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Record W4380762884 · doi:10.1161/circ.146.suppl_1.9382

Abstract 9382: Reperfusion Delays and Outcomes Among ST-Segment-Elevation Myocardial Infarction Patients With and Without Cardiogenic Shock

2022· article· en· W4380762884 on OpenAlexaffabout
Andrew Kochan, Terry Lee, Nima Moghaddam, Grace Milley, Joel Singer, John A. Cairns, Graham C. Wong, Christopher B. Fordyce

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineCardiogenic shockMaceMyocardial infarctionConventional PCIInternal medicineCardiologyStroke (engine)Heart failureLogistic regressionPercutaneous coronary interventionReperfusion therapyEmergency medicine

Abstract

fetched live from OpenAlex

Introduction: Mortality remains high among STEMI patients with cardiogenic shock (CS), and rapid reperfusion has been shown to improve outcomes. However, the comparative benefits of shorter reperfusion times among STEMI patients with and without CS is unclear. Hypothesis: STEMI patients presenting with CS would have significantly greater increases in mortality and MACE as FMC-to-device time increased compared to non-CS patients. We hoped to identify a FMC-to-device time threshold below which outcomes were optimized for CS and non-CS patients. Methods: We performed a retrospective analysis using prospective data from the Vancouver Coastal Health Authority STEMI registry. We included all patients with STEMI who received PCI between January 1, 2010, and December 31, 2020. Patients were stratified based the presence of CS at the time of admission and assessed for the primary outcome of in-hospital mortality and the secondary outcome of an in-hospital major adverse cardiovascular event (MACE), defined as a composite of the first occurrence of mortality, cardiac arrest, heart failure, ICH/CVA/Stroke or reinfarction. Logistic regression was used to estimate the relationships between FMC-to-device time and the primary and secondary outcomes in the CS and non-CS groups. Results: 2929 consecutive STEMI patients were included, of whom 9.4% (n= 275) had CS. Median FMC-to-device time was 113.5 (IQR 93.0-145.0) minutes for CS patients and 103.0 (IQR 85.0- 130.0) minutes for non-CS patients. CS patients were more likely to have FMC-to-device times above guideline recommendations (76.6% vs. 54.1%, p <0.001) compared to non-CS patients. Between 60 and 90 minutes, for each 10-minute increase in FMC-to-device time, mortality for STEMI patients with CS increased by 5-8%, while for non-CS patients it increased by less than 0.5%. Compared to non-CS patients, those with CS had higher incidences of mortality (41.1% vs. 1.9%, p <0.001) and MACE (81.1% vs. 19.4%, p <0.001). Conclusions: Among STEMI patients undergoing primary PCI, reperfusion delays among CS patients are associated with significantly worse outcomes compared to non-CS patients. Strategies to reduce FMC-to-device times for STEMI patients specifically presenting with CS are needed.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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