Neurally Adjusted Ventilatory Assist After Pediatric Cardiac Surgery: Clinical Experience and Impact on Ventilation Pressures
Bibliographic record
Abstract
BACKGROUND: After pediatric cardiac surgery, ventilation with high airway pressures can be detrimental to right ventricular function and pulmonary blood flow. Neurally adjusted ventilatory assist (NAVA) improves patient–ventilator interactions, helping maintain spontaneous ventilation. This study reports our experience with the use of NAVA in children after a cardiac surgery. We hypothesize that using NAVA in this population is feasible and allows for lower ventilation pressures. METHODS: We retrospectively studied all children ventilated with NAVA (invasively or noninvasively) after undergoing cardiac surgery between January 2013 and May 2015 in our pediatric intensive care unit. The number and duration of NAVA episodes were described. For the first period of invasive NAVA in each subject, detailed clinical and ventilator data in the 4 h before and after the start of NAVA were extracted. RESULTS: 33 postoperative courses were included in 28 subjects with a median age of 3 [interquartile range (IQR) 1–12] months. NAVA was used invasively in 27 courses for a total duration of 87 (IQR 15–334) h per course. Peak inspiratory pressures and mean airway pressures decreased significantly after the start of NAVA (mean differences of 5.8 cm H 2 O (95% CI 4.1–7.5) and 2.0 cm H 2 O (95% CI 1.2–2.8), respectively, P < .001 for both). There was no significant difference in vital signs or blood gas values. NAVA was used noninvasively in 14 subjects, over 79 (IQR 25–137) h. CONCLUSIONS: NAVA could be used in pediatric subjects after cardiac surgery. The significant decrease in airway pressures observed after transition to NAVA could have a beneficial impact in this specific population, which should be investigated in future interventional studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".