Prenatal Detection of Wolff-Parkinson-White Syndrome Using the Atrioventricular Interval on Fetal Echocardiogram
Bibliographic record
Abstract
Background Fetal Doppler echocardiography has been widely used for the detection and characterization of fetal tachyarrhythmias. Doppler interrogation of the superior vena cava and the aorta (SVC-Ao) is used to determine the electrophysiological etiology of arrhythmias. The present study aims to investigate if the SVC-Ao technique in fetal echocardiograms could identify fetuses with Wolff-Parkinson-White (WPW) syndrome. Methods We retrospectively searched for consecutive fetal echocardiograms with evidence of tachyarrhythmias performed at the CHU Sainte-Justine from January 2000 to July 2021. The primary outcome was defined as the presence of pre-excitation on postnatal electrocardiogram (ECG) in the context of a prenatal tachyarrhythmia and the identification of a short atrioventricular (AV) interval. Results From a cohort of 69 patients presenting with fetal tachyarrhythmia diagnosed by echocardiography, AV intervals were measured in 9 fetuses (13%) that showed WPW on the postnatal surface ECG. The AV interval measured using fetal echocardiography showed a median of 107 ms (interquartile range: 104-116 ms), representing a z -score of –1.27 (–2.01 to –0.56). Six fetuses (67%) had repeated AV intervals ≤–2 standard deviation. Three (33%) had WPW on postnatal surface ECG but did not have short AV intervals on fetal echocardiograms, representing a false negative for the diagnostic yield of the technique. Conclusions Doppler echocardiographic AV interval measurements in fetuses with arrhythmias allow identification of prenatal WPW. The early diagnosis of WPW offers the possibility of a more comprehensive postnatal management plan, including the neonatology and cardiology teams, as well as preparing in advance for specific antiarrhythmic drug therapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".