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Record W2918067884 · doi:10.1161/strokeaha.118.023769

Glucose Modifies the Effect of Endovascular Thrombectomy in Patients With Acute Stroke

2019· review· en· W2918067884 on OpenAlexaff
Ángel Chamorro, Scott Brown, Sergio Amaro, Michael D. Hill, Keith W. Muir, Diederik W.J. Dippel, Wim H. van Zwam, Kenneth Butcher, Gary A. Ford, Heleen M. den Hertog, Peter Mitchell, Andrew M. Demchuk, Charles B.L.M. Majoie, Serge Bracard, Igor Sibon, Ashutosh P. Jadhav, Blanca Lara‐Rodríguez, Aad van der Lugt, Elizabeth Osei, Arturo Renú, Sébastien Richard, David Rodríguez‐Luna, Geoffrey A. Donnan, Anand Dixit, Mohammed Almekhlafi, S. Deltour, Jonathan Epstein, B. Guillon, Serge Bakchine, Meritxell Gomis, Richard du Mesnil de Rochemont, Demetrius K. Lopes, Vivek Reddy, G. Rüdel, Yvo B.W.E.M. Roos, Alain Bonafé, Hans‐Christoph Diener, Olvert A. Berkhemer, Geoffrey Cloud, Stephen M. Davis, Robert van Oostenbrugge, Françis Guillemin, Mayank Goyal, Bruce Campbell, Bijoy K. Menon

Bibliographic record

VenueStroke · 2019
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of AlbertaUniversity of Calgary
FundersCilagEuropean Regional Development FundAstraZenecaAllerganNational Health and Medical Research CouncilPenumbraMedical Research CouncilBayer VitalNational Institutes of HealthMedacCentres de Recerca de CatalunyaH. Lundbeck A/SDaiichi Sankyo EuropeServierNational Stroke FoundationDaiichi-SankyoGeneralitat de CatalunyaNational Institute for Health and Care ResearchGlaxoSmithKlineSanofiEuropean CommissionStrykerZonMwInstituto de Salud Carlos IIIAmgenPfizerEli Lilly and Company
KeywordsMedicineInterquartile rangeModified Rankin ScaleStroke (engine)Odds ratioInternal medicineRandomized controlled trialLogistic regressionIschemic strokeIschemia

Abstract

fetched live from OpenAlex

Background and Purpose- Hyperglycemia is a negative prognostic factor after acute ischemic stroke but is not known whether glucose is associated with the effects of endovascular thrombectomy (EVT) in patients with large-vessel stroke. In a pooled-data meta-analysis, we analyzed whether serum glucose is a treatment modifier of the efficacy of EVT in acute stroke. Methods- Seven randomized trials compared EVT with standard care between 2010 and 2017 (HERMES Collaboration [highly effective reperfusion using multiple endovascular devices]). One thousand seven hundred and sixty-four patients with large-vessel stroke were allocated to EVT (n=871) or standard care (n=893). Measurements included blood glucose on admission and functional outcome (modified Rankin Scale range, 0-6; lower scores indicating less disability) at 3 months. The primary analysis evaluated whether glucose modified the effect of EVT over standard care on functional outcome, using ordinal logistic regression to test the interaction between treatment and glucose level. Results- Median (interquartile range) serum glucose on admission was 120 (104-140) mg/dL (6.6 mmol/L [5.7-7.7] mmol/L). EVT was better than standard care in the overall pooled-data analysis adjusted common odds ratio (acOR), 2.00 (95% CI, 1.69-2.38); however, lower glucose levels were associated with greater effects of EVT over standard care. The interaction was nonlinear such that significant interactions were found in subgroups of patients split at glucose < or >90 mg/dL (5.0 mmol/L; P=0.019 for interaction; acOR, 3.81; 95% CI, 1.73-8.41 for patients < 90 mg/dL versus 1.83; 95% CI, 1.53-2.19 for patients >90 mg/dL), and glucose < or >100 mg/dL (5.5 mmol/L; P=0.004 for interaction; acOR, 3.17; 95% CI, 2.04-4.93 versus acOR, 1.72; 95% CI, 1.42-2.08) but not between subgroups above these levels of glucose. Conclusions- EVT improved stroke outcomes compared with standard treatment regardless of glucose levels, but the treatment effects were larger at lower glucose levels, with significant interaction effects persisting up to 90 to 100 mg/dL (5.0-5.5 mmol/L). Whether tight control of glucose improves the efficacy of EVT after large-vessel stroke warrants appropriate testing.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations91
Published2019
Admission routes1
Has abstractyes

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