [IQR]) and categorical variables were expressed as count (percentage). Chi-square test was used to compare categorical variables in each group. Fisher's exact test was used when the expected count was <5. To obtain the comparison of continuous variables independent t-test was used for normally distributed data or Mann–Whitney U-test was used for nonnormally distributed data. Pearson correlation was used to find the correlation between MOCA score and gait velocity (m/s). Multivariate linear regression was used to find the influence of predictors on MOCA score. The significance level was set at P < 0.05. Receiver operating curve (ROC) was constructed by plotting test sensitivity against (1-specificity). All statistical analysis was performed using the SPSS, version 21.0. Armonk, NY: IBM corp.


  Results Top


Patient characteristics

A total of 189 consecutive patients were recruited for the study; 88 with AF and 101 controls without AF (non-AF). The mean age of the participants was 60 ± 12 years with a mean body mass index (BMI) of 26.5 ± 5.7 kg/m2. The average MoCA scores and median (IQR) gait velocity were 17.9 ± 5.5 and 0.80 (0.65–0.93) m/s. There was no difference between the groups in age, gender, or BMI. Risk factors such as hypertension, DM, and CAD were significantly higher in the non-AF group, while valvular heart disease and rheumatic heart disease were more common in the AF group [Table 1].
Table 1: Demographic, education, and risk factor profile of atrial fibrillation and nonatrial fibrillation patients

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Cognition

Language of preference was Punjabi in 60% of the participants and the rest opted for Hindi. A total of 7 (6.9%) patients had normal cognition, 24 (23.8%) had mild cognitive impairment (MCI), and 70 (69.3%) had dementia in the non-AF group. In patients with AF group, 3 (3.4%) had normal cognition, 16 (18.2%) had MCI and 69 (78.4%) had dementia, there was no difference in the patients with AF and non-AF (P = 0.3). There was no difference in MoCA scores and gait velocity in both groups [Table 2]. The subsection of MoCA compared with gait velocity did not show any significant difference [Table 3].
Table 2: Study assessments in atrial fibrillation and nonatrial fibrillation patients

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Table 3: Mean subdomain scores in the Montreal cognitive assessment scale in atrial fibrillation and nonatrial fibrillation patients

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Over 50% had more than 10 years of education, while the percentage of illiterate was around 20%, which was similar in both the groups [Figure 1]. The median MoCA scores according to the education levels were similar 17.5 (9–26), 20 (7–28), 19 (7–29), 21 (7–28), and 13 (10–22) for primary school level, middle school level, secondary school level, graduate level, and postgraduate level, respectively (P = 0.08). On multivariate linear regression with MoCA as dependent variable after adjusting for hypertension, DM and CAD, gait velocity showed a correlation with MoCA (coefficient B = 9.567, P < 0.001) [Table 4]. However, when illiterate patients were excluded from the analyses, there was no correlation noted (−0.09 95% confidence interval [CI] [−7.4]−1.9; P = 0.2) between the gait velocity and MoCA.
Figure 1: Trend of Montreal cognitive assessment scale and education of patients with atrial fibrillation and nonatrial fibrillation

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Table 4: Multivariate linear regression to assess predictors of Montreal cognitive assessment score in the study population

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Receiver-operating curve

To identify a cutoff value of gait velocity to diagnose dementia, we plotted an ROC after excluding patients who were illiterate. The gold standard for the diagnosis of dementia was an education adjusted MOCA of <26. The area under the curve was 0.65 (95% CI 0.56–0.74, P = 0.001). The overall model quality was 0.56. At cutoff value of <0.80 m/s, the sensitivity was 66% and specificity was 61.4%.


  Discussion Top


In this study, we found that many patients visiting the cardiology OPD in North-West India have low MoCA scores irrespective of whether they had AF or not. Gait velocity can be performed in a busy OPD setting and be used for screening of cognitive impairment. However, normative data in patients with different education and age categories are needed to identify appropriate cutoff points. Patients with gait velocity of <0.80 m/s may be referred for further cognitive assessment and treatment.

In our study, the mean MoCA scores were 17.9 ± 5.5. This may be attributed to the large proportion of patients with lower education; however, MoCA allows for an addition of 1 score for those <12 years of education even with that the scores are well below the cutoff suggesting a significant cognitive impairment in these patients.[11] MoCA has not been tested in illiterate patients. Hence, the above conclusion cannot be adapted to those patients. Patients in our study had high frequency of known risk factors for poor cognition such as DM, hypertension, and CAD. The median CHA2DS2VASc score in our patients without AF was higher at 3. Similar findings have been noted in a Veteran Affairs cardiology clinic in Boston, where at least 50% of patients had cognitive impairment or mobility limitation.[15] In patients with heart failure, the prevalence of cognitive impairment is between 30% and 80%.[16] In the Chilean National Health Survey (2009–2010), it was noted that patients with ideal cardiovascular health (nonsmoker, ideal BMI, regular physical activity, good diet, ideal total cholesterol, blood pressure [BP], and fasting glucose) have low prevalence of cognitive impairment.[17]

Some studies define 0.80 m/s as pathological gait velocity, which is dependent on the method of measurement. In this study, though the mean gait velocity was 0.80 m/s in both the groups, a cutoff of <0.80 m/s had high sensitivity and specificity to diagnose dementia. Several factors affect gait velocity, and cardiac patients can have slower gait than normal due to heart failure, dyspnea on exertion, fatigue, or tachycardia. To accommodate this factor fatigue score was done on all patients, and it was found to be equal in both the groups, although significantly higher than the cutoff of 6.5. The mean ejection fraction was 55%. An additional factor to consider is frailty. In recent study, patients in cardiology and diabetes clinic were noted to have higher incidence of frailty. It was associated with cognitive impairment and sarcopenia.[18]

The analysis between AF and non-AF patients showed no significant difference in gait velocity or MOCA scores. This can be explained by the disproportionately high presence of confounders such as DM, hypertension, and CAD in non-AF group. In a community study from rural China high BP was an independent predictor of cognitive decline in people older than 60 years.[19] Furthermore, there may be a higher prevalence of cognitive impairment in India. In a recent community study from South Indian state, the prevalence of MCI was 26% in patients 60 years and older.[20]

The study has limitations because of the small sample size. A large number of our patients had cognitive impairment, which may indicate a high prevalence in the study population. The study is cross-sectional, and no follow-up was conducted. It is possible that there may have been fluctuation in cognition, and since the assessments were made in the OPD, patients may not have had optimal conditions to perform the MoCA test. The gait velocity also being low in this same population points to a high proportion of patients suffering from undiagnosed dementia in this study. The tools to evaluate cognition are cumbersome, and this shows that gait velocity can be used to screen patients for cognition, overcoming language, and educational barriers. The method used for gait velocity in this study is simple and can be easily reproduced.


  Conclusion Top


This study shows a high prevalence of cognitive impairment among patients visiting cardiology OPD. The findings of this study are clinically relevant, as routine screening of cognition is not done in cardiology OPD. The pathophysiology of low cognition in these patients needs further evaluation with advanced imaging studies to ascertain possible preventive and therapeutic targets.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Smith EE. Clinical presentations and epidemiology of vascular dementia. Clin Sci (Lond) 2017;131:1059-68.  Back to cited text no. 1
    
2.
Poggesi A, Inzitari D, Pantoni L. Atrial fibrillation and cognition: Epidemiological data and possible mechanisms. Stroke 2015;46:3316-21.  Back to cited text no. 2
    
3.
Andrade J, Khairy P, Dobrev D, Nattel S. The clinical profile and pathophysiology of atrial fibrillation: Relationships among clinical features, epidemiology, and mechanisms. Circ Res 2014;114:1453-68.  Back to cited text no. 3
    
4.
Cao L, Pokorney SD, Hayden K, Welsh-Bohmer K, Newby LK. Cognitive function: Is there more to anticoagulation in atrial fibrillation than stroke? J Am Heart Assoc 2015;4:e001573.  Back to cited text no. 4
    
5.
Vermeer SE, Prins ND, den Heijer T, Hofman A, Koudstaal PJ, Breteler MM, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med 2003;348:1215-22.  Back to cited text no. 5
    
6.
Silent brain infarction in nonrheumatic atrial fibrillation. EAFT Study Group. European atrial fibrillation trial. Neurology 1996;46:159-65.  Back to cited text no. 6
    
7.
Harada M, Van Wagoner DR, Nattel S. Role of inflammation in atrial fibrillation pathophysiology and management. Circ J 2015;79:495-502.  Back to cited text no. 7
    
8.
Lappegård KT, Pop-Purceleanu M, van Heerde W, Sexton J, Tendolkar I, Pop G, et al. Improved neurocognitive functions correlate with reduced inflammatory burden in atrial fibrillation patients treated with intensive cholesterol lowering therapy. J Neuroinflammation 2013;10:78.  Back to cited text no. 8
    
9.
Verghese J, Annweiler C, Ayers E, Barzilai N, Beauchet O, Bennett DA, et al. Motoric cognitive risk syndrome: Multicountry prevalence and dementia risk. Neurology 2014;83:718-26.  Back to cited text no. 9
    
10.
Lip GY. Implications of the CHA(2)DS(2)-VASc and HAS-BLED scores for thromboprophylaxis in atrial fibrillation. Am J Med 2011;124:111-4.  Back to cited text no. 10
    
11.
Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The montreal cognitive assessment, moCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53:695-9.  Back to cited text no. 11
    
12.
Jefferson AL, Gibbons LE, Rentz DM, Carvalho JO, Manly J, Bennett DA, et al. A life course model of cognitive activities, socioeconomic status, education, reading ability, and cognition. J Am Geriatr Soc 2011;59:1403-11.  Back to cited text no. 12
    
13.
Berg A, Lönnqvist J, Palomäki H, Kaste M. Assessment of depression after stroke: A comparison of different screening instruments. Stroke 2009;40:523-9.  Back to cited text no. 13
    
14.
Wei C, Zhang F, Chen L, Ma X, Zhang N, Hao J, et al. Factors associated with post-stroke depression and fatigue: Lesion location and coping styles. J Neurol 2016;263:269-76.  Back to cited text no. 14
    
15.
Orkaby AR, Orkaby AR, Qazi S, Champagne JM, Peralta A, Gaziano JM, et al. Prevalence of physical and cognitive frailty in VA Boston cardiology clinics. Innov Aging 2018;2:S1.  Back to cited text no. 15
    
16.
Cannon JA, McMurray JJ, Quinn TJ. 'Hearts and minds': Association, causation and implication of cognitive impairment in heart failure. Alzheimers Res Ther 2015;7:22.  Back to cited text no. 16
    
17.
García-Hermoso A, Ramírez-Vélez R, Ramírez-Campillo R, Izquierdo M. Prevalence of ideal cardiovascular health and its association with cognitive function in older adults: The Chilean National Health Survey (2009-2010). Rejuvenation Res 2018;21:333-40.  Back to cited text no. 17
    
18.
Tamura Y, Ishikawa J, Fujiwara Y, Tanaka M, Kanazawa N, Chiba Y, et al. Prevalence of frailty, cognitive impairment, and sarcopenia in outpatients with cardiometabolic disease in a frailty clinic. BMC Geriatr 2018;18:264.  Back to cited text no. 18
    
19.
Ren L, Bai L, Wu Y, Ni J, Shi M, Lu H, et al. Prevalence of and risk factors for cognitive impairment among elderly without cardio- and cerebrovascular diseases: A population-based study in rural China. Front Aging Neurosci 2018;10:62.  Back to cited text no. 19
    
20.
Mohan D, Iype T, Varghese S, Usha A, Mohan M. A cross-sectional study to assess prevalence and factors associated with mild cognitive impairment among older adults in an urban area of Kerala, South India. BMJ Open 2019;9:e025473.  Back to cited text no. 20
    


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