Stroke is the leading cause of disability and mortality in the United States. Every year, it affects 795 000 people in the United States. In India, the prevalence of stroke varies from 84 to 262/100 000 in rural areas to 334–424/100 000 in metropolitan areas. According to current population-based research, the incidence is around 119–145/100 000, with ischemic stroke accounting for 80%t of the cases .
Young adults account for between 10 and 14% of all ischemic strokes, and the number is rising, which is a big concern .
Stroke is a preventable disease that is the focus of a number of programs aimed at lowering stroke risk factors. The goal of primary prevention is to lower the risk of stroke in persons who are asymptomatic. Hypertension, carotid artery stenosis, heart illness, dyslipidemia, smoking, diabetes mellitus, sickle cell disease, obesity, physical inactivity, alcohol and drug misuse, and the metabolic syndrome are all conditions that can lead to death.
Vascular dementia is a type of dementia that develops after a stroke. However, not all stroke survivors who have cognitive impairment fit the dementia criteria. As a result, the term ‘vascular dementia’ has been replaced by ‘vascular cognitive impairment.’ During the early 3 months following a stroke, a large percentage of stroke survivors develop cognitive impairment. Despite the fact that poststroke cognitive impairment is common .
Stroke is a risk factor for cognitive impairment in 66% of people after a stroke, while Alzheimer’s disease affects 33%. This means that cognitive decline can be caused by stroke or Alzheimer’s disease, as well as other variables such as diabetes, atrial fibrillation, myocardial infarction, hypertension, age, medial temporal lobe atrophy, and white matter alterations, and a lack of education .
Stroke survivors often feel sleep difficulties, low motivation, low self-esteem, and fears about their future due to constraints and disabilities caused by brain lesions (lacunar infarcts and cortex). Depression and anxiety symptoms result from these psychological shifts and stressful conditions, affecting their executive function, memory, speed, and motor processing .
The frequency of lacunes and growing white matter hyperdensities (WMH) are linked to poorer Mini-Mental State Examination (MMSE) scores in nondisabled seniors .
White matter changes (WMC), global cerebral atrophy, and silent infarcts are all predictors of poststroke dementia .
There are various screening techniques for diagnosing cognitive impairment, the most common of which is the MMSE. The MMSE has shown considerable difficulty in detecting early dementia, with the majority of patients matching clinical criteria for MCI scores above 26, which is within the typical range for senior people. As a result, there is no universally approved and simply administered technique for assessing mild cognetive impairment (MCI). As a result, the Montreal Cognitive Assessment (MoCA) was created as a screening tool for patients with modest cognitive deficits and MMSE scores in the normal range. The sensitivity and specificity of the MoCA in individuals with MCI and healthy controls are investigated in this study .
Patients and methods
This study includes 72 Egyptian patients with recent ischemic stroke who completed the study from the total 112 patients, who were divided into two groups: Group 1 patients developed no cognitive dysfunction (n=45). Group 2 patients developed cognitive dysfunction (n=27). Patients were selected from the Neurology Department, Al-Azhar University Hospitals, Cairo, Egypt. This study included consecutive in-patients with acute first-ever ischemic stroke within hours to days from the onset of symptoms. Stroke is the occurrence of focal neurological deficit of acute onset due to cerebrovascular disease that is confirmed by computed tomography or MRI. Patients who did not develop cognitive dysfunction were cross-matched for sex, age, and socioeconomic status, and education level. All participants offered written consents either by themselves or by their first-degree relatives after explanation of the study for them.
Those included were Egyptians, with confirmed clinical picture and brain computed tomography and/or MRI scan after initial acute stroke within the first week, consent to participate in the study, and ability to provide information about patients cognitive functioning.
Those with a previous stroke, hemorrhagic stroke, patients with known renal disease, liver diseases, endocrinal (except diabetes mellitus), or metabolic disorders that affect cognitive function, traumatic causes of cerebrovascular stroke, sinus thrombosis and retinal infarction, prestroke dementia, depression, drug abuse, medications that affect cognitive function, and dysphasia were excluded.
The MMSE and MoCA were used to measure cognitive status at baseline within 14 days following stroke, as well as at 3 and 6 months follow-up, with a cutoff score of 26 [8,9].
If there was a decrease of two points in the measured scores across two time points, the MoCA and MMSE scores were considered to be declining. The move from one category to a more severe one defined the decline .
The National Institutes of Health Stroke Scale (NIHSS) was used to measure the severity of stroke at admission and discharge, as well as their modified Rankin scale (mRS) scores 3–6 months later to determine functional disability .
Large-artery atherosclerosis, cardioembolism, small-artery atherosclerosis, stroke of other determined cause, and stroke of unknown etiology were the subtypes of ischemic stroke identified by the Trial of Org 10172 in Acute Stroke Treatment criteria .
The ethics committee of Al-Azhar University’s Faculty of Medicine in Cairo approved the study. Each participant signed an informed consent form. The research is carried out in compliance with the 2013 revisions to the Helsinki standards.
Data were collected, tabulated, and formulated using IBM SPSS 24. (IBM Corp., Armonk, New York, USA). Categorical data were presented as frequency and percent after testing for normality using The Kolmogorov–Smirnov test. Numerical data were presented as mean, SD, range (minimum and maximum), and median. P value less than 5% was considered statistically significant.
The final sample was 72 patients who completed the study and fulfilled the inclusion and exclusion criteria from the total 119 patients.
The patients included were 44 males and 28 females with a mean age of 56.93±8.58 years and range 43.0–80.0. Of the patients, 33.3% were illiterate, 25% had primary school education, 19.4% had secondary school education and 22.2% had college education. Forty-four (61.1%) patients had stroke of the left hemisphere and 27 (37.5%) patients had stroke of the right hemisphere and one patient had bilateral stroke. Twenty-seven (37.5%) patients had cognitive impairment by MoCA test with a cutoff score of 26 at baseline with a mean score of 20.37±3.24; 24 (33.3%) patients at 3 and 6 months follow-up with a mean score of 21.26±4.55; 15 (20.8%) patients had cognitive impairment by MMSE with cutoff score of 26 at baseline with a mean score of 27.06±3.22; and 13 (18.1%) patients at 3 and 6 months’ follow-up with a mean score of 27.35±3.30. The contributing vascular lesions to cognitive disturbance were large artery in 18 (66.7%) cases and small vessel lacunar lesions in nine (33.3%) patients.
After admission, the mean NIHSS score was 5.72±8.09 and at discharge, it was 6.68±8.60, and the mean mRS score was 1.29±1.32.
Patients were divided into two groups. Group 1 with no cognitive impairment, group 2 with cognitive impairment. The two groups were classified mainly according to the MoCA test and then compared with the MMSE test.
The mean age in group 1 is 53.62±6.98 and median 53.0 and in group 2 is 62.44±8.27 and median 62.0 showing significant difference between two groups (P<0.001). The level of schooling has an impact on cognitive state as the illiterate score was 59.3% (χ2=13.067, P<0.001), hypertension and diabetes millets were a significant factor (70.4%) (P=0.007, χ2=7.170) and 66.7% (P=0.028, χ2=4.800), respectively. Most of the cases are affected by lesions in the left hemisphere with a percentage of 81.5% with significant difference at (P=0.006, χ2=6.641), lacunar lesion (33.3%) (P=0.013, χ2=6.815), and posterior cerebral artery (PCA) lesion (3.7%) with significant difference (P=0.024, χ2=6.014). Serum lipids that affect the cognitive status are triglycerides (186.9±68.06) (P<0.001), low-density lipoprotein (184.7±55.17) (P=0.011), and high-density lipoprotein as protective factors (251.3±49.67) (P<0.001). We considered mRS score less than or equal to 1 as a favorable functional outcome and mRS score more than or equal to 2 as a poor functional outcome. Most of the patients (n=46, 63.9%) had mRS score less than or equal to 1 (good functional outcomes), while most of the cognitively impaired patients (n=19, 70.3%) (2.07±1.36) had mRS more than or equal to 2, and (n=13) 48.1%) of them had score 2 (P<0.001). Poorer functional outcome patients had significantly lower scores of the MMSE and the MoCA. When using 2 as a median split of NHSS scores at baseline, patients with NIHSS scores less than or equal to 2 (median=1, range 0–2) were considered as patients with less severe strokes and with an NIHSS score >2 (median=5, range 3–18) were considered as having more severe strokes.
At 3–6 months after a stroke, the NIHSS predicted mRS scores (10.26±10.45/13.15±9.86) (median 4/13) at admission and discharge (P<0.001), respectively. In addition to baseline NIHSS scores, baseline MMSE scores offered a minor but statistically significant prediction of functional outcomes, whereas baseline MoCA scores did not (MMSE: R2 changes=0.006, P=0.03; MoCA: R2 changes=0.004, P=0.083).
In patients with an NIHSS score more than 2, baseline MMSE and MoCA had a significant prediction for functional outcomes at 3 and 6 months in addition to baseline NIHSS scores, while baseline neurocognitive measure and baseline NIHSS showed no significant prediction for functional outcomes in patients with less severe strokes (NIHSS score ≤2).
The improved cases by MoCA were seven (9.7%), newly discovered cases four (5.6%), increased score 13 (18.1%), and the decline six (8.3%). While the improved cases by MMSE were four (5.6%), newly discovered cases two (2.8%), increased score 5 (7.0%), and the decline four (4.2%).
Using of MoCA show a sensitivity of 100%, specificity of 78.95%, positive predictive value of 55.56%, and negative predictive value of 100.0% with an accuracy of 83.33% and χ2(P) 31.579 (<0.001) (Tables 1 and 2). On the other hand, MMSE showed a sensitivity of 55.56%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 78.95% with an accuracy of 83.33% and χ2(P) 31.579 (<0.001) (Table 3).
Many factors, such as heterogenicity, relevance, prevalence, and uniqueness, are linked to cognitive dysfunction following a stroke. Because strokes can occur in any region of the brain, there is no common phenotype. It is difficult to prove a link between stroke and dementia, or to rule out Alzheimer’s disease as a cause of cognitive impairment. Determining a suitable medical diagnosis, conducting epidemiological and interventional investigations, and delivering the best care are all challenges when it comes to defining the term ‘vascular dementia.’ There is a theory that numerous vascular processes and many vascular illnesses can cause vascular-related cognitive impairment .
Patients with lower levels of education were more vulnerable to poststroke cognitive impairment than those with greater levels of education, owing to variations in functional cognitive reserve, as well as lifestyle and risk factor profiles that protect against cognitive decline. The traditional view holds that vascular dementia is caused by a critical volume of infarcted brain tissue, regardless of its topography .
In this study, patients with poststroke cognitive impairment were more likely to have hemispheric dominance involvement, with lesions in the left hemisphere accounting for 81.5% of cognitively impaired patients. The integrity of hemisphere dominance and language processing are essential for cognitive efficiency and intelligence. In general, poststroke cognitive impairment is inversely proportional to motor and functional disability; however, other studies have found no such relationship .
Despite the fact that several researches have been conducted to identify vascular risk factors for poststroke cognitive impairment, the results have been inconsistent and disputed. Hypertension and diabetes mellitus were shown to be more common in patients with cognitive impairment in this study, with 70.4% of cognitively impaired patients being hypertensive and 66.7% being diabetic, respectively, with P values of 0.007 and 0.028. In addition, serum triglycerides and low-density lipoprotein have significant associations with (P=0.001, 0.011), respectively, and there is a substantial inverse connection with increased blood high-density lipoprotein levels, P value of 0.001. Factors including smoking, heart disease, and carotid stenosis have no effect [15,16].
Hypertension is a major risk factor for cerebrovascular illnesses, such as stroke, and it also plays a part in the development of vascular cognitive impairment and dementia. According to several studies, blood pressure should be managed within a specified range (below 140/90 mmHg) to prevent cardiovascular and cerebrovascular damage, as well as ensuring adequate cerebral perfusion to preserve cognitive function and prevent dementia. Other research suggests that hypertension in young age is linked to cognitive impairment in old age . Cardiac disease was common in patients with poststroke cognitive impairment, although it was not significantly linked to the condition. Atrial fibrillation (AF) and ischemic heart disease (IHD) cause thrombosis as well as a reduction in cardiac output. Faster ventricular rates cause a higher drop in cardiac output, which can lead to cerebral hypoperfusion. A second route of brain injury and cognitive decline could be a failure to maintain enough cerebral perfusion . Carotid stenosis was more common in post stenotic dilatation (PSD) patients, but the severity of the stenosis was not linked to cognitive loss. The intima-media thickness of the common carotid artery has been linked to an increased risk of cognitive impairment in elderly women, particularly impaired memory and cognitive speed, according to some studies .
The assessment of cognition in stroke patients is critical, and the use of a quick cognitive test can help with this assessment even in the early stages. In this regard, it is worth noting that the data does not support MoCA’s unambiguous advantages over other techniques. MoCA, on the other hand, has several advantages, such as brevity, ease of use, availability in multiple languages, and free access (Table 4) .
MoCA detected 100% of MCI individuals using a cutoff score of 26, whereas the MMSE had a sensitivity of 55.56%. For both MoCA and MMSE, specificity was 78.95 and 100%, respectively. When comparing the results of the MMSE and the MoCA in the same patients, a significant trend emerged (Fig. 1). The majority of patients scored in the normal range, while the MMSE classified 16.7% of MCI patients who scored in the abnormal range on the MoCA as normal. Even after intensive screening, patients with an MoCA score of above 26 would be exceedingly unlikely to match clinical and neuropsychological criteria for MCI in clinical practice. Using the MoCA as a screening tool should, in general, give timely guidance for referral and further investigation of MCI.
The MoCA is highly acceptable by MCI patients, who regard the MMSE’s cognitive activities to be insultingly basic. This method expedites the evaluation of patients with cognitive problems. There are no rapid screening tools for assessing various cognitive impairment levels. The MoCA is helpful for early cognitive impairment stages (such as MCI and mild Alzheimer disease (AD)), while the MMSE is better for more advanced cognitive impairment stages (AD patients with more significant functional impairment). Other screening techniques to swiftly and accurately distinguish MCI from healthy controls are currently unavailable .
It is important to acknowledge the study’s shortcomings. First, because the majority of the patients had mild to moderate strokes with minor disability, our findings may not be applicable to all stroke groups. Second, the sample size for individuals who have been diagnosed with impaired cognitive state is insufficient to investigate the drop in MoCA and MMSE scores between mild and moderate Vascular cognitive impairment (VCI). Third, as in many research, the follow-up period of 3 and 6 months is rather brief; the loss in cognitive state is reported after 6 months. Fourth, because this information was not collected, we did not conduct a systematic review of rehabilitation services.
A better assessment tool should be present for the cognitive effects of stroke and the relation between dementia and stroke with less cognitive burden owing to improved treatments. Efforts should be made to bring together investigators to improve assessment, treatment, and prevention.
MoCA is feasible in all phases of stroke and has some advantages such as ease of use, brevity, free access, and available in different languages. Also, it has a good predictive value for the development of cognitive impairment in the follow-up.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
This study is funded by the Neurology Department, Al-Azhar University Hospitals, Cairo, Egypt. The authors thank all participants for their involvement and the research team from the Neurology Department for data collection.
1. Paramasivam S. Current trends in the management of acute ischemic stroke Neurol India. 2015;63:665–672
2. Schaapsmeerders P, Maaijwee NA, van Dijk EJ, Rutten-Jacobs LC, Arntz RM, Schoonderwaldt HC, et al Long-term cognitive impairment after first-ever ischemic stroke in young adults Stroke. 2013;44:1621–1628
3. Sun JH, Tan L, Yu JT. Post-stroke cognitive impairment: epidemiology, mechanisms and management Ann Transl Med. 2014;2:80
4. Surawan J, Areemit S, Tiamkao S, Sirithanawuthichai T, Saensak S. Risk factors associated with post-stroke dementia: a systematic review and meta-analysis Neurol Int. 2017;9:7216
5. Zulkifly F, XXXX GS, Normah CD, Singh DKA, Subramaniam P. A review of risk factors for cognitive impairment in stroke survivors Sci World J. 2016;2016:1–16
6. van der Flier WM, Scheltens P. Epidemiology and risk factors of dementia J Neurol Neurosurg Psychiatry. 2005;76(Suppl 5)(Suppl 5):v2–v7
7. Leys D, Hénon H, Mackowiak-Cordoliani MA, Pasquier F. Poststroke dementia Lancet Neurol. 2005;4:752–759
8. 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;53:695–699
9. Kabátová O, Putekova S, Martinkova J. Analysis of psychometric features of the Mini-Mental State Examination and the Montreal Cognitive Assessment methods Clin Soc Work J. 2016;7:62–69
10. Tan HH, Xu J, Teoh HL, Chan BP, Seet RC, Venketasubramanian N, et al Decline in changing Montreal Cognitive Assessment (MoCA) scores is associated with post-stroke cognitive decline determined by a formal neuropsychological evaluation PLoS ONE. 2017;12:e0173291
11. Roth EJKreutzer JS, DeLuca J, Caplan B. NIH stroke scale Encyclopedia of clinical neuropsychology. 2011 New York, NY Springer:1113–1115
12. Adams HP Jr, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, et al Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment Stroke. 1993;24:35–41
13. Haring HP. Cognitive impairment after stroke Curr Opin Neurol. 2002;15:79–84
14. Khedr EM, Hamed SA, El-Shereef HK, Shawky OA, Mohamed KA, Awad EM, et al Cognitive impairment after cerebrovascular stroke: relationship to vascular risk factors Neuropsychiatr Dis Treat. 2009;5:103–116
15. Ikeda A, Yamagishi K, Tanigawa T, Cui R, Yao M, Noda H, et al Cigarette smoking and risk of disabling dementia in a Japanese rural community: a nested case-control study Cerebrovasc Dis. 2008;25:324–331
16. Siennicki-Lantz A, Reinprecht F, Wollmer P, Elmståhl S. Smoking-related changes in cerebral perfusion in a population of elderly men Neuroepidemiology. 2008;30:84–92
17. Harrington F, Saxby BK, McKeith IG, Wesnes K, Ford GA. Cognitive performance in hypertensive and normotensive older subjects Hypertension. 2000;36:1079–1082
18. Ott A, Breteler MM, de Bruyne MC, van Harskamp F, Grobbee DE, Hofman A. Atrial fibrillation and dementia in a population-based study. The Rotterdam Study Stroke. 1997;28:316–321
19. Komulainen P, Kivipelto M, Lakka TA, Hassinen M, Helkala EL, Patja K, et al Carotid intima-media thickness and cognitive function in elderly women: a population-based study Neuroepidemiology. 2007;28:207–213
20. Chiti G, Pantoni L. Use of Montreal Cognitive Assessment in patients with stroke Stroke. 2014;45:3135–3140