Evidence map›Paper›PMID 39708145›Full record

ArticleJournal of neurology2024

Using clinical data to reclassify ESUS patients to large artery atherosclerotic or cardioembolic stroke mechanisms.

Lauren Klein-Murrey, David L Tirschwell, Daniel S Hippe, Mona Kharaji, Cristina Sanchez-Vizcaino, Brooke Haines, Niranjan Balu, Thomas S Hatsukami, Chun Yuan, Nazem W Akoum and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Lauren Klein-MurreyDepartment of Neurology, Harborview Medical Center, University of Washington School of Medicine, 325 Ninth Avenue, Seattle, WA, USA.
David L TirschwellDepartment of Neurology, Harborview Medical Center, University of Washington School of Medicine, 325 Ninth Avenue, Seattle, WA, USA.
Daniel S HippeClinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Mona KharajiVascular Imaging Lab, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
Cristina Sanchez-VizcainoVascular Imaging Lab, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
Brooke HainesDepartment of Neurology, Harborview Medical Center, University of Washington School of Medicine, 325 Ninth Avenue, Seattle, WA, USA.
Niranjan BaluVascular Imaging Lab, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
Thomas S HatsukamiVascular Imaging Lab, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
Chun YuanVascular Imaging Lab, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
Nazem W AkoumDepartment of Cardiology, University of Washington School of Medicine, Seattle, WA, USA.
Eardi LilaDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Mahmud Mossa-BashaVascular Imaging Lab, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA. mmossab@uw.edu.ORCID http://orcid.org/0000-0001-7798-8158

Funding

Quantitative model-based ESUS reclassification using cardiac and cerebral vessel wall MRIR01NS125635 · NINDS · UNIVERSITY OF WASHINGTON · PI AKOUM, NAZEM, MOSSA-BASHA, MAHMUD · 2022 to 2025
$3.0M
NINDS NIH HHS R01 NS125635NINDS NIH HHS R01NS125635
6 · The paper itself

Abstract

purposeEmbolic stroke of unidentified source (ESUS) represents 10-25% of all ischemic strokes. Our goal was to determine whether ESUS could be reclassified to cardioembolic (CE) or large-artery atherosclerosis (LAA) with machine learning (ML) using conventional clinical data.

methodsWe retrospectively collected conventional clinical features, including patient, imaging (MRI, CT/CTA), cardiac, and serum data from established cases of CE and LAA stroke, and factors with p < 0.2 in univariable analysis were used for creating a ML predictive tool. We then applied this tool to ESUS cases, with ≥ 75% likelihood serving as the threshold for reclassification to CE or LAA. In patients with longitudinal data, we evaluated future cardiovascular events.

results191 ischemic stroke patients (80 CE, 61 LAA, 50 ESUS) were included. Seven and 6 predictors positively associated with CE and LAA etiology, respectively. The c-statistic for discrimination between CE and LAA was 0.88. The strongest predictors for CE were left atrial volume index (OR = 2.17 per 1 SD increase) and BNP (OR = 1.83 per 1 SD increase), while the number of non-calcified stenoses ≥ 30% upstream (OR = 0.34 per 1 SD increase) and not upstream (OR = 0.74 per 1 SD increase) from the infarct were for LAA. When applied to ESUS cases, the model reclassified 40% (20/50), with 11/50 reclassified to CE and 9/50 reclassified to LAA. In 21/50 ESUS with 30-day cardiac monitoring, 1/4 in CE and 3/16 equivocal reclassifications registered cardiac events, while 0/1 LAA reclassifications showed events.

conclusionML tools built using standard ischemic stroke workup clinical biomarkers can potentially reclassify ESUS stroke patients into cardioembolic or atherosclerotic etiology categories.

Indexed as

AtherosclerosisEmbolic StrokeMachine LearningAgedAged, 80 and overFemaleHumansIschemic StrokeMaleMiddle AgedRetrospective StudiesAtherosclerosisCardioembolismMachine learningStroke

Identifiers

PMID39708145
PMCPMC13418418

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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.