Abstract 10590: Trends in Major Adverse Cardiac Events During the Covid-19 Pandemic in Alberta, Canada
Bibliographic record
Abstract
Introduction: The coronavirus disease 2019 (COVID-19) pandemic has impacted cardiovascular (CV) outcomes and care globally, although to date, limited data exist on the Canadian experience. This study aimed to describe trends in major adverse cardiovascular events (MACE)/procedures during the COVID-19 pandemic in the Province of Alberta, Canada. Methods: A repeated cross-sectional study design compared MACE from inpatient and emergency department visits among Albertan adults between March 15, 2019, and September 14, 2020. The Alberta population was 4.4 million in 2020 and all residents are part of a single payer healthcare system. The percent change between each MACE reported in the control (2019) and COVID-19 restriction periods (2020) were calculated. MACE were defined individually and as a primary composite outcome (CV death, acute coronary syndrome [ACS], ischemic stroke, or coronary revascularization procedures). These preliminary data are part of an ongoing study, measuring outcomes throughout the first year of the pandemic. Results: Compared to March-June 2019, primary MACE during the initial COVID-19 restriction period (March-June 2020) decreased by 15.6% (n=739 patients), and by 7.3% (n=342 patients) during June-September 2020, when COVID-19 restrictions were eased. Most individual MACE followed similar patterns with reduced reported events/procedures during the initial restriction period and an increase towards previous rates thereafter (Figure 1): CV death (17.7% vs 10.2%), ACS (19.1% vs 8.4%), coronary revascularization (17.0% vs 9.2%). The exception was ischemic stroke, which was not notably impacted by pandemic restrictions. Conclusions: Declines in MACE/procedures during the COVID-19 pandemic are suggestive of a substantial gap in patient management and healthcare-seeking behaviour that may have negative downstream implications. Ongoing analyses will further explore reported MACE up to one year after the pandemic began.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".