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Record W2908248722 · doi:10.1002/clc.23146

Association of acute myocardial infarction cardiac arrest patient volume and in‐hospital mortality in the United States: Insights from the National Cardiovascular Data Registry Acute Coronary Treatment And Intervention Outcomes Network Registry

2018· article· en· W2908248722 on OpenAlexaff
Michael C. Kontos, Christopher B. Fordyce, Anita Y. Chen, Karen Chiswell, Jonathan Enriquez, James A. de Lemos, Matthew T. Roe

Bibliographic record

VenueClinical Cardiology · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMyocardial infarctionInternal medicineOdds ratioConfidence intervalCardiologyEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known about how differences in out of hospital cardiac arrest patient volume affect in-hospital myocardial infarction (MI) mortality. HYPOTHESIS: Hospitals accepting cardiac arrest transfers will have increased hospital MI mortality. METHODS: MI patients (ST elevation MI [STEMI] and non-ST elevation MI [NSTEMI]) in the Acute Coronary Treatment Intervention Outcomes Network Registry were included. Hospital variation of cardiac arrest and temporal trend of the proportion of cardiac arrest MI patients were explored. Hospitals were divided into tertiles based on the proportion of cardiac arrest MI patients, and association between in-hospital mortality and hospital tertiles of cardiac arrest was compared using logistic regression adjusting for case mix. RESULTS: A total of 252 882 patients from 224 hospitals were included, of whom 9682 (3.8%) had cardiac arrest (1.6% of NSTEMI and 7.5% of STEMI patients). The proportion of MI patients who had cardiac arrest admitted to each hospital was relatively low (median 3.7% [25th, 75th percentiles: 3.0%, 4.5%]).with a range of 4.2% to 12.4% in the high-volume tertiles. Unadjusted in-hospital mortality increased with tertile: low 3.8%, intermediate 4.6%, and high 4.7% (P < 0.001); this was no longer significantly different after adjustment (intermediate vs high tertile odds ratio (OR) = 1.02; 95% confidence interval [0.90-1.16], low vs high tertile OR = 0.93 [0.83, 1.05]). CONCLUSIONS: The proportion of MI patients who have cardiac arrest is low. In-hospital mortality among all MI patients did not differ significantly between hospitals that had increased proportions of cardiac arrest MI patients. For most hospitals, overall MI mortality is unlikely to be adversely affected by treating cardiac arrest patients with MI.

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)0.004
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.349
Teacher spread0.308 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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