Evidence map›Paper›PMID 35324905›Full record

ArticlePloS one2022

Trends in stroke admissions before, during and post-peak of the COVID-19 pandemic: A one-year experience from the Qatar stroke database.

Naveed Akhtar, Saadat Kamran, Salman Al-Jerdi, Yahia Imam, Sujatha Joseph, Deborah Morgan, Mohamed Abokersh, R T Uy, Ashfaq Shuaib

Abstract read
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Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

9 authors.

Naveed AkhtarThe Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar.
Saadat KamranThe Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar.
Salman Al-JerdiWeill Cornell Medical College- Qatar Foundation, Doha, Qatar.
Yahia ImamThe Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar.
Sujatha JosephThe Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar.
Deborah MorganThe Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar.
Mohamed AbokershThe Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar.
R T UyThe Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar.
Ashfaq ShuaibNeurology Division, Department of Medicine, University of Alberta, Edmonton, Canada.ORCID 0000-0002-3380-7068

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral reports document a decrease in the rates of stroke hospital admissions during the covid-19 pandemic. There is very little information whether the admission rates will change as the infection is controlled. We report on our rates of admissions before, during and following the peak of covid-19 infections in a prospective database from Qatar. METHODS AND

resultsThe stroke admissions in the six months prior to COVID-19 pandemic averaged 229/month. There was a decrease to 157/month in March-June during the peak of the pandemic. In the 6 months following the peak, as covid-19 numbers began to decrease, the average numbers increased back to 192/month. There was an increase in severe ischemic strokes and decreased in functional recovery. The decreased admissions were mainly driven by fewer stroke mimics. Patients presenting with ischemic stroke or cerebral hemorrhage remained unchanged.

conclusionsFewer stroke mimics presenting to the hospital can explain the fewer admissions and poor outcome at the height of the covid-19 pandemic. The continued decrease in the number of ischemic stroke and stroke mimic admissions following the pandemic peak requires more study.

Indexed as

COVID-19Ischemic StrokeStrokeHumansPandemicsQatarRetrospective Studies

Identifiers

PMID35324905
PMCPMC8947388

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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.