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Record W4366602027 · doi:10.1161/strokeaha.122.042200

Infarcts in a New Territory: Insights From the ESCAPE-NA1 Trial

2023· article· en· W4366602027 on OpenAlexaff
Nishita Singh, Petra Cimflová, Johanna M. Ospel, Nima Kashani, Martha Marko, Arnuv Mayank, Raul G. Nogueira, Ryan McTaggart, Andrew M. Demchuk, Alexandre Y. Poppe, Jeremy Rempel, Thalia S. Field, Dar Dowlatshahi, Brian van Adel, Richard H. Swartz, Ruchir Shah, Eric Sauvageau, Volker Puetz, Frank L. Silver, Bruce Campbell, René Chapot, Michael Tymianski, Mayank Goyal, Mohammed Almekhlafi, Michael D. Hill

Bibliographic record

VenueStroke · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsNoNO (Canada)University of TorontoSt. Michael's HospitalSunnybrook Health Science CentreMcMaster UniversityUniversity of British ColumbiaUniversity Health NetworkHealth Sciences CentreOttawa HospitalUniversity of Alberta HospitalManitoba HealthCentre Hospitalier de l’Université de MontréalUniversity of CalgaryUniversity of ManitobaFoothills Medical CentreUniversity of Ottawa
Fundersnot available
KeywordsMedicineThrombolysisModified Rankin ScaleStroke (engine)Magnetic resonance imagingRandomized controlled trialInternal medicineSurgeryRadiologyIschemiaIschemic strokeMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: Infarct in a new territory (INT) is a known complication of endovascular stroke therapy. We assessed the incidence of INT, outcomes after INT, and the impact of concurrent treatments with intravenous thrombolysis and nerinetide. METHODS: Data are from ESCAPE-NA1 trial (Safety and Efficacy of Nerinetide [NA-1] in Subjects Undergoing Endovascular Thrombectomy for Stroke), a multicenter, international randomized study that assessed the efficacy of intravenous nerinetide in subjects with acute ischemic stroke who underwent endovascular thrombectomy within 12 hours from onset. Concurrent treatment and outcomes were collected as part of the trial protocol. INTs were identified on core lab imaging review of follow-up brain imaging and defined by the presence of infarct in a new vascular territory, outside the baseline target occlusion(s) on follow-up brain imaging (computed tomography or magnetic resonance imaging). INTs were classified by maximum diameter (<2, 2-20, and >20 mm), number, and location. The association between INT and clinical outcomes (modified Rankin Scale and death) was assessed using standard descriptive techniques and adjusted estimates of effect were derived from Poisson regression models. RESULTS: Among 1092 patients, 103 had INT (9.3%, median age 69.5 years, 49.5% females). There were no differences in baseline characteristics between those with versus without INT. Most INTs (91/103, 88.3%) were not associated with visible occlusions on angiography and 39 out of 103 (37.8%) were >20 mm in maximal diameter. The most common INT territory was the anterior cerebral artery (27.8%). Almost half of the INTs were multiple (46 subjects, 43.5%, range, 2-12). INT was associated with poorer outcomes as compared to no INT on the primary outcome of modified Rankin Scale score of 0 to 2 at 90 days (adjusted risk ratio, 0.71 [95% CI, 0.57-0.89]). Infarct volume in those with INT was greater by a median of 21 cc compared with those without, and there was a greater risk of death as compared to patients with no INT (adjusted risk ratio, 2.15 [95% CI, 1.48-3.13]). CONCLUSIONS: Infarcts in a new territory are common in individuals undergoing endovascular thrombectomy for acute ischemic stroke and are associated with poorer outcomes. Optimal therapeutic approaches, including technical strategies, to reduce INT represent a new target for incremental quality improvement of endovascular thrombectomy. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT02930018.

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 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)0.008
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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