Ethnic Differences in Atrial Fibrillation Identified Using Implanted Cardiac Devices
Bibliographic record
Abstract
INTRODUCTION: Atrial fibrillation (AF) is suggested to be less common among black and Asian individuals, which could reflect bias in symptom reporting and access to care. In the Asymptomatic AF and Stroke Evaluation in Pacemaker Patients and the AF Reduction Atrial Pacing Trial (ASSERT), patients with hypertension but no history of AF had AF recorded via an implanted pacemaker or defibrillator, thus allowing both symptomatic and asymptomatic AF incidence to be determined without ascertainment bias. METHODS AND RESULTS: The ASSERT enrolled 2,580 patients in 23 countries in North America, Europe, and Asia. AF was defined as device-recorded AF episodes >190/min, lasting either for >6 minutes or >6 hours in duration. All ethnic groups with >50 patients were enrolled. Ethnic groups studied include Europeans (n = 1900), black Africans (n = 73), Chinese (n = 89), and Japanese (n = 105) patients. Compared to Europeans, black Africans had more risk factors for AF such as heart failure (27.8 vs 14.6%) and diabetes (41.7 vs 26.3%). At 2.5 years follow-up, all 3 non-European races had a lower incidence of AF (8.3%, 10.1%, and 9.5% vs 18.0%, respectively, for AF>6 minutes, P < 0.006). When adjusted for baseline difference, Chinese had a lower incidence of AF > 6 minutes (P < 0.007), and Japanese and black Africans had a lower incidence of AF > 6 hours (P < 0.04 and P = 0.057, respectively). CONCLUSIONS: Black Africans, Chinese, and Japanese had lower incidence of AF compared to Europeans. In the case of black Africans, this is despite an increased prevalence of AF risk factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".