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Record W4297312018 · doi:10.3389/fneur.2022.939215

Eptifibatide use in ischemic stroke patients undergoing endovascular thrombectomy: A matched cohort analysis

2022· article· en· W4297312018 on OpenAlexaboutno aff
Ameena Rana, Siyuan Yu, Savina Reid-Herrera, Scott Kamen, Krystal Hunter, Hamza Shaikh, Tudor G. Jovin, Olga R. Thon, Parth Patel, James E. Siegler, Jesse Thon

Bibliographic record

VenueFrontiers in Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineThrombolysisStroke (engine)Logistic regressionInternal medicineEptifibatideCohortIntracerebral hemorrhagePropensity score matchingHematomaSubarachnoid hemorrhageSurgeryCardiologyPercutaneous coronary intervention

Abstract

Introduction Small studies have suggested that eptifibatide (EPT) may be safe in acute ischemic stroke (AIS) following intravenous thrombolysis or during endovascular therapy (EVT) for large vessel occlusion (LVO). However, studies are called upon to better delineate the safety of EPT use during EVT. Methods A comprehensive stroke center registry (09/2015-12/2020) of consecutive adults who had undergone EVT for anterior LVO was queried. Patients treated with EPT were matched with 2 control groups based on known factors associated with intracranial hemorrhage (ICH) risk - age, Alberta Stroke Program Early Computed Tomography Score (ASPECTS), and number of thrombectomy passes. Safety outcomes (intracranial hemorrhage [ICH], parenchymal hematoma [PH-2] grade hemorrhagic transformation, symptomatic ICH [sICH]) and efficacy outcomes (TICI 2B/3 recanalization, 24-h National Institutes of Health Stroke Scale [NIHSS] score), were compared between matched groups using descriptive statistics. In addition, multivariable logistic regression was used to assess for an association between EPT and PH-1/PH-2 grade hemorrhages. Results A total of 162 patients were included, 54 of whom (33%) received EPT. The rate of ICH was similar between groups ( p = 0.62), while PH-2 was significantly more frequent with EPT (16.7% EPT vs. 3.7 vs. 1.9%; p = 0.009), but without significant differences in sICH (5.6% EPT vs. 7.4 vs. 3.7%; p = 0.72). Rates of TICI Score ≥ 2B were nominally higher with EPT use (83.3 vs. 77.8 vs. 77.8%, p = 0.70). Between the EPT and control groups, there were no differences in 24-h NIHSS ( p = 0.09) or 90-day mortality ( p = 0.58). Our adjusted multivariate analysis identified that the number of passes ( p < 0.01), EPT use ( p < 0.01), and tandem occlusion ( p = 0.03) were independent predictors of PH1/PH2 grade hemorrhage. Additionally, every unit increase in number of passes resulted in a 1.5 times greater odds of a high-grade hemorrhagic transformation in EPT-treated patients (adjusted OR = 1.594). Conclusion In this single-center analysis, EPT use during EVT was associated with a significantly higher rate of PH1/PH2 grade hemorrhages, but not with differences in sICH, 24-h NIHSS, or 90-day mortality. Randomized prospective trials are needed to determine the safety and efficacy of EPT in this population.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: about_only · design weight: 3321.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Matched cohort analysis of drug safety during thrombectomy; clinical question.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

This cohort analysis evaluates a stroke treatment, not research practice.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Clinical stroke cohort on eptifibatide during thrombectomy.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2022
Admission routes1
Has abstractyes

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