Use of neurally adjusted ventilatory assist (NAVA) in a patient with severe SARS-CoV-2 pneumonia: A case report
Bibliographic record
Abstract
Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia may necessitate intubation and prolonged mechanical ventilation. Early in the course of mechanical ventilation neuromuscular blocking agents may be used to allow synchronous lung protective ventilation. However, patients with SARS-CoV-2 pneumonia tend to have an intense respiratory drive resulting in patient-ventilator asynchrony when neuromuscular blocking agents are discontinued. Case and Outcomes: A 75-year-old male was admitted to the hospital with SARS-CoV-2 pneumonia requiring invasive mechanical ventilation. By ventilator day 5 the neuromuscular blocking agent had been discontinued, and the patient was markedly asynchronous in the volume control mode despite receiving continuous intravenous sedatives. The ventilator mode was changed to the neurally adjusted ventilatory assist (NAVA) mode. Initially NAVA resulted in improved synchrony and reduced work of breathing. However, a few days later the patient's tidal volume had fallen to <300 mL on NAVA despite increases in the NAVA level. It appeared that the inspiratory phase was prematurely terminating, and the expiratory threshold in NAVA is not adjustable. The ventilator mode was changed to pressure support resulting in an increased tidal volume and reduced respiratory frequency. Conclusion: In patients with SARS-CoV-2 pneumonia and intense respiratory drive, the performance of NAVA may be variable. NAVA may result in hypopnea and tachypnea when compared with pressure support. An assessment of the impact of an adjustable expiratory threshold in NAVA is warranted.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".