Sex differences in presentation of atrial fibrillation: Findings from 30-day ambulatory monitoring in real-world practice
Bibliographic record
Abstract
Background: Women are less likely to receive oral anticoagulation or ablation for treatment of atrial fibrillation (AF). Identification of sex differences in arrhythmia characteristics and symptoms may lead to a better understanding of potential reasons for these differences. Objectives: To determine sex differences in AF with respect to heart rate, duration, burden, and symptoms in patients undergoing mobile cardiac telemetry (MCT) monitoring. Methods: All patients who registered for ≤30-day MCT using PocketECG (MediLynx) in the USA in 2017 were included (n = 27,512, 58 % women). PocketECG records and transmits a three-lead ambulatory electrocardiogram (ECG) with real-time beat-to-beat analysis. Sex-related differences were analyzed with Chi2 and Spearmans rho. Results: Fewer women than men were diagnosed with AF lasting ≥30s (13.7 % versus [vs] 19.0 %, p < 0.001). AF burden was lower in women in all age groups <90 years (all p < 0.01). Women were older at the time of AF diagnosis (median 76 vs 73 years, p < 0.001), had faster heart rate during AF (mean: 104.7 ± 26.0 vs 96.7 ± 26.7 bpm, p < 0.001), and shorter AF duration (mean: 96.2 ± 176.0 vs 121.6 ± 189.9 min, p < 0.001). There was a non-significant trend toward more symptoms (such as dizziness, racing heart, fatigue, or palpitations) during AF in women compared to men (46.5 % vs 43.7 %, p = 0.062). Conclusions: AF was less prevalent and occurred at lower burdens in women than men in each age strata. Despite faster heart rates in AF in women, there were no significant sex differences in reported symptoms during AF. Sex differences in therapy cannot be explained by differences in symptoms or rates in AF. Condensed abstract: Real-world data on sex differences in AF using a 30-day MCT monitoring device remain scarce. We aim to determine the sex differences in AF with respect to prevalence, burden, heart rate, and symptom in patients undergoing ≤30-day MCT monitoring. Our data analysis suggests that fewer women than men had AF, women were older at diagnosis of AF, and women with AF had higher mean heart rate, shorter mean AF duration, and lower mean AF burden than men. Further studies are needed to examine reasons for sex differences, specifically in relation to AF therapy and its impact on clinical outcomes.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".