Impact of COVID-19 Pandemic on Sex and Racial Disparities in Chest Pain Presentation and Management Through the Emergency Department
Bibliographic record
Abstract
Background: Sex and racial disparities in the presentation and management of chest pain persist, however, the impact of coronavirus disease 2019 (COVID-19) on these disparities have not been studied. We sought to determine whether the COVID-19 pandemic contributed to pre-existing sex and racial disparities in the presentation, management, and outcomes of patients presenting to the emergency department (ED) with chest pain. Methods: We conducted an observational cohort study with retrospective data collection of patients between January 1, 2016, and May 1, 2022. This was a single study conducted at a quaternary academic medical center of all patients who presented to the ED with a complaint of chest pain or chest pain equivalent symptoms. Patient were further segregated into different groups based on sex (male, female), race, ethnicity (Asian, Black, Hispanic, White, and other), and age (18 - 40, 41 - 65, > 65). We compared diagnostic evaluations, treatment decisions, and outcomes during prespecified time points before, during, and after the COVID-19 pandemic. Results: This study included 95,764 chest pain encounters. Total chest pain presentations to the ED fell about 38% during the early pandemic months. Females presented significantly less than males during initial COVID-19 (48% vs. 52%, P < 0.001) and Asian females were least likely to present. There was an increase in the total number of troponins and echocardiograms ordered during peak COVID-19 across both sexes, but females were still less likely to have these tests ordered across all timepoints. The number of coronary angiograms did not increase during peak COVID-19, and females were less likely to undergo coronary angiogram during all timepoints. Finally, females with chest pain were less likely to be diagnosed with acute myocardial infarction (AMI) during all timepoints, while in-hospital deaths were similar between males and females during all timepoints. Conclusions: During COVID-19, females, especially Asian females, were less likely to present to the ED for chest pain. Non-White patients were less likely to present to the ED compared to White patients prior to and during the pandemic. Disparities in management and outcomes of chest pain encounters remained similar to pre-COVID-19, with females receiving less cardiac workup and AMI diagnoses than males, but in-hospital mortality remaining similar between groups and timepoints.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".