COVID-19 pandemic and trends in new diagnosis of atrial fibrillation: A nationwide analysis of claims data
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is associated with a five-fold increased risk of stroke and a two-fold increased risk of death. We aimed to quantify changes in new diagnoses of AF following the onset of the COVID-19 pandemic. Investigating changes in new diagnoses of AF is of relevance because delayed diagnosis interferes with timely treatment to prevent stroke, heart failure, and death. METHODS: Using De-identified Optum's Clinformatics® Data Mart, we identified 19,500,401 beneficiaries continuously enrolled for 12 months in 2016-Q3 2020 with no history of AF. The primary outcome was new AF diagnoses per 30-day interval. Secondary outcomes included AF diagnosis in the inpatient setting, AF diagnosis in the outpatient setting, and ischemic stroke as initial manifestation of AF. We constructed seasonal autoregressive integrated moving average models to quantify changes in new AF diagnoses after the onset of the COVID-19 pandemic (3/11/2020, date of pandemic declaration). We tested whether changes in the new AF diagnoses differed by race and ethnicity. RESULTS: The average age of study participants was 51.0±18.5 years, and 52% of the sample was female. During the study period, 2.7% of the study sample had newly-diagnosed AF. New AF diagnoses decreased by 35% (95% CI, 21%-48%) after the onset of the COVID-19 pandemic, from 1.14 per 1000 individuals (95% CI, 1.05-1.24) to 0.74 per 1000 (95% CI, 0.64 to 0.83, p-value<0.001). New AF diagnoses decreased by 37% (95% CI, 13%- 55%) in the outpatient setting and by 29% (95% CI, 14%-43%) in the inpatient setting. The decrease in new AF diagnoses was similar across racial and ethnic subgroups. CONCLUSION: In a nationwide cohort of 19.5 million individuals, new diagnoses of AF decreased substantially following the onset of the COVID-19 pandemic. Our findings evidence pandemic disruptions in access to care for AF, which are concerning because delayed diagnosis interferes with timely treatment to prevent complications.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".