Sex-related electrocardiographic differences in patients with different types of atrial fibrillation: Results from the SWISS-AF study
Bibliographic record
Abstract
BACKGROUND: Sex-related electrocardiographic differences are a well-known phenomenon, but not their expression in patients with atrial fibrillation (AF). In this study we aim to assess the presence of significant sex-related differences in ECG features, with particular attention to P-wave parameters, of a large cohort of patients affected by different types of AF. METHODS: A 5-min resting 16-lead ECG was evaluated for 1119 AF patients in sinus rhythm. The durations of the main ECG waves and intervals were measured for both atrial and ventricular activity. Moreover, the beat-to-beat P-wave variability was computed for lead II and for the first principal component (PC1) computed across the 16 leads. The percentage of variance explained by PC1 was computed. RESULTS: Males compared to females showed significantly longer RR interval (1.02 ± 0.16 s vs 0.97 ± 0.15 s, p < .001), PQ interval (191 ± 34 ms vs 183 ± 35 ms, p = .008), QRS duration (105 ± 17 ms vs 98 ± 13 ms, p = .021), significantly lower percentage of variance explained by PC1 and P-wave variability. Males with paroxysmal AF compared to females with paroxysmal AF had significantly longer RR interval (1.01 ± 0.17 s vs 0.96 ± 0.14 s, p < .001), shorter QTc (388 ± 27 ms vs 402 ± 27 ms, p < .001), lower P-wave variability in PC1. Males with persistent AF compared to females with persistent AF had significantly shorter QTc interval (396 ± 30 ms vs 407 ± 26 ms, p = .019), longer PQ interval (194 ± 35 ms vs 182 ± 30 ms, p = .037), higher V1 terminal force (2.1 ± 1.2 mV*ms vs 1.8 ± 1 mV*ms, p = .007), lower percentage of variance explained by PC1. CONCLUSIONS: AF patients present with several sex-related ECG differences. Consequently, sex should be taken into account when developing ECG algorithms identifying patients at risk for AF progression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".