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Abstract 213: Treatment Delays in Patients with ST-Elevation Myocardial Infarction Across All Hospitals Performing Primary Percutaneous Coronary Intervention in Quebec, Canada: Results of a Third Field Evaluation

2015· article· en· W1935051144 on OpenAlexaffabout
Laurie Lambert, L. Azzi, Richard Harvey, Simon Kouz, Philippe L. L’Allier, Sébastien Maire, Stéphane Rinfret, Dave Ross, Eli Segal, Céline Carroll, C. Beauchamp, Lucy J. Boothroyd, Peter Bogaty

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsJewish General HospitalInstitut universitaire de cardiologie et de pneumologie de QuébecCégep de LévisMontreal Heart InstituteCegep regional de LanaudiereCentre Hospitalier Universitaire de SherbrookeSanté MontérégieInstitut National d'Excellence en Santé et en Services Sociaux
Fundersnot available
KeywordsMedicinePercutaneous coronary interventionMyocardial infarctionEmergency medicineMedical emergencyReperfusion therapyEmergency departmentEmergency medical servicesCardiologyNursing

Abstract

fetched live from OpenAlex

Background: Our government-funded independent cardiology evaluation unit conducted a third systematic province-wide field evaluation of ST-elevation myocardial infarction (STEMI) in collaboration with an interdisciplinary committee of clinical experts. Primary percutaneous coronary intervention (PPCI) is the predominant reperfusion treatment for STEMI in Quebec and in 2 previous evaluations (2006-7; 2008-9), there were wide variations in treatment delays across the 13 PPCI hospitals. Initiatives to improve delays have included individualized hospital report cards, presentations and discussions of inter-regional analyses with clinicians and decision-makers and the province-wide implantation of an ambulance (prehospital) ECG program. Our objective was to describe the differences in STEMI processes of care over time that these initiatives may have produced. Methods: Medical charts of all patients with a final diagnosis of acute myocardial infarction who presented with characteristic symptoms to an emergency department (ED) during a 6-month period in 2013-2014 were reviewed. Trained medical record librarians collected data on clinical characteristics and time points of care. An automated algorithm was used to identify STEMI. All patients with STEMI who presented directly to the ED of one of the 13 PPCI centers (by ambulance or other means) and were treated with PPCI were included in this preliminary analysis. ECGs were reviewed at our core laboratory. Results: During the 6-month study period, 735 patients with STEMI presented directly to the ED of a PPCI hospital (including ambulance bypass of local non-PPCI hospitals) and were treated with PPCI. Center patient volume varied from 29 to 117. Over 80% (596/735) of the patients arrived by ambulance (up from 71% in 2009-9) and of these, 93% (552/596) had a prehospital ECG compared with 20% in 2008-9. The median ED door-in-door-out delay was 26 min (10-90th percentile: 8-72) compared with 49 min (17-126) in 2008-9 (p=0.001). The median door-to-device delay was 59 min (30-125), compared with 77 min (38-163) during 2008-9 (p=0.017), and varied from a median of 38 to 87 min across the 13 PPCI hospitals. For patients arriving by ambulance with documented first medical contact time, 48% (260/541) had a first medical contact-to-device time ≤ 90 min compared with 35% in 2008-9 (p=0.06). Conclusions: This third province-wide evaluation shows important improvements in Quebec’s system of STEMI care. Since 2008-9, ED door-in-door-out delay improved by 47% and door-to-device delay for direct admission PPCI improved by 23%. These improvements have occurred after local, regional and provincial efforts to monitor and reduce treatment delays and the implantation of a province-wide prehospital ECG program. However, the wide variation in delays across PPCI centers underlines the need for continued evaluation and improvement initiatives.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.312
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.336
Teacher spread0.284 · 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".

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Citations1
Published2015
Admission routes2
Has abstractyes

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