Evidence map›Paper›PMID 41362543›Full record

ArticleCureus2025

COVID-19 and Stroke: Evaluating Correlations, Risk Factors, and Patient Outcomes.

Hassan A Farid, Samina Alim, Fiza Shaikh, Ali R Hashim

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hassan A FaridNeurology, Manchester Centre for Clinical Neurosciences, Salford, GBR.
Samina AlimNeurology, City St George's, University of London, London, GBR.
Fiza ShaikhNeurology, City St George's, University of London, London, GBR.
Ali R HashimNeurology, University of Basrah, Basrah, IRQ.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) has been associated with a range of neurological complications, including an increased risk of stroke. This study aimed to examine the relationship between COVID-19 and stroke risk in hospitalized patients. A retrospective cohort study was conducted involving 280 patients with COVID-19 and 300 non-COVID-19 controls. Sociodemographic data, medical history, COVID-19 characteristics, and inflammatory biomarkers were analyzed using chi-square tests and unpaired Student's t-tests to assess associations with stroke occurrence. Sociodemographic factors, including age, sex, smoking status, and prevalence of chronic conditions, did not significantly differ between the two groups (p > 0.05). However, COVID-19 patients exhibited a higher prevalence of ischemic stroke (n = 17, 6.07%), hemorrhagic stroke (n = 4, 1.43%), and cerebral venous thrombosis (n = 3, 1.07%) compared to non-COVID-19 controls (p = 0.019). Severe COVID-19, prolonged illness (>2 weeks), low oxygen saturation (<70%), and cytokine storm were significantly associated with increased stroke risk (p < 0.05). Elevated interleukin-6 levels were also strongly correlated with stroke occurrence (p = 0.001). Notably, 66.7% (n = 16) of strokes occurred within the first month of infection. This study underscores the importance of considering demographic, medical, and COVID-19-related factors when assessing stroke risk in patients with COVID-19. The findings also highlight the potential role of inflammatory biomarkers, particularly interleukin-6, as indicators of stroke risk in this population.

Indexed as

cerebral venous sinus thrombosiscovid-19cytokine storminterleukin (il)-6intracranial hemorrhage (ich)ischemic strokepandemicstrokestroke mortalityworld pandemic

Identifiers

PMID41362543
PMCPMC12682340

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Registered trials

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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 OpenQuestion graph.