Variable Effects of the COVID-19 Pandemic on Reported Adverse Events for Arrhythmic Activity and 30-Day Fills For Anti-Arrhythmic Agents
Bibliographic record
Abstract
COVID-19 had large impacts on the lives of many individuals with rhythmical cardiac problems.With limitations that COVID-19 had on the ability to track medical based data, a controversy on the effect of COVID-19 on the incidence of arrhythmic activity has been apparent.To determine the effect that pandemic had on the incidence arrhythmic activity, we studied adverse event trends of 4 anti-arrhythmic agents -propafenone, sotalol, amiodarone, and dronedarone.Extracting data from the FDA FAERS database, we concluded significant (p<0.05)decreases for propafenone (55.8% decrease) and amiodarone and dronedarone (16.9% decrease) from 2020 to 2021 as well as an insignificant decrease for sotalol (30% decrease).In response to suggestive decreasing trends, we proceeded with a cost-analysis to explore possible reasons behind sudden decreases in reported adverse events.Using the Medicare Part D database, data for costs between generic vs. brand-name for previously examined antiarrhythmic agents as well as associations between 30-day fills and adverse event reports was examined.For each of the agents, the brand-name agents had a significantly higher cost than the brand-name agents.Associations between adverse events and 30-day fills were demonstrated through R2 values, which resulted in values of 0.238 for propafenone, 0.796 for sotalol, and 0.651 for amiodarone and dronedarone.We concluded that cost did not have a significant effect on adverse event reporting.Overall, we recommend further study into the potential underdiagnosis and undertreatment of cardiac arrhythmias during the COVID-19 pandemic, which can have dangerous implications on the long-term mortality of individuals without the appropriate materials.To accomplish this, it is necessary to analyze 2022 adverse event data for various anti-arrhythmic agents to observe new or continuing trends.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".