11. Prognostic value of B-type natriuretic peptide in predicting adverse cardiac and pregnancy outcomes in pregnant women with heart disease: A systematic review
Bibliographic record
Abstract
Objectives: B-natriuretic peptide (BNP) is widely used as a prognostic marker in non-pregnant patients, however there is no consensus on its utility in informing prognosis in pregnant women with heart disease.This systematic review aims to combine current studies which measure BNP levels to predict adverse outcome in pregnant women with heart disease.Design: This study forms a systematic review.Meta-analysis was not possible as outcomes were reported heterogeneously.Methods: Retrospective and prospective cohort studies were included.Those with less than 10 participants were excluded.We required that studies measured BNP or NT-pro-BNP levels during pregnancy in women with heart disease and compared BNP levels to maternal and/or fetal complications.Medline and Embase were searched from inception to December 2019.Risk of bias was assessed using the Newcastle-Ottawa scale.Results: Seven studies were included, involving 787 women.In women who had an adverse cardiac event during pregnancy or in the postnatal period the median BNP ranged between 185-354pg/mL and for women who had no adverse event, median BNP ranged between 73-100pg/mL.One study observed the mean BNP in women who had a small for gestational age neonate as 160 +-183pg/mL, compared to women with neonates with normal growth as 77.1 +-66.5pg/mL,p¼0.003. Conclusion:The available evidence suggests that women with a higher BNP are more likely to have an adverse cardiac event or a small for gestational age neonate.Further studies with robust methodology are needed to clarify the prognostic value of BNP in pregnant women with heart disease.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".