Helmet CPAP revisited in COVID-19 pneumonia: A case series
Bibliographic record
Abstract
INTRODUCTION: Noninvasive positive pressure ventilation (NIPPV) plays an important role in the management of respiratory failure. However, since the emergence of the COVID-19 pandemic, utilization of traditional face mask NIPPV has been curtailed in part due to risk of aerosolization of respiratory particles and subsequent health care worker exposure. A randomized clinical trial in 2016 reported that an alternative interface, helmet NIPPV, may be more effective than traditional NIPPV at preventing intubation and improving mortality. The helmet NIPPV interface provides positive airway pressure, while also theoretically minimizing aerosolization, making it a feasible modality in management of respiratory failure in COVID-19 patients. CASE AND OUTCOMES: This report describes a single-center experience of a series of three COVID-19 patients with hypoxemic respiratory failure managed with helmet NIPPV. One patient was able to avoid intubation while a second patient was successfully extubated to NIPPV. Ultimately, the third patient was unable to avoid intubation with helmet NIPPV, although the application of the device was late in the progression of the disease. DISCUSSION: NIPPV is an important modality in the management of respiratory failure and has been shown to reduce the need for immediate endotracheal intubation in select populations. For patients unable to tolerate facemask NIPPV, the helmet provides an alternate interface. In COVID-19 patients, the helmet interface may reduce the risk of virus exposure to health care workers from aerosolization. Based on this experience, we recommend that helmet NIPPV can be considered as a feasible option for the management of patients with COVID-19, whether the goal is to prevent immediate intubation or avoid post-extubation respiratory failure. Randomized studies are needed to definitively validate the use of helmet NIPPV in this population. CONCLUSION: Helmet NIPPV is a feasible therapy to manage COVID-19 patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| 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".