Abstract
Background
Stroke is a major cause of disability and mortality, often leading to both motor and cognitive impairments. Cognitive impairment is common among stroke survivors, with ischaemic strokes constituting the majority. While physical rehabilitation is emphasised, cognitive assessment remains underutilised in acute care.
Purpose
This study aimed to assess cognitive impairment in patients with ischaemic stroke affecting the anterior circulation using the Montreal Cognitive Assessment (MoCA) scale, and to correlate clinical variables with cognitive outcomes.
Methods
This observational study was conducted over 18 months at a tertiary healthcare centre in coastal Karnataka, India. Patients with first-ever ischaemic stroke involving the middle cerebral artery (MCA) and anterior cerebral artery (ACA) territories were included. Cognitive impairment was assessed using the MoCA scale within 1 week of stroke onset and again at 90 days. Clinical data such as NIHSS and mRS scores, as well as stroke subtype (via TOAST classification), were also collected.
Results
A total of 96 patients were included, with a mean age of 62.5 ± 10.3 years. At baseline, 55% of patients showed cognitive impairment, which improved at the 90-day follow-up (45% impaired). The most frequently affected domains were Attention (55%), Language (45%) and Executive function (50%). Factors such as older age, lower education level and higher NIHSS scores were associated with worse cognitive outcomes. Stroke aetiology (large-artery atherosclerosis and cardioembolism) was linked to more severe cognitive deficits. Aphasia was observed in 29 patients, and follow-up MoCA testing showed minimal cognitive improvement in this group.
Conclusion
Vascular cognitive impairment is a significant concern in ischaemic stroke patients. The MoCA scale is an effective tool for the early identification of cognitive impairment, even within the first week post-stroke. Age, education, NIHSS and mRS scores are critical predictors of post-stroke cognitive decline. Further studies are needed to refine cognitive assessment tools, especially for aphasic patients.
Introduction
According to the World Health Organization (WHO), stroke is a clinical syndrome characterised by the rapid onset of focal or global cerebral dysfunction lasting more than 24 h or resulting in death, with no apparent cause other than a vascular origin. 1 It is a major global contributor to disability and mortality, commonly described as a neurocognitive impairment caused by acute focal damage to the central nervous system due to vascular events such as cerebral infarction, intracerebral haemorrhage or subarachnoid haemorrhage. Ischaemic stroke accounts for 75%–80% of all strokes, followed by haemorrhagic stroke, which constitutes 15% of cases. 2
Stroke in younger individuals imposes a greater economic burden compared to the elderly, as it affects them during their most productive years. Over 50% of stroke survivors experience post-stroke cognitive impairment, with approximately two-thirds suffering from mild cognitive impairment. 3 While physical rehabilitation remains the primary focus of stroke recovery, cognitive assessment is often neglected in acute care. Cognitive evaluations, such as the Folstein Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), are underutilised despite their importance in identifying cognitive deficits following a stroke. 4
Rehabilitation targeting post-stroke cognitive impairment has received limited attention, despite the profound physical and psychological consequences it poses for patients. Few global and Indian studies have assessed cognitive outcomes after a stroke, with most evaluations occurring at 3 and 6 months post-stroke. This study aims to assess cognitive impairment in patients with middle cerebral artery (MCA) and anterior cerebral artery (ACA) territories ischaemic stroke using the MoCA assessment scale, and to correlate the clinical subtype of stroke with cognitive impairment at a tertiary health care centre in South India.
Methods
Study Design
This observational study was conducted over 18 months, from 11 February 2020, to 10 August 2021, at a tertiary healthcare centre in coastal Karnataka, India. Patients with acute ischaemic stroke admitted to Kasturba Hospital were screened based on inclusion and exclusion criteria. Eligible patients were enrolled after obtaining informed consent, and the study was approved by the Institutional Ethics Committee (IEC 90/2020) and registered with CTRI (CTRI/2020/06/025502).
Inclusion Criteria
Patients were included in the study if they were 18 years or older, of either gender and experiencing their first-ever ischaemic stroke. Eligibility was further confirmed by CT/MRI findings demonstrating a recent ischaemic stroke involving the anterior circulation, specifically the MCA, ACA or both, as these territories are predominantly involved in cognitive functions.
Exclusion Criteria
Exclusion criteria comprised patients under 18 years of age, those with a history of recurrent stroke (clinically or radiologically confirmed), and patients with strokes resulting in retinal infarction, hearing or comprehension disorders or death within 3 days of onset. Other excluded conditions included non-ischaemic strokes (e.g., haemorrhagic or venous infarcts), posterior circulation strokes (e.g., basilar, vertebral or posterior cerebral artery territories), brain tumours, demyelination, severe psychiatric illness, pre-existing cognitive impairment (e.g., dementia) and transient ischaemic attacks.
Data Collection
Demographic details, including age, sex, education and occupation, were collected. Education and occupation were categorised using the Kuppuswamy scale. Clinical data encompassed the stroke onset date, duration of hospitalisation, history of addiction, comorbidities, radiological findings (territory involvement and TOAST classification) and laboratory investigations such as haemoglobin (Hb), platelet counts, HbA1c and lipid profiles.
Assessments
Initial assessments were conducted within 7 days of stroke onset using the National Institutes of Health Stroke Scale (NIHSS) and Modified Rankin scale (MRS). Cognitive evaluation was performed using the MoCA scale. Patients with aphasia who were unable to complete the MoCA assessment were analysed in a separate group (Aphasic and Non-Aphasic groups). Follow-up evaluations were conducted at 90 days, including mRS scoring, MoCA reassessment and clinical evaluation.
Statistical Analysis
Data were entered into Microsoft Excel and analysed using SPSS software (version 26.0). Continuous variables were presented as medians with interquartile ranges for non-normally distributed data and as mean ± standard deviation for normally distributed data. Categorical variables were expressed as proportions, and age was categorised for analysis. Bivariate and multivariate analyses were performed using Pearson’s chi-square or Fisher’s exact tests to identify factors associated with outcomes. Multivariable logistic regression was used to assess the independent effects of variables, with statistical significance set at p < .05.
Results
Demographic and Clinical Baseline Information of Patients
The study included 96 patients, with a mean age of 62.5 ± 10.3 years. The cohort was predominantly male (55%) and had a higher proportion with educational levels of ≤10 years (68%). Common comorbidities included hypertension (50%), hyperlipidaemia (35%) and diabetes mellitus (25%). Smoking was reported in 40% of the patients. This baseline data highlights the risk factors and clinical features of the study population (Table 1). At the time of admission, the mean systolic blood pressure was 145 ± 18 mmHg, and the diastolic pressure was 90 ± 10 mmHg, suggesting a significant number of patients with elevated blood pressure. The heart rate averaged 82 ± 10 beats per minute (Table 2).
Demographic Information.
Clinical Baseline Information.
Stroke Aetiology and NIHSS Scores
Stroke aetiology was categorised using the TOAST classification, with the majority of patients experiencing large-artery atherosclerosis (35%) and cardioembolism (30%). The mean NIHSS score at presentation was 9.5 ± 4.2 for large-artery atherosclerosis, 10.2 ± 5.3 for cardioembolism and 8.3 ± 3.6 for small-vessel disease. A higher proportion of patients with cardioembolism (23%) and large-artery atherosclerosis (20%) presented with severe strokes (NIHSS ≥16) (Table 3). This demonstrates that stroke severity varies by aetiology, with certain stroke subtypes correlating with more severe neurological deficits.
Stroke Aetiology and NIHSS Scores.
MoCA Assessment Results, Subdomain Analysis and Associated Factors
The MoCA was conducted within 1 week of stroke onset and again at a 90-day follow-up. At baseline, 45% of patients had normal cognitive function, while 55% showed impaired scores. However, at the 90-day follow-up, the number of patients with normal cognitive function increased to 55%, while those with impairments decreased to 45%. This indicates a recovery trend in cognitive function following the stroke (Figure 1). Subdomain analysis of the MoCA (at the 90-day follow-up) revealed that the most frequent impairments occurred in the Attention (55%) and Language (45%) domains. Executive function and delayed recall were also affected, with 50% and 40% of patients, respectively, showing abnormal results. The least affected domain was orientation, with 35% showing impairments. This detailed breakdown helps in understanding the specific cognitive areas impacted by stroke (Figure 2).
Regression analysis identified several factors significantly associated with MoCA scores. Older age (β = −0.25, p = .04) and lower education level (β = 0.35, p = .02) were positively correlated with cognitive decline. Additionally, a higher NIHSS score at admission (β = −0.55, p < .001) was associated with worse cognitive outcomes. Smoking history also had a slight negative impact on MoCA scores (β = −0.15, p = .09) (Table 4). These findings suggest that both demographic and clinical variables influence cognitive recovery post-stroke.
Regression Analysis for MoCA Score and Associated Factors.
Radiological analysis of the patients revealed that the left MCA territory was the most commonly affected region, observed in 52% (50 out of 96) of the cases. This was followed by infarcts in the right MCA territory, which accounted for 31.25% (30 out of 96) of the patients (Table 5). Bilateral MCA territory involvement was seen in five patients, highlighting a smaller but significant subset with extensive ischaemia. In terms of hemisphere involvement, left cerebral hemisphere infarcts were more prevalent, identified in 57 patients compared to 34 patients with infarcts in the right cerebral hemisphere. Among the ACA infarcts, isolated involvement was relatively rare, with two cases on the right side and three on the left, while combined ACA and MCA infarcts were observed in two right-sided and approximately four left-sided cases (Figure 3).
Radiology Findings.
Discussion
In the present study, a total of 96 patients were recruited for cognitive assessment, encompassing all age groups, genders, educational levels and occupations. The study focused on patients with anterior circulation ischaemic strokes, with cognitive assessments conducted at 7 days and 90 days post-stroke. In contrast, studies by Chaurasia et al. and Jacquin et al. included both ischaemic and haemorrhagic stroke patients, with assessments conducted at 3 and 6 months.5, 6
The mean age of participants in our study was 59.22 years (±12.428), which is comparable to the mean ages of 64 years in the study by Chaurasia et al. and 66 years in the study by Jacquin et al. respectively.5, 6 Age was found to be associated with cognitive impairment post-stroke, which aligns with findings from a meta-analysis by Khaw et al., where older age was linked to poorer cognitive outcomes. 7 Regarding gender distribution, our study found that 70% of patients were male and 30% were female, consistent with the findings of Tham et al. in Singapore. 8
Educational status in our cohort revealed that approximately 70% of patients had 1–12 years of formal education, while 11% had more than 12 years of education. In contrast, the study by Jacquin et al. showed that 85% of patients had 1–12 years of education. 6 We observed a significant inverse relationship between educational status and post-stroke cognitive impairment, which was statistically significant. This finding is in agreement with the results of studies by Chaurasia et al. and Khaw et al. 7 Thus, education level appears to be a crucial predictor of cognitive outcomes following a stroke. However, occupation did not show a significant association with cognitive impairment in our study.
Hypertension (63.54%) and diabetes mellitus (39.53%) were the most common comorbidities observed in our study, followed by rheumatic heart disease, atrial fibrillation and ischaemic heart disease. In comparison, studies by Chaurasia et al. and Jacquin et al. reported lower frequencies of hypertension and diabetes as comorbidities, with Jacquin et al. also including sleep apnoea and transient ischaemic attack as risk factors, which were not assessed in our study.5, 6
Stroke aetiology, classified using the TOAST criteria, revealed that large vessel disease and cardioembolic aetiology contributed to 66% of the cases in our study. This contrasts with the study by Aam et al., where these aetiologies contributed to only 52% of the study population. 9 Regarding stroke severity, our study found that most patients had mild (0–5) or moderate (6–15) NIHSS scores, with 45% and 36% of patients in these categories, respectively. Only 18% had an NIHSS score of 16 or higher. The median NIHSS score in our study was 6, while Jacquin et al. reported a median score of 3, and other studies focused exclusively on patients with mild NIHSS scores. 7
Regarding the stroke territory, 59% of patients in our study had left MCA territory involvement, while 36% had right MCA territory involvement, with the remaining cases being bilateral MCA strokes. For cognitive assessment, we used the MoCA scale, where a score of <26 out of 30 was considered indicative of impaired cognition. At 1 week post-stroke, 85% of non-aphasic patients scored <26. Jacquin et al. reported that 56% of patients had impaired MoCA scores in the acute phase of stroke, 6 and Tham et al. found cognitive impairment in 40% of patients at baseline using the MMSE scale. 8 The discrepancy in findings could be attributed to differences in NIHSS scores and educational backgrounds of the patients in these studies. Previous studies have suggested that the MMSE has lower sensitivity compared to the MoCA, which could explain the lower rates of cognitive impairment seen with MMSE tools compared to the MoCA scale. In a study by Demeyere et al. 76% of patients showed cognitive impairment on MoCA testing. 10
Subdomain analysis of the MoCA in our study revealed significant impairment in all subdomains among patients with impaired MoCA scores, particularly in Executive Function, Language and Delayed Recall, which were affected in over 80% of patients. These subdomains were not analysed in other major studies. Additionally, 29 patients in our study were aphasic at the initial assessment and could not complete the MoCA within 1 week of stroke onset. Of these, 13 patients underwent MoCA testing at 90 days, all scoring <5, with improvements noted in the orientation subdomain. The remaining 16 patients did not show improvement and remained aphasic by the 3-month follow-up, and thus could not complete the MoCA assessment. Aphasic patients were generally excluded from previous studies, even though aphasia is an important factor causing post-stroke disability. Hence, these patients were included in our study analysis.
Follow-up MoCA testing for non-aphasic patients showed no significant improvement in scores at 90 days compared to the 1-week assessment, with only five patients reaching a MoCA score of 26 or above. This finding was not statistically significant (p = .267). Thus, we suggest that MoCA testing can serve as an early predictor of post-stroke cognitive impairment, as there was no significant difference between the 1-week and 3-month assessments.
In our study, factors such as NIHSS score, duration of hospital stay, mRS score at admission and age all showed an inverse correlation with MoCA scores. Higher age, higher NIHSS scores and worse mRS scores at admission were associated with lower MoCA scores at both the initial and follow-up assessments. Occupation, type of stroke and gender did not show significant associations with MoCA scores, which is consistent with the findings of Chaurasia et al. 5
Conclusion
Our study has certain limitations, including the small number of cases, primarily due to the COVID-19 pandemic lockdown and recruitment restrictions imposed by the ethical committee. Additionally, aphasia, a significant cognitive impairment, cannot be adequately assessed by the MoCA scale, suggesting the need for a more appropriate tool for this population.
In conclusion, vascular cognitive impairment is a common neurological comorbidity associated with motor deficits in stroke patients. The MoCA scale is a simple, easily accessible tool that can be used as early as 1 week post-stroke to predict cognitive impairment in ischaemic stroke patients. Aphasia contributes to approximately one-third of cognitive deficits, with executive function, language and delayed recall being the most frequently impaired subdomains in patients with post-stroke cognitive impairment. Age, educational status, mRS score at admission and NIHSS score are important predictors of post-stroke cognitive impairment. These findings underscore the value of early cognitive assessment and highlight the need for further studies to explore more comprehensive tools for assessing aphasia.
Footnotes
Acknowledgements
We would like to express our gratitude to all the authors, co-authors and supportive personnel for their invaluable assistance with this project.
Authors’ Contribution
Sumedh S. Agrawal was responsible for collecting the data and performing the analysis.
Nikith Ampar provided study materials, performed the analysis and wrote the article.
Arvind N. Prabhu conceived and designed the analysis, and provided critical review, commentary and revision.
Aparna R. Pai provided oversight and leadership responsibility, as well as critical review, commentary and revision.
All authors have reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work, ensuring its accuracy and integrity.
Data Availability Statement
Original data generated in this study are included.
Statement of Ethics
Ethics committee approval from the Institutional Ethics Committee (UREC) was obtained before the start of the study.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding
The authors received no financial support for the research, authorship and/or publication of this article.
ICMJE Statement
Both authors made substantial contributions to the conception, design and execution of the study (CTRI Ref. No: REF/2025/01/098554).
Informed Consent
Informed consent was obtained from all the subjects in the study.
