Maternal Cardiac Output and Fetal Doppler Predict Adverse Neonatal Outcomes in Pregnant Women With Heart Disease
Bibliographic record
Abstract
BACKGROUND: The mechanistic basis of the proposed relationship between maternal cardiac output and neonatal complications in pregnant women with heart disease has not been well elucidated. METHODS AND RESULTS: Pregnant women with cardiac disease and healthy pregnant women (controls) were prospectively followed with maternal echocardiography and obstetrical ultrasound scans at baseline, third trimester, and postpartum. Fetal/neonatal complications (death, small-for-gestational-age or low birthweight, prematurity, respiratory distress syndrome, or intraventricular hemorrhage) comprised the primary study outcome. One hundred and twenty-seven women with cardiac disease and 45 healthy controls were enrolled. Neonatal events occurred in 28 pregnancies and were more frequent in the heart disease group as compared with controls (n=26/127 or 21% versus n=2/45 or 4%; P=0.01). Multiple complications in an infant were counted as a single outcome event. Neonatal complications in the heart disease group were small-for-gestational-age/low birthweight (n=18), prematurity (n=14), and intraventricular hemorrhage/respiratory distress syndrome (n=5). Preexisting obstetric risk factors (P=0.003), maternal cardiac output decline from baseline to third trimester (P=0.017), and third trimester umbilical artery Doppler abnormalities (P<0.001) independently predicted neonatal complications and were incorporated into a novel risk index in which 0, 1, and >1 predictor corresponded to expected complication rates of 5%, 30%, and 76%, respectively. CONCLUSIONS: Decline in maternal cardiac output during pregnancy and abnormal umbilical artery Doppler flows independently predict neonatal complications. These findings will enhance the identification of higher risk pregnancies that would benefit from close antenatal surveillance.
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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 | 0.000 | 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 | 0.001 |
| 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".