Incidence and predictors of post-stroke cognitive impairment among patients admitted with first stroke at tertiary hospitals in Dodoma, Tanzania: A prospective cohort study

Abstract

Introduction
Stroke survivors develop cognitive impairment, which significantly impacts their quality of life, their families, and the community as a whole but not given attention. This study aims to determine the incidence and predictors of post-stroke cognitive impairment (PSCI) among adult stroke patients admitted to a tertiary hospital in Dodoma, Tanzania.

Methodology
A prospective cohort study was conducted at tertiary hospitals in the Dodoma region, central Tanzania. A sample size of 158 participants with the first stroke confirmed by CT/MRI brain aged ≥ 18 years met the criteria. At baseline, social-demographic, cardiovascular risks and stroke characteristics were acquired, and then at 30 days, participants were evaluated for cognitive functioning using Montreal Cognitive Assessment (MoCA). Key confounders for cognitive impairment, such as depression and apathy, were evaluated using the Personal Health Questionnaire (PHQ-9) and Apathy Evaluation Scale (AES), respectively. Descriptive statistics were used to summarise data; continuous data were reported as Mean (SD) or Median (IQR), and categorical data were summarised using proportions and frequencies. Univariate and multivariable logistic regression analysis was used to determine predictors of PSCI.

Results
The median age of the 158 participants was 58.7 years; 57.6% of them were female, and 80.4% of them met the required criteria for post-stroke cognitive impairment. After multivariable logistic regression, left hemisphere stroke (AOR: 5.798, CI: 1.030–32.623, p = 0.046), a unit cm³ increase in infarct volume (AOR: 1.064, 95% CI: 1.018–1.113, p = 0.007), and apathy symptoms (AOR: 12.259, CI: 1.112–89.173, p = 0.041) had a significant association with PSCI.

Conclusion
The study revealed a significant prevalence of PSCI; early intervention targeting stroke survivors at risk may improve their outcomes. Future research in the field will serve to dictate policies and initiatives.

Full text

RESEARCH ARTICLE
Incidence and predictors of post-stroke
cognitive impairment among patients
admitted with first stroke at tertiary hospitals
in Dodoma, Tanzania: A prospective cohort
study
Baraka AlphonceID
1,2
, John Meda
1,3
, Azan NyundoID
2,4
*
1Department of Internal Medicine, School of Medicine & Dentistry, The University Dodoma, Dodoma,
Tanzania, 2Department of Internal Medicine, The Benjamin Mkapa Hospital, Dodoma, Tanzania,
3Department of Cardiology, The Benjamin Mkapa Hospital, Dodoma, Tanzania, 4Department of Psychiatry
and Mental Health, School of Medicine, The University Dodoma, Dodoma, Tanzania
*azannaj@gmail.com,azan.nyundo@udom.ac.tz
Abstract
Introduction
Stroke survivors develop cognitive impairment, which significantly impacts their quality of
life, their families, and the community as a whole but not given attention. This study aims to
determine the incidence and predictors of post-stroke cognitive impairment (PSCI) among
adult stroke patients admitted to a tertiary hospital in Dodoma, Tanzania.
Methodology
A prospective cohort study was conducted at tertiary hospitals in the Dodoma region, central
Tanzania. A sample size of 158 participants with the first stroke confirmed by CT/MRI brain
aged 18 years met the criteria. At baseline, social-demographic, cardiovascular risks and
stroke characteristics were acquired, and then at 30 days, participants were evaluated for
cognitive functioning using Montreal Cognitive Assessment (MoCA). Key confounders for
cognitive impairment, such as depression and apathy, were evaluated using the Personal
Health Questionnaire (PHQ-9) and Apathy Evaluation Scale (AES), respectively. Descrip-
tive statistics were used to summarise data; continuous data were reported as Mean (SD) or
Median (IQR), and categorical data were summarised using proportions and frequencies.
Univariate and multivariable logistic regression analysis was used to determine predictors of
PSCI.
Results
The median age of the 158 participants was 58.7 years; 57.6% of them were female, and
80.4% of them met the required criteria for post-stroke cognitive impairment. After multivari-
able logistic regression, left hemisphere stroke (AOR: 5.798, CI: 1.030–32.623, p= 0.046),
a unit cm
3
increase in infarct volume (AOR: 1.064, 95% CI: 1.018–1.113, p= 0.007), and
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OPEN ACCESS
Citation: Alphonce B, Meda J, Nyundo A (2024)
Incidence and predictors of post-stroke cognitive
impairment among patients admitted with first
stroke at tertiary hospitals in Dodoma, Tanzania: A
prospective cohort study. PLoS ONE 19(4):
e0287952. https://doi.org/10.1371/journal.
pone.0287952
Editor: Kamal Sharma, UN Mehta Institute of
Cardiology and Research Center, INDIA
Received: June 18, 2023
Accepted: February 1, 2024
Published: April 10, 2024
Copyright: ©2024 Alphonce et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files.
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist

apathy symptoms (AOR: 12.259, CI: 1.112–89.173, p= 0.041) had a significant association
with PSCI.
Conclusion
The study revealed a significant prevalence of PSCI; early intervention targeting stroke sur-
vivors at risk may improve their outcomes. Future research in the field will serve to dictate
policies and initiatives.
Introduction
Stroke is the leading cause of death and disability, affecting around 67 million people globally
each year, with roughly 5,700,000 dying and 5,000,000 rendered incapacitated [1,2]. Stroke
survivors endure cognitive impairment, which has a substantial impact on the quality of life of
the sufferer, the family, and the community as a whole. PSCI is associated with reduced quality
of life, increased likelihood of depressive symptoms, high level of dependence, increased health
care cost, lost wages, and social isolation [3–6].
Globally, PSCI prevalence ranges from 35 to 92% [7–9]. In the few studies undertaken in
Sub-Saharan Africa, 40% and 34% of Nigerian and Ghanaian stroke survivors, respectively,
were diagnosed with PSCI at three and two years [10,11]. The disparity in prevalence may be
rooted in variances in the diagnostic tools used to evaluate PSCI across studies, the timing of
cognitive impairment screening following a stroke, ethnicity, and cultural backgrounds [12].
Ageing, female gender, fewer years of formal education, hypertension, diabetes, dyslipidae-
mia, atrial fibrillation, current alcohol and tobacco use, type of stroke, structures involved in
stroke, stroke laterality, the size of the infarct or hematoma, and neuropsychiatric manifesta-
tions at baseline have all been linked in previous studies as independent risk factors for PSCI
at a different stage of stroke [13–17]. The study aimed to assess the incidence and predictors of
PSCI in early phase following a first episode of stroke among patients admitted at tertiary hos-
pitals in Dodoma, Tanzania.
Material and methods
Study design and setting
This prospective cohort study was carried out at Dodoma Referral Regional Hospital and Ben-
jamin Mkapa Hospital, both of which serve 20–30 stroke patients per month. Both are recog-
nised teaching hospitals for the University of Dodoma for medical training at the
undergraduate and residency levels. With its well-built and state-of-the-art infrastructure, the
Benjamin Mkapa Hospital is equipped with neuroimaging services, such as Computed
Tomography scans and Magnetic Resonance Imaging.
Sample size and sampling procedure
The sample size was determined using a method for proportion in a prospective cohort study
[18]. The estimated sample size was 130, at the very least. However, with a 30% attrition rate in
our setting, 170 participants were required to meet the expected sample size. From June 2021
to March 2022, 158 participants who were willing to participate and met the inclusion criteria
were recruited for the nine-month study [19].
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Inclusion criteria/exclusion criteria
Patients who were 18 years of age or older, who provided informed consent or proxy consent
from a close relative if the patient is incapable, presented with their first stroke within 14 days,
and whose stroke was verified by a CT scan or MRI of the brain, were included in the study.
Patients with severe motor impairment on their dominant side and those with intracerebral
haemorrhage from a tumour or trauma were excluded, as were those with severe sensory
impairment (blindness and deafness), Transient Ischemic Attack, subarachnoid haemorrhage,
and prior neurological conditions including epilepsy.
Outcome variable
Post-stroke cognitive impairment was defined as a MoCA score of less than 23 out of 30
assessed at 30-days post admission. Compared to the widely used 26/30 cut-off, a 23/30 cut-off
provides greater diagnostic accuracy [20]. A group with lesser levels of education has proven
to benefit from the MoCA tool. The tool examines eight major cognitive domains: visuospa-
tial-executive (trail making B task, 3-D cube copy and clock drawing); naming (unfamiliar ani-
mals); language (sentence repetition and phonemic fluency task); short-term memory
(delayed recall of words); abstraction (verbal abstraction); attention and calculation (digits for-
ward and backwards, target detection using tapping, serial 7s subtraction) and orientation
(time place and people) [21].
Independent variables
Through a questionnaire that was structured based on existing evidence, variables such as age,
gender, level of education, history of current /less than one year of alcohol use, cigarette smok-
ing, and diabetes were acquired [22]. Other confounding clinical variables, such as post-stroke
depression and apathy, were also assessed using the Patient Health Questionnaire (PHQ) and
Apathy Evaluation Scale (AES), respectively.
Blood pressure (BP) readings were recorded according to the 2018 AHA/ACC Hyperten-
sion guideline for standard measurement of BP [23]. Hypertension was defined as BP 140/90
mmHg or a patient on antihypertensive medications [24]. Radial pulse and heart rate were
also recorded; a deficit of ten or more was considered to indicate atrial fibrillation [25].
A blood sample was analysed for Lipid profiles; according to the National Cholesterol Educa-
tion Program (NCEP), dyslipidaemia will be defined as HDL-Cholesterol <40 mg/dl or Total
Cholesterol 200 mg/dl, or LDL-Cholesterol 130 mg/dl or triglyceride levels 130mg/dl [26].
Hyperglycaemia was defined according to the American Diabetes Association as random blood
sugar >11.1 mmol/L, fasting blood sugar >7.0 mmol/L or glycated haemoglobin6.5% [27].
A 12-lead ECG was done on each participant under the supervision of a consultant cardiol-
ogist. Atrial fibrillation was diagnosed as the absence of P waves and irregular-irregular RR
interval [28]. Further screening for atrial fibrillation using a 24-hour ECG Holter was done in
a patient with ischemic stroke whose 12-lead ECG tracing was normal [29].
All patients had brain imaging with either a Computed Tomography scan (SIEMENS-SO-
MATOM Definition Flash) or Magnetic Resonance Imaging (MAGNETUM SPECTRA A TIM
+Dot System 3T). Strokes were characterised according to type, hemisphere affected, cortical or
subcortical, and volume of infarct/hematoma, measured using the ellipsoid method [30,31].
The Patient Health Questionnaire (PHQ)– 9, with a total score of 27, was used to screen
stroke survivors for post-stroke depression; the score was classified as minimal depression
(1–4), mild depression (5–9), moderate depression (10–14), moderately severe depression
(15–19), and severe depression (20–27). Apathy was evaluated using the apathy evaluation
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scale; a score >38 was suggestive of apathy. A cut-off>38 has sensitivity of 80% and speci-
ficity of 100% [32,33].
Data analysis
For statistical analysis, data were entered on a Microsoft Excel sheet and then converted to
IBM SPSS PC version 26. Continuous variables were reported as mean and standard deviation
(SD) or Median and interquartile ranges; frequencies and percentages were used for categori-
cal variables. Chi square and Mann-Whitney U test were used to determine the difference in
Social-Demographic, cardiovascular risk factors, stroke characteristics, and neuropsychiatric
manifestations, which are depression and apathy by post-stroke cognitive outcomes. The pre-
dictors were evaluated by binary logistic regression, and only variables that met at least a 20%
(p-value0.2) statistical significance [34] were selected for multivariable Logistic regression
analysis to determine independent predictors for post-stroke cognitive impairment. The
adjusted odds ratio (aOR) and the 95% confidence interval (CI) were determined. Statistical
significance was determined by a two-sided p 0.05.
Ethical issues
After receiving ethical approval from the Directorate of Research and Publications (reference
number MA.84/261/02), the Vice Chancellor’s office at the University of Dodoma granted
authorisation for the study to be carried out. Later, the administrative divisions of Benjamin
Mkapa and Dodoma Regional Referral Hospitals gave their respective approvals for data col-
lection under the references AB.150/293/01/196 and EB.21/267/01/123. It was made clear to
participants that their participation was completely optional and that they might withdraw at
any time. Participants’ identities were changed to identification numbers in order to maintain
privacy and confidentiality; however, their choice to participate had no bearing on the stan-
dard of care they received. Depressive symptoms in stroke survivors led to a referral to a psy-
chiatrist for further evaluation and therapy.
Results
Out of 255 stroke patients were evaluated for eligibility (Fig 1), 158 participants met the criteria
and were evaluated for the Post-Stroke Cognitive Impairment at 30 days of follow-up, and 127
(80.4%) met the criteria for PSCI.
Social demographic characteristics
The mean age of the 158 study participants was 58.7±13.4 years, and 57.6% of them were
female. The majority (66.5%) were referred from a primary healthcare facility, 50% lived in
urban areas, and nearly half (49.4%) had completed seven or fewer years of formal education.
Only older age (p >0.001) and seven or fewer years of formal education (p 0.001) demon-
strated significant differences with post-stroke cognitive outcomes (Tables 1and 2).
Clinical characteristics of participants
Thirty-one participants (19.6%) had atrial fibrillation, 36 (22.6%) were diabetic, 106 (67.1%)
had dyslipidaemia, and 117 (94.1%) of the patients had hypertension. There was no significant
difference in post-stroke cognitive outcomes by other vascular risk factors; however, a higher
proportion (20.5%) of patients with a history of alcohol use were substantially overrepresented
among stroke survivors with post-stroke cognitive impairment (p = 0.022) (Tables 1and 2).
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The majority of strokes (69.3%) were ischemic, and the median infarct and hematoma vol-
umes were 40 and 20.7 IQR (87 and 28), respectively. Only the infarct volume, cortical strokes,
and left-sided strokes exhibited significantly greater proportions among those who had post-
stroke cognitive impairment (p 0.001, p = 0.003, and p 0.001, respectively) (Tables 1and 2).
The majority of individuals (80.4%) fit the criteria for mild to moderate depression, with a
median PHQ-9 score of 8, and IQR of (10), whereas apathy was found in 36.1% of participants,
with a median EAS score of 34, IQR (17). Only apathy was substantially overrepresented
among post-stroke cognitive impairment subjects (p 0.001) (Tables 1and 2).
Predictors of post-stroke cognitive impairment
Under unadjusted logistic regression, increasing age, less than eight years of formal education,
hypertension, a history of current alcohol use, increasing infarct volume, left-sided stroke, cor-
tical stroke, and apathy were all significantly associated with post-stroke cognitive impairment
(Table 3). However, under adjusted logistic regression, only increasing infarct volume (AOR:
1.064, 95% CI: 1.018–1.113, p= 0.007), left-sided stroke (AOR: 5.798, CI: 1.030–32.623,
p= 0.046), and apathy (AOR: 12.259, CI: 1.112–89.173, p= 0.041) remained significantly asso-
ciated with cognitive impairment at 5% (p0.05) level of significance while increasing age
(p= 0.072) had 10% level of significance (Table 2).
Discussion
The main objective of this study was to determine the predictors of early cognitive impairment
among patients with first-ever stroke admitted at tertiary hospitals in Dodoma. Moreover, we
also determined the prevalence of post-stroke cognitive impairment. We revealed a high prev-
alence of PSCI at 30 days (80.4%), which was independently associated with stroke laterality,
increasing infarct volume and apathy.
While the prevalence of PSCI varies around the globe, our findings allude to the high inci-
dence and prevalence of PSCI in the early stages after a stroke episode, observed in past studies
Fig 1. Algorithm for enrolment of study participants and 30 days post-stroke cognitive outcome.
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[34]. The PSCI rates generally range from 20–70% depending on the definition, phases of the
stroke, severity of the stroke at admission, population heterogeneity, and pre-morbid cognitive
functioning [7–9]. Similarly, high PSCI rates of 66.4–75.2% are reported when cognitive
Table 1. Demographic and clinical characteristics of patients with different cognitive outcomes (N = 158).
All (N = 158) No PSCI (N = 31) PSCI (N = 127)
Variables Frequency (%) Frequency (%) Frequency (%) P-value
Social Demographic characteristics
Age (Mean ±SD) 58.7 ±13.4 50.5 ±12.5 60 ±12.9
<50 37 (23 .4) 10 (32.3) 27 (21.3) 0.001
50–60 53 (33.5) 17 (54.8) 36 (28.3)
>60 68 (43.1) 4 (12.9) 64 (50.4)
Sex
Male 67 (42.4) 9 (29) 58 (45.7) 0.093
Female 91 (57.6) 22 (71) 69 (54.3)
Residence
Urban 79 (50) 11 (35.5) 68 (53.5) 0.071
Rural 79 (50) 20 (64.5) 59 (46.5)
Referral status
Self 53 (33.5) 13 (41.9) 40 (31.5) 0.270
Referred 105 (66.5) 18 (58.1) 87 (68.5)
Years of formal education
7 years 78 (49.4) 4 (12.9) 74 (58.3) <0.001
8 years 80 (50.6) 27 (87.1) 53 (41.7)
Vascular risk factors
Current Cigarette smoking 33 (20.9) 5 (16.1) 28 (22) 0.467
Current Alcohol intake 27 (17.1) 1 (3.2) 26 (20.5) 0.022
Hypertension 117 (94.1) 19 (61.3) 98 (77.2) 0.071
Diabetes 36 (22.8) 7 (22.6) 29 (22.8) 0.976
Atrial fibrillation 31 (19.6) 4 (12.9) 27 (21.3) 0.294
Dyslipidaemia 106 (67.1) 20 (64.5) 86 (67.7) 0.734
Stroke characteristics
Stroke type
Ischemic 109 (69.3) 21 (67.7) 88 (69.3) 0.867
Haemorrhagic 49(30.7) 10 (32.3) 39 (30.7)
Structures involved
Cortical 88 (55.7) 10 (32.3) 78 (61.4) 0.003
Subcortical 70 (44.3) 21 (67.7) 49 (38.6)
Stroke laterality
Left 97 (61.4) 10 (32.3) 87 (68.5) <0.001
Right/brain stem, cerebellum 61 (38.6) 21 (67.7) 40 (31.5)
Stroke vascular territory
Posterior 16 (10.1) 5 (16.1) 11 (8.7) 0.217
Anterior 142 (89.9) 26 (83.9) 116 (91.3)
Psychiatric factors
Apathy 57 (36.1) 3 (9.7) 54 (42.5) 0.001
Depression
Minimal-moderate 127 (80.4) 27 (87.1) 100 (78.7) 0.294
Severe 31 (19.6) 4 (12.9) 27 (21.3)
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assessment is done at a comparable time frame of two to eight weeks after the stroke [7,9,35].
However, a lower prevalence of 57 and 67% was observed in the acute phase among individuals
without pre-morbid cognitive impairment [36]. In general, using screening tools for evalua-
tion of cognitive functioning shows a higher prevalence of PSCI, as observed in this study; on
the contrary, when a comprehensive neuropsychological battery is used, prevalence as low as
34% and 39% were reported in Ghana and Nigeria, respectively [10,11]. Higher rates of PSCI
could further be explained by the significant proportion of our study participants having less
than seven years of formal education and residing in rural areas; these two factors are shown
to be independent predictors of poor performance on cognitive functioning in the previous
studies and also supported by our findings [10].
The association between post-stroke cognitive impairment and left hemisphere stroke
observed in this study is the replication of previous findings [34,37]. Since language is primar-
ily a left hemispheric cognitive domain for more than 90% of individuals globally [38], damage
to the left hemisphere due to stroke could significantly impact the language domain and over-
all cognitive performance [39].
The index study showed that every (cm
3
) unit increase in infarct volume predicted PSCI;
the link between a larger infarct volume and PSCI was initially described by Tomlison et al.,
who demonstrated that infarct volume closer to 100 cm3 considerably increased the likelihood
Table 2. Clinical characteristics of patients with different cognitive outcomes (N = 158).
All (N = 158) No PSCI (N = 31) PSCI (N = 127)
Variable Median (IQR) Median (IQR) Median (IQR) P-value
Stroke characteristics
NIHSS scale 12 (7) 12 (8) 12(7) 0.813
Infarct volume (cm
3
) 40 (87) 15 (25) 23 (35) <0.001
Hematoma volume (cm
3
) 20.7 (28) 15 (25) 23 (35) 0.248
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Table 3. Logistic regression analysis of predictors of cognitive Impairment at 1 month.
Unadjusted results Adjusted results
Variable OR (95% CI) P-value AOR (95% CI) P-value
Age 1.064 (1.028–1.101) <0.001 1.075 (0.993–1.163) 0.072
Male gender 2.055 (0.878–4.810) 0.097 0.773 (0.170–3.525) 0.740
<8 Years of formal education 9.425 (3.113–28.532) <0.001 2.802 (0.510–15.399) 0.236
Cigarette smoking 1.471 (0.517–4.182) 0.469
Alcohol use 4.636(0.593–36.260) 0.144 6.858 (0.470–72.067) 0.159
Hypertension 2.134 (0.928–4.910) 0.074 0.936 (0.162–5.395) 0.941
Diabetes 1.015 (0.397–2.593) 0.976
Dyslipidaemia 1.154 (0.506–2.631) 0.734
Atrial fibrillation 1.822 (0.587–5.658) 0.299
NIHSS 1.014 (0.932–1.102) 0.753
Stroke type, Ischemic 1.074 (0.463–2.494) 0.867
Infarct volume 1.048 (1.019–1.078) 0.001 1.064 (1.018–1.113) 0.007
Hematoma volume 1.026 (0.979–1.074) 0.288
Stroke laterality, Left 4.002 (1.764–9.081) 0.001 5.798 (1.030–32.623) 0.046
Structures involved, Cortical 3.343 (1.453–7.693) 0.005 1.057 (0.131–8.540) 0.959
Apathy 6.904 (1.995–23.895) 0.002 12.259 (1.112–89.173) 0.041
Depression 1.558 (0.492–4.931) 0.451
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of PSCI [40]. Kumral et al. demonstrated that infarct volume over 90 cm
3
independently pre-
dicted PSCI [41]. The correlation between the infarct volume and PSCI has been shown in ear-
lier studies; an infarct greater than 17 cm3 may be adequate to predict PSCI independently
[34]. However, in the multitude of methodological approaches to measuring infarct volumes
in the aforementioned studies, predicting PSCI based solely on infarct volume parameters
needs more evidence to improve the reliability [42].
Several neuropsychiatric phenomena, including apathy, may share a common pathway to
PSCI; based on the strong correlation, apathy may be considered an inherent sign of cognitive
impairment rather than a distinct neuropsychiatric condition [14,43]. The same underlying
brain lesion may drive apathy and cognitive impairment, specifically, the frontal lobes and
subcortical structures, where the corresponding lesions may lead to the loss of cognitive func-
tion that restricts a person’s ability to organise goal-directed behaviour [44].
Given the high risk and debilitating complications with profound disabilities among stroke
survivors, early stratification of those at risk for cognitive impairment is highly recommended
[45–47]. Identifying patients who could benefit from early cognitive assessment is crucial for
better outcomes through somatic and psychological interventions [48].
Limitation of the study
This prospective cohort study design had a high attrition rate due to loss to follow-up and
death; this needed extensive recruitment of patients to mitigate the effect. Since the pre-mor-
bid cognitive assessment was not assessed, we could not clearly understand the status of pre-
stroke cognitive functions; hence, its influence on PSCI remains speculative. Therefore, a sur-
vey such as an Informant Questionnaire for Cognitive Decline in the Elderly (IQCODE) [49]
may be included in research designs to collect baseline data for pre-stroke cognitive
performance.
The exclusion of patients with TIA may be confounding since TIA may raise the risk of cog-
nitive impairment in at least one cognitive domain by approximately 30% [50], underscoring
the benefits of screening cognitive changes in minor cerebrovascular [51]. Similarly, using
MoCA rather than the gold standard test (comprehensive neuropsychological battery) limited
the diagnostic accuracy, grading the severity of cognitive impairments, determining functional
limitations, and planning for ideal treatment and rehabilitation [52].
Conclusion
Post-stroke cognitive impairment is a common manifestation among stroke survivors in the
early phase of recovery. Factors associated with PSCI are predictable; thus, identifying and tar-
geting individuals at risk for specific interventions in the acute setting is crucial. For a compre-
hensive understanding of the magnitude, drivers, characteristics and overall clinical course of
PSCI, well-designed long-term prospective research, including clinical trials, is necessary for
progress.
Supporting information
S1 Checklist. STROBE statement—checklist of items that should be included in reports of
observational studies.
(DOCX)
S1 File. IRB approval for data collection.
(PDF)
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S2 File. Psci excel deidentified data.
(XLSX)
Author Contributions
Conceptualization: Baraka Alphonce, John Meda, Azan Nyundo.
Data curation: Baraka Alphonce.
Formal analysis: Baraka Alphonce.
Investigation: Baraka Alphonce.
Methodology: Baraka Alphonce, Azan Nyundo.
Supervision: John Meda, Azan Nyundo.
Writing – original draft: Baraka Alphonce.
Writing – review & editing: Baraka Alphonce, John Meda, Azan Nyundo.
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