Abstract 17894: Clinical and Electrocardiographic Predictors of Occlusive and Obstructive Coronary Artery Disease Following Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Background: Following an out-of-hospital cardiac arrest (OHCA), there are no clear recommendations for early coronary angiography (CA) in patients without ST elevation (STE). Purpose: To examine the association between baseline characteristics, arrest rhythm, post-resuscitation electrocardiogram (ECG) and coronary artery disease (CAD) on CA. Methods: We performed a retrospective analysis of consecutive OHCA patients in the Metro Vancouver area from 2009-2015, using the Resuscitation Outcomes Consortium Cardiac Arrest Registry. Patients who did not survive to admission, or had a non-cardiac cause for OHCA were excluded. ECGs were reviewed for ST-segment change (≥0.1mV in 2 contiguous leads). Presence of CAD was defined as obstructive (any artery ≥70% or left main ≥50% stenosis) or occlusive CAD (any artery 99-100% stenosis). Patients with uninterpretable ECGs or left bundle branch block were excluded. Results: Baseline characteristics, ECG, and CA results for n=604 OHCA patients are reported (Table 1). After multivariate adjustment, the strongest predictor for occlusive CAD was STE (OR 6.38, 95% CI 3.47-11.7). Among non-STE OHCA patients with CA (n=116), male sex, VT/VF, and prior acute coronary syndrome (ACS) were independently associated with occlusive and obstructive CAD, while ST depression was not predictive (Table 2). Conclusions: Male sex, VT/VF, and prior ACS were the strongest predictors of occlusive and obstructive CAD on CA in OHCA patients without STE. In contrast, ST depression on the post-resuscitation ECG had limited value. These results help identify OHCA patients without STE with significant CAD burden, including those who may benefit from early CA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".