Impact of COVID‐19 on Prenatal Diagnosis and Surgical Outcomes of Congenital Heart Disease: Fetal Heart Society and Society of Thoracic Surgeons Collaborative Study
Bibliographic record
Abstract
Background Fetal echocardiography is the mainstay of prenatal diagnosis of congenital heart disease. The COVID‐19 pandemic led to shifts in triage of prenatal services. Our objective was to evaluate the impact of COVID‐19 restrictions on prenatal diagnosis, surgical outcomes, and disparities in neonatal critical congenital heart disease (CCHD) management in the United States during the pandemic's first year. Methods and Results A multi‐institutional retrospective cohort study compared neonatal CCHD outcomes (requiring surgery within 60 days of birth) 1 year prior (prepandemic era) and during the peak pandemic era, supplemented by a Fetal Heart Society survey assessing regional practice changes. Data on prenatal diagnosis, demographics, outcomes, and 2020 state Area Deprivation Index were analyzed using Wilcoxon rank sum and χ 2 tests. The survey, completed by 72 fetal cardiologists from 9 US census regions, showed 75% of institutions implemented restrictions by March 2020, affecting triage, referrals, and number of prenatal cardiology visits. Compared with CCHD neonates born prepandemic (n=4637), those born during the pandemic (n=1806) had a higher proportion of prenatal diagnosis (66% versus 63%, P <0.05). There were no significant differences in complications or mortality, but pandemic‐era neonates had longer hospital stays. During the pandemic, CCHD neonates had a more disadvantaged Area Deprivation Index and had surgery at hospitals located in more advantaged regions. Conclusions Although pandemic‐driven care delivery adjustments affected perinatal cardiology referrals and triage, prenatal diagnosis, perioperative outcomes, and survival remained robust. The management of CCHD demonstrates health care resilience, maintaining core prenatal and perioperative care. Regional variations highlight the need for targeted strategies to address disparities during health care crises.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".