Fetal Echocardiography From 10 to 15 Weeks of Gestation—Reliability, Genetic Associations, and Outcomes
Bibliographic record
Abstract
Introduction There is increasing demand for accurate early fetal cardiac disease (FCD). We assessed the accuracy of early fetal echo (EFE) conducted in our high-volume fetal cardiac program and reviewed the spectrum of FCD, associated genetic anomalies, and outcomes encountered. Methods We identified all EFEs performed from 10 +0 to 15 +6 weeks of gestation from 2009 to 2021. We compared findings at EFE to fetal echo at ≥18 +0 weeks or autopsy and documented genetic testing results for all FCD cases. For those with discrepancy between EFE and later exam, the discrepancy impact was reviewed. A score was used to quantify the anatomy assessed. Results A total of 1,662 EFEs were performed in 1,387 pregnancies; all but 41 were considered diagnostic. Fetal cardiac disease was diagnosed at EFE in 130, including 101 major, 12 minor, 13 other FCD, and 4 arrhythmias. In 14/130 with FCD, endovaginal imaging was undertaken, which increased the score (1.6/9 vs 3.5/9; P = .049). Thirty-five of 130 had repeat EFE, which increased the score (5.2/9 vs 7.4/9, P < .0001). Fetal loss occurred before confirmation of FCD in 16 and termination in 64, and 11 were lost to follow-up. Thirty-nine had autopsy and/or fetal echo ≥18 +0 : 35 had FCD confirmed, and 4 had resolution. Of the 35 confirmed FCD, 27 had no, 7 minor, and 1 major change. Of 1,489 with normal EFE, later echo demonstrated FCD in 14: 3 major and 11 minor. In 16, FCD evolved, including 4 arrhythmias and 12 with progressive FCD. Sensitivity, specificity, and positive and negative predictive values of EFE in identifying major FCD were 92.9%, 100%, 100%, and 99.7%, respectively. In cases with FCD, 85.4% had genetic testing, of whom 71% (60.8% of the total) had abnormal results. Conclusions In our experience, EFE permits accurate diagnosis and exclusion of most FCD. Endovaginal imaging and repeat EFE studies improved the ability to visualize structures adequately.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".