A physiologic Comparison of Continuous Neurally Adjusted Ventilation (NeuroPAP) Versus Neurally‐Adjusted Ventilatory Assist (NAVA) in Infants With Respiratory Failure
Bibliographic record
Abstract
ABSTRACT Objectives And Hypothesis Tonic diaphragmatic activity is common during non‐invasive ventilation (NIV), suggesting efforts to increase end‐expiratory lung volume. We assessed the feasibility and physiological impact of NeuroPAP, a novel NIV mode continuously adjusting the delivered positive pressure proportionally to diaphragm electric activity (Edi) during both inspiration and expiration, in infants with respiratory failure. We hypothesized NeuroPAP would enable dynamic control of end‐expiratory pressures (PEEP). Methodology This prospective crossover study enrolled premature neonates (25–34 weeks) and infants with bronchiolitis supported by NIV‐NAVA. Subjects underwent three ventilation phases: NIV‐NAVA, NeuroPAP, and repeat NIV‐NAVA. Ventilation pressures, Edi, cardio‐respiratory events, neural breathing patterns, and systemic and cerebral oxygenation were assessed. Results A total of 15 infants with bronchiolitis and 8 premature neonates were included. The overall median PEEP was unchanged between modes, but PEEP was actively adjusted, with increased breath‐to‐breath variability of PEEP in NeuroPAP ( p < 0.001). Compared to pre‐study settings, individual PEEP increased in NeuroPAP in 7, decreased in 9, and was unchanged in 7 patients. In NeuroPAP, the breathing pattern was phasic 74% of the time and tonic 16% of the time, compared to 61% ( p = 0.31) and 23% ( p = 0.36) in NIV‐NAVA. Respiratory rate was lower in NeuroPAP in the neonates ( p = 0.006). The estimated PaO 2 /FiO 2 ratio was higher in the post‐NeuroPAP NIV‐NAVA period in the bronchiolitis group ( p = 0.006). Edi, heart rate, cerebral NIRS, or cardio‐respiratory events were unchanged. Conclusion In infants with respiratory failure, NeuroPAP allowed dynamic control and personalization of PEEP. The clinical impact of this warrants further evaluation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".