Evidence map›Paper›PMID 32819897›Full record

ArticleAJNR. American journal of neuroradiology2020

Regional Mechanical Thrombectomy Imaging Protocol in Patients Presenting with Acute Ischemic Stroke during the COVID-19 Pandemic.

P S Dhillon, K Pointon, R Lenthall, S Nair, G Subramanian, N McConachie, W Izzath

Open access · hybridAbstract read
In one paragraph

Article in AJNR. American journal of neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.2field-weighted citation impact, top 45% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Effect of sarcopenia on mortality in acute stroke patients receiving endovascular treatment.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2024
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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

7 authors at 2 institutions in 1 country.

P S DhillonFrom the Interventional Neuroradiology Department (P.S.D., R.L., S.N., N.M., W.I.), Queen's Medical Centre, Nottingham University Hospitals National Health Service Trust, Nottingham, UK permesh.dhillon@nhs.net.ORCID 0000-0003-4353-4515
K PointonCardiothoracic Radiology Department (K.P.).ORCID 0000-0003-1708-9235
R LenthallFrom the Interventional Neuroradiology Department (P.S.D., R.L., S.N., N.M., W.I.), Queen's Medical Centre, Nottingham University Hospitals National Health Service Trust, Nottingham, UK.ORCID 0000-0002-2386-1335
S NairFrom the Interventional Neuroradiology Department (P.S.D., R.L., S.N., N.M., W.I.), Queen's Medical Centre, Nottingham University Hospitals National Health Service Trust, Nottingham, UK.ORCID 0000-0001-5877-1033
G SubramanianStroke Medicine Department (G.S.), Nottingham City Hospital, Nottingham University Hospitals National Health Service Trust, Nottingham, UK.ORCID 0000-0002-6970-5979
N McConachieFrom the Interventional Neuroradiology Department (P.S.D., R.L., S.N., N.M., W.I.), Queen's Medical Centre, Nottingham University Hospitals National Health Service Trust, Nottingham, UK.ORCID 0000-0003-4829-4872
W IzzathFrom the Interventional Neuroradiology Department (P.S.D., R.L., S.N., N.M., W.I.), Queen's Medical Centre, Nottingham University Hospitals National Health Service Trust, Nottingham, UK.ORCID 0000-0003-1744-9683
National Health Service · GBEssex Cardiothoracic Centre · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeChest CT is a rapid, useful additional screening tool for coronavirus disease 2019 (COVID-19) in emergent procedures. We describe the feasibility and interim outcome of implementing a modified imaging algorithm for COVID-19 risk stratification across a regional network of primary stroke centers in the work-up of acute ischemic stroke referrals for time-critical mechanical thrombectomy. MATERIALS AND

methodsWe undertook a retrospective review of 49 patients referred to the regional neuroscience unit for consideration of mechanical thrombectomy between April 14, 2020, and May 21, 2020. During this time, all referring units followed a standard imaging protocol that included a chest CT in addition to a head CT and CT angiogram to identify Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infective pulmonary changes.

resultsOverall, 2 patients had typical COVID-19 radiologic features and tested positive, while 7 patients had indeterminate imaging findings and tested negative. The others had normal or atypical changes and were not diagnosed with or suspected of having COVID-19. There was an overall sensitivity of 100%, specificity of 74.1%, negative predictive value of 100%, and positive predictive value of 22.2% when using chest CT to diagnose COVID-19 in comparison with the real-time reverse transcriptase-polymerase chain reaction test. The mean additional time and radiation dose incurred for the chest CT were 184 ± 65.5 seconds and 2.47 ± 1.03 mSv. Multiple cardiovascular and pulmonary incidental findings of clinical relevance were identified in our patient population.

conclusionsChest CT provides a pragmatic, rapid additional tool for COVID-19 risk stratification among patients referred for mechanical thrombectomy. Its inclusion in a standardized regional stroke imaging protocol has enabled efficient use of hospital resources with minimal compromise or delay to the overall patient treatment schedule.

Indexed as

BetacoronavirusAgedAged, 80 and overBrain IschemiaCoronavirus InfectionsCOVID-19FemaleHumansMaleMiddle AgedPandemicsPneumonia, ViralRetrospective StudiesSARS-CoV-2StrokeThrombectomy

Identifiers

PMID32819897
PMCPMC7661079
OpenAlexW3080357975

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.