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Record W2999171528 · doi:10.1161/strokeaha.119.027268

A Risk Score Including Carotid Plaque Inflammation and Stenosis Severity Improves Identification of Recurrent Stroke

2020· article· en· W2999171528 on OpenAlexaff
Peter J. Kelly, Pol Camps‐Renom, Nicola Giannotti, Joan Martí‐Fábregas, J.P. McNulty, Jean‐Claude Baron, Mary Barry, Shelagh B. Coutts, Simon Cronin, Raquel Delgado‐Mederos, Eamon Dolan, Alejandro Fernández‐León, Shane Foley, Joseph Harbison, Gillian Horgan, Eoin C. Kavanagh, Michael Marnane, John McCabe, Ciarán McDonnell, Vijay K. Sharma, David Williams, Martin O’Connell, Séan Murphy

Bibliographic record

VenueStroke · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsUniversity of Calgary
FundersInstituto de Salud Carlos IIINational Medical Research CouncilMedical Research CouncilIrish Heart Foundation
KeywordsMedicineStenosisStroke (engine)Internal medicineHazard ratioCardiologyCarotid endarterectomyCohortConfidence interval

Abstract

fetched live from OpenAlex

Background and Purpose— In randomized trials of symptomatic carotid endarterectomy, only modest benefit occurred in patients with moderate stenosis and important subgroups experienced no benefit. Carotid plaque 18 F-fluorodeoxyglucose uptake on positron emission tomography, reflecting inflammation, independently predicts recurrent stroke. We investigated if a risk score combining stenosis and plaque 18 F-fluorodeoxyglucose would improve the identification of early recurrent stroke. Methods— We derived the score in a prospective cohort study of recent (<30 days) non-severe (modified Rankin Scale score ≤3) stroke/transient ischemic attack. We derived the SCAIL (symptomatic carotid atheroma inflammation lumen-stenosis) score (range, 0–5) including 18 F-fluorodeoxyglucose standardized uptake values (SUV max <2 g/mL, 0 points; SUV max 2–2.99 g/mL, 1 point; SUV max 3–3.99 g/mL, 2 points; SUV max ≥4 g/mL, 3 points) and stenosis (<50%, 0 points; 50%–69%, 1 point; ≥70%, 2 points). We validated the score in an independent pooled cohort of 2 studies. In the pooled cohorts, we investigated the SCAIL score to discriminate recurrent stroke after the index stroke/transient ischemic attack, after positron emission tomography-imaging, and in mild or moderate stenosis. Results— In the derivation cohort (109 patients), recurrent stroke risk increased with increasing SCAIL score ( P =0.002, C statistic 0.71 [95% CI, 0.56–0.86]). The adjusted (age, sex, smoking, hypertension, diabetes mellitus, antiplatelets, and statins) hazard ratio per 1-point SCAIL increase was 2.4 (95% CI, 1.2–4.5, P =0.01). Findings were confirmed in the validation cohort (87 patients, adjusted hazard ratio, 2.9 [95% CI, 1.9–5], P <0.001; C statistic 0.77 [95% CI, 0.67–0.87]). The SCAIL score independently predicted recurrent stroke after positron emission tomography-imaging (adjusted hazard ratio, 4.52 [95% CI, 1.58–12.93], P =0.005). Compared with stenosis severity (C statistic, 0.63 [95% CI, 0.46–0.80]), prediction of post-positron emission tomography stroke recurrence was improved with the SCAIL score (C statistic, 0.82 [95% CI, 0.66–0.97], P =0.04). Findings were confirmed in mild or moderate stenosis (adjusted hazard ratio, 2.74 [95% CI, 1.39–5.39], P =0.004). Conclusions— The SCAIL score improved the identification of early recurrent stroke. Randomized trials are needed to test if a combined stenosis-inflammation strategy improves selection for carotid revascularization where benefit is currently uncertain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.254
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations58
Published2020
Admission routes1
Has abstractyes

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