Evidence map›Paper›PMID 36588883›Full record

ArticleFrontiers in neurology2022

Automated quantification of atrophy and acute ischemic volume for outcome prediction in endovascular thrombectomy.

Balázs Kis, Ain A Neuhaus, George Harston, Olivier Joly, Davide Carone, Stephen Gerry, Zoltán Chadaide, András Pánczél, Eszter Czifrus, Viktória Csike and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2022. 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
0.6field-weighted citation impact, top 31% 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, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Utilizing Quantitative Analysis of CSF Volume from Clinical T1-Weighted MRI to Predict Thrombectomy Outcomes.Journal of neuroimaging : official journal of the American Society of Neuroimaging
    Article
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

13 authors at 3 institutions in 2 countries.

Balázs KisNational Institute of Mental Health, Neurology, and Neurosurgery (NIMNN), Budapest, Hungary.
Ain A NeuhausOxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
George HarstonOxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Olivier JolyBrainomix Ltd., Oxford, United Kingdom.
Davide CaroneOxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Stephen GerryCentre for Statistics in Medicine, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom.
Zoltán ChadaideBrainomix Ltd., Oxford, United Kingdom.
András PánczélFaculty of Medicine, Semmelweis University, Budapest, Hungary.
Eszter CzifrusFaculty of Medicine, Semmelweis University, Budapest, Hungary.
Viktória CsikeNational Institute of Mental Health, Neurology, and Neurosurgery (NIMNN), Budapest, Hungary.
Ágnes SurányiNational Institute of Mental Health, Neurology, and Neurosurgery (NIMNN), Budapest, Hungary.
István SzikoraNational Institute of Mental Health, Neurology, and Neurosurgery (NIMNN), Budapest, Hungary.
Loránd ErőssNational Institute of Mental Health, Neurology, and Neurosurgery (NIMNN), Budapest, Hungary.
Brainomix (United Kingdom) · GBSemmelweis University · HUUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Short- and long-term outcomes from endovascular thrombectomy (EVT) for large vessel occlusion stroke remain variable. Numerous relevant predictors have been identified, including severity of neurological deficits, age, and imaging features. The latter is typically defined as acute changes (most commonly Alberta Stroke Programme Early CT Score, ASPECTS, at presentation), but there is little information on the impact of imaging assessment of premorbid brain health as a determinant of outcome. Aims: To examine the impact of automated measures of stroke severity and underlying brain frailty on short- and long-term outcomes in acute stroke treated with EVT. Methods: In 215 patients with anterior circulation stroke, who subsequently underwent EVT, automated analysis of presenting non-contrast CT scans was used to determine acute ischemic volume (AIV) and e-ASPECTS as markers of stroke severity, and cerebral atrophy as a marker of brain frailty. Univariate and multivariate logistic regression were used to identify significant predictors of NIHSS improvement, modified Rankin scale (mRS) at 90 and 30 days, mortality at 90 days and symptomatic intracranial hemorrhage (sICH) following successful EVT. Results: For long-term outcome, atrophy and presenting NIHSS were significant predictors of mRS 0-2 and death at 90 days, whereas age did not reach significance in multivariate analysis. Conversely, for short-term NIHSS improvement, AIV and age were significant predictors, unlike presenting NIHSS. The interaction between age and NIHSS was similar to the interaction of AIV and atrophy for mRS 0-2 at 90 days. Conclusion: Combinations of automated software-based imaging analysis and clinical data can be useful for predicting short-term neurological outcome and may improve long-term prognostication in EVT. These results provide a basis for future development of predictive tools built into decision-aiding software in stroke.

Indexed as

artificial intelligenceendovascular thrombectomymachine learningneuroimagingneuroradiologystrokethrombolysis

Identifiers

PMID36588883
PMCPMC9797714
OpenAlexW4311505149

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.