Inhaled Epoprostenol for Pulmonary Hypertension Treatment in Neonates: A 12-Year Experience
Bibliographic record
Abstract
Background Persistent pulmonary hypertension of the newborn (PPHN) occurs in 10% of neonatal respiratory insufficiency. To selectively reduce pulmonary vascular resistance, several treatments have been tried. Inhaled epoprostenol (iPGI2) has been used for 12 years in our institution for the management of refractory PPHN despite the gaps in the literature to support this use. Objectives The primary objective was to evaluate the efficacy of iPGI2 for PPHN. The secondary objectives were to describe its use in neonates and assess side effects. Study Design This retrospective cohort study included infants < 28 days with PPHN treated with iPGI2 in the neonatal or pediatric intensive care units of our institution between 2004 and 2016. Results We reviewed 43 patient' care episodes (mean gestational age of 36 weeks). This was an extremely ill population with 54% mortality rate. Oxygenation index improved significantly after 12-hour treatment (p = 0.047), with a rebound effect when discontinuing nebulization. By the end of the therapy, the fraction of inspired oxygen had significantly dropped (p = 0.0018). Echocardiographic markers tended to normalize during treatment. No potential side effects were reported. Conclusion In these sick newborns, we observed an improvement in PPHN under iPGI2 without significant adverse effects. To our knowledge, this is the largest neonatal cohort reported to have received iPGI2 for PPHN.
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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 | 0.002 |
| 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 | 0.000 | 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".