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Components and Trends in Door to Treatment Times for Endovascular Therapy in Get With The Guidelines-Stroke Hospitals

2019· article· en· W2910684244 on OpenAlexaff
Bijoy K. Menon, Haolin Xu, Margueritte Cox, Jeffrey L. Saver, Mayank Goyal, Eric D. Peterson, Ying Xian, Roland Matsuoka, Reza Jehan, Dileep R. Yavagal, Rishi Gupta, Brijesh Mehta, Deepak L. Bhatt, Gregg C. Fonarow, Lee H. Schwamm, Eric E. Smith

Bibliographic record

VenueCirculation · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
FundersEisaiBelvoir Media GroupDuke Clinical Research InstituteMedicines CompanyAmarin CorporationRegeneron PharmaceuticalsIdorsia PharmaceuticalsCleveland ClinicAmgenPfizerIronwood Pharmaceuticals, IncorporatedBristol-Myers SquibbEli Lilly and CompanySanofiAmerican Heart Association
KeywordsMedicineInterquartile rangeStroke (engine)Emergency medicineEmergency departmentFirst passConfidence intervalIschemic strokeClinical trialSurgeryInternal medicineIschemia

Abstract

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BACKGROUND: Endovascular therapy (EVT) is standard of care in patients with acute disabling ischemic stroke attributable to large-vessel occlusion and is more effective when delivered quickly. It is currently unclear whether time targets achieved in clinical trials can be achieved in clinical practice. We describe interval times from patient arrival in the emergency department (door) to first pass (treatment initiation) in patients receiving EVT within Get With The Guidelines-Stroke hospitals and analyze patient- and hospital-level variables associated with these times. METHODS: Data are from sites participating fully as Comprehensive Stroke Centers within Get With The Guidelines-Stroke hospitals from October 2014 to September 2016. Workflow times analyzed include door to imaging, imaging to arterial access, arterial access to first pass, and the composite door to first pass time. Data are described overall and by calendar-year quarters. Multivariable modeling was used to identify patient- and hospital-level variables associated with workflow times. RESULTS: Among 2929 patients with EVT from 195 hospitals (median age, 71 years [interquartile range {IQR}, 60-81]; 50.7% female; median baseline National Institute of Health Stroke Score, 17 [IQR, 12-22]; median annual EVT administration number, 16 [IQR, 10-27]), median door to first pass time was 130 minutes (IQR, 101-170 minutes), door to imaging time was 12 minutes (IQR, 7-20 minutes), imaging to arterial puncture time was 93 minutes (IQR, 68-126 minutes), and arterial puncture to first pass time was 18 minutes (IQR, 4-31 minutes). Overall, 3% patients achieved a door to first pass time ≤60 minutes. A statistically significant linear time trend was noted for door to first pass time (quarter 4 year 2014 median time, 134.5 minutes to quarter 3 year 2016 median time, 128 minutes, P=0.002). In multivariable analysis, older age, arrival during nonregular hours, and history of diabetes mellitus were associated with longer door to first pass time. Hospitals achieving shorter door to intravenous alteplase administration (door to needle) times were more likely to achieve faster door to first pass time ( P<0.001). Each 5 cases/y increase in EVT case volume was associated with a 3% shorter door to first pass time, up to a case volume of 40 per year ( P<0.001). CONCLUSIONS: Although EVT treatment times are modestly improving, additional efforts are needed to streamline workflow so that the true potential of this treatment is realized. These data may inform benchmark goals for EVT workflow times.

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)0.002
metaresearch head score (Gemma)0.013
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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".

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Citations46
Published2019
Admission routes1
Has abstractyes

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