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Abstract 37: Long-term Outcomes Of Patients With ST-Elevation Myocardial Infarction (STEMI) With And Without Reperfusion Therapy: Results Of A Systematic Province-wide Field Evaluation In Quebec

2012· article· en· W2651221154 on OpenAlexaffabout
Laurie Lambert, Kevin Brown, James M. Brophy, Jim Nasmith, Eli Segal, Josep Rodés‐Cabau, Céline Carroll, Maude Giguère, Peter Bogaty

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsSt. Paul's HospitalMcGill University Health CentreInstitut universitaire de cardiologie et de pneumologie de QuébecInstitut National d'Excellence en Santé et en Services Sociaux
Fundersnot available
KeywordsMedicineReperfusion therapyPrimary angioplastyMyocardial infarctionRevascularizationThrombolysisAngioplastyEmergency medicineST elevationFibrinolysisFibrinolytic therapyMedical recordInternal medicinePercutaneous coronary interventionCardiologyMedical emergency

Abstract

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BACKGROUND: Reperfusion treatment of patients with ST-Elevation Myocardial Infarction (STEMI) reduces short-term morbidity and mortality. There is little real-world data on longer-term benefits. Our province-wide field evaluation of STEMI care linked process-of-care data to Quebec’s medico-administrative data to determine clinical outcomes at 3 years. METHODS: Medical charts of patients with a final diagnosis of myocardial infarction who presented with acute symptoms to one of 80 Quebec hospitals (that treat over 95% of such patients) were reviewed. STEMI was confirmed by core laboratory analysis of the presenting electrocardiogram. Patient characteristics, process-of-care data and treatment delays were entered in a centralized website. Patients who were not sent for primary angioplasty or did not receive fibrinolysis within 4 hours of presentation were classified as having no reperfusion treatment. Treated patients were classified according to guidelines, as having timely or untimely reperfusion therapy. For follow-up to 3 years, medical insurance numbers were linked to medico-administrative data recording hospitalisations, revascularization procedures and deaths. RESULTS: We identified 2035 patients with STEMI or left bundle branch block during a 6 month period (2006-7). Of these, 23% (n=469) were classified as not having received reperfusion treatment. Of the remaining 1566 patients, 564 had timely treatment and 891 had delays beyond the recommended maximum. Timeliness could not be classified for 111 patients who were sent for primary angioplasty but did not undergo device deployment. At 3 years follow-up, 39% (184/469) of patients without reperfusion treatment had died compared to 12% (190/1566) of patients with reperfusion treatment (RR=3.23; 95% CI=2.72-3.85). Patients without reperfusion treatment were also more likely than treated patients to be hospitalized for heart failure (13.2% vs 4.4%; RR=3.00; 95% CI=2.16-4.16) and reinfarction (7.2% vs 4.9%; RR=1.47; 95% CI=1.00-2.18). Of patients who received reperfusion treatment beyond maximum recommended delays, 14.4% (n=128/891) died within 3 years, compared to 6.9% (n=39/564) of those who received timely treatment (RR=2.26, 95% CI=1.25-3.02). Patients with untimely treatment were more likely than those with timely treatment to be hospitalized for heart failure (5.5% vs 2.3% ; RR=2.47; 95% CI=1.33-4.56) but their risk of readmission for myocardial infarction was similar (5.3% vs 4.1%; RR=1.31; 95% CI=0.79-2.18). After adjustment for demographic and clinical factors, the risk ratios for 3-year mortality associated with non-receipt of reperfusion therapy and untimely reperfusion therapy relative to timely reperfusion were 2.38 (95% CI=1.59-3.56) and 1.61 (95% CI=1.12-2.32), respectively. CONCLUSIONS: This systematic evaluation of STEMI care with long-term follow-up indicates that reperfusion treatment is associated with improved clinical outcomes at 3 years. Greater benefit was observed in patients who had reperfusion therapy within guideline-recommended delays. These results emphasize the importance of increasing the use of reperfusion therapy and decreasing treatment delays.

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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)0.004
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 score0.007
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.071
GPT teacher head0.343
Teacher spread0.272 · 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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Citations0
Published2012
Admission routes2
Has abstractyes

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