Abstract 15043: Assessment of Cardiovascular Events Among Symptomatic Outpatients With Suspected Coronary Artery Disease Who Presented During COVID-19 Pandemic
Bibliographic record
Abstract
Background: Infection mitigation efforts during the SARS-CoV-2 pandemic highlights a need to optimize evaluation of symptomatic outpatients (pts) with suspected coronary artery disease (CAD). Successful paradigms must have a low incidence of major adverse cardiovascular (CV) events (MACE) and be efficient and scalable across a large population. We describe a strategy implemented during peak interruption of usual clinical services across an integrated healthcare system looking at short-term clinical outcomes. Methods: We evaluated consecutive pts presenting to emergency departments (ED) from March to April 2020 within Intermountain Healthcare. Symptomatic pts were risk stratified using the HEART Score (HS). Those with HS of ≤3 were discharged to “active surveillance”comprised of weekly telephone calls to assess for symptom progression, prompting urgent stress testing. All pts with a HS of ≥4 received cardiac stress PET/SPECT imaging. Physician interpretation guided subsequent pt management. Short-term (minimum 30-day follow-up) clinical outcomes were incident MACE (all-cause mortality, acute MI, stroke, and return ED visits for suspected angina). Results: Those with a HS ≥4 (n=212) had an increased burden of CV risk factors compared to HS ≤3 (n=90) (Table 1). Three of the HS ≤3 group had progressive symptoms leading to urgent stress testing (resulting in low-risk findings and no intervention) and one had a MACE event (NSTE-ACS 10 days post index ED visit). In the HS ≥4 group, 123 tests were read as intermediate or high-risk, of which 59 proceeded to coronary angiography and 43 resulted in revascularization. Two pts with HS ≥4 died from complications related to preexisting heart failure. No additional MACE events occurred in the HS ≥4 group. Conclusion: Among symptomatic pts with suspected CAD who presented for ED evaluation during the SARS-CoV-2 pandemic, the HS combined with active clinical surveillance appears to be a safe and effective management strategy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".