Evidence map›Paper›PMID 37098461›Full record

ArticleBMC neurology2023

Prevalence and predictors of post-stroke cognitive impairment among stroke survivors in Uganda.

Martin N Kaddumukasa, Mark Kaddumukasa, Elly Katabira, Nelson Sewankambo, Lillian D Namujju, Larry B Goldstein

Open access · goldAbstract read
In one paragraph

Article in BMC neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
3.6field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.

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  10. Machine learning-based predictive model for post-stroke dementia.BMC medical informatics and decision making · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Martin N KaddumukasaDepartment of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.
Mark KaddumukasaDepartment of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.
Elly KatabiraDepartment of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.
Nelson SewankamboDepartment of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda.
Lillian D NamujjuDepartment of Electrical and Computer Engineering, College of Engineering, Design, Art and Technology, Makerere University, Kampala, Uganda.
Larry B GoldsteinDepartment of Neurology, University of Kentucky, Lexington, KY, USA.
Makerere University · UGUniversity of Kentucky · US

Funding

NURTURE:Research Training and Mentoring Program for Career Development of Faculty at Makerere University College of Health SciencesD43TW010132 · FIC · MAKERERE UNIVERSITY COLLEGE OF HEALTH SCIENCES · PI SEWANKAMBO, NELSON K · 2015 to 2019
$4.3M
FIC NIH HHS D43 TW010132FIC NIH HHS D43TW010132NIMHD NIH HHS D43TW010132
6 · The paper itself

Abstract

backgroundLittle is known about the characteristics and determinants of post-stroke cognitive impairment in residents of low- and middle-income countries. The objective of this study was to determine the frequencies, patterns, and risk factors for cognitive impairment in a cross-sectional study of consecutive stroke patients cared for at Uganda's Mulago Hospital, located in sub-Saharan Africa.

methods131 patients were enrolled a minimum of 3-months after hospital admission for stroke. A questionnaire, clinical examination findings, and laboratory test results were used to collect demographic information and data on vascular risk factors and clinical characteristics. Independent predictor variables associated with cognitive impairment were ascertained. Stroke impairments, disability, and handicap were assessed using the National Institute of Health Stroke Scale (NIHSS), Barthel Index (BI), and modified Rankin scale (mRS), respectively. The Montreal Cognitive Assessment (MoCA) was used to assess participants' cognitive function. Stepwise multiple logistic regression was used to identify variables independently associated with cognitive impairment.

resultsThe overall mean MoCA score was 11.7-points (range 0.0-28.0-points) for 128 patients with available data of whom 66.4% were categorized as cognitively impaired (MoCA < 19-points). Increasing age (OR 1.04, 95% CI 1.00-1.07; p = 0.026), low level of education (OR 3.23, 95% CI 1.25-8.33; p = 0.016), functional handicap (mRS 3-5; OR 1.84, 95% CI 1.28-2.63; p < 0.001) and high LDL cholesterol (OR 2.74, 95% CI 1.14-6.56; p = 0.024) were independently associated with cognitive impairment.

conclusionsOur findings highlight the high burden and need for awareness of cognitive impairment in post stroke populations in the sub-Saharan region and serve to emphasize the importance of detailed cognitive assessment as part of routine clinical evaluation of patients who have had a stroke.

Indexed as

Cognitive DysfunctionStrokeCross-Sectional StudiesHumansNeuropsychological TestsPrevalenceSurvivorsUgandaCognitive impairmentDementiaMontreal Cognitive AssessmentStrokeSub-Saharan Africa

Identifiers

PMID37098461
PMCPMC10127321
OpenAlexW4366988565

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.