How to deliver aerosolized medications through high flow nasal cannula safely and effectively in the era of COVID-19 and beyond: A narrative review
Bibliographic record
Abstract
BACKGROUND: The treatments of COVID-19 involve some degree of uncertainty. Current evidence also shows mixed findings with regards to bioaerosol dispersion and airborne transmission of COVID-19 during high flow nasal cannula (HFNC) therapy. While coping with this global pandemic created hot debates on the use of HFNC, it is important to bring detached opinions and current evidence to the attention of health care professionals (HCPs) who may need to use HFNC in patients with COVID-19. AIM: The purpose of this paper is to provide a framework on the selection, placement, and use of nebulizers as well as HFNC prongs, gas flow, and delivery technique via HFNC to help clinicians deliver aerosolized medications through HFNC safely and effectively in the era of COVID-19 and beyond. METHODS: We searched PubMed, Medline, CINAHL, and Science Direct to identify studies on aerosol drug delivery through HFNC using the following keywords: ("aerosols," OR "nebulizers") AND ("high flow nasal cannula" OR "high flow oxygen therapy" OR "HFNC") AND ("COVID-19," OR "SARS-CoV-2"). Twenty-eight articles including in vitro studies, randomized clinical trials, scintigraphy studies, review articles, prospective and retrospective research were included in this review. DISCUSSION AND RESULTS: It is not clear if the findings of the previous studies on bacterial contamination could be applied to viral transmission because they do not provide data that could be extrapolated to the risk of SARS-CoV-2 transmission. In the face of the unknown risk with the transmission of COVID-19 during HFNC therapy, the benefits of HFNC must be weighed against the risk of infection to HCPs and other patients. Due to the limited number of ventilators available in hospitals and the confirmed effectiveness of HFNC in treating hypoxemic respiratory failure, HFNC may prevent early intubation, and prolonged intensive care unit stays in patients with COVID-19. CONCLUSION: Clinicians should review the magnitude of this risk based on current evidence and use the suggested strategies of this paper for safe and effective delivery of aerosolized medications through HFNC in the era of COVID-19 and beyond.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".