HELMET NON-INVASIVE VENTILATION IN ACUTE RESPIRATORY FAILURE: A SYSTEMATIC REVIEW, CASE SERIES AND PILOT FEASIBILITY TRIAL
Bibliographic record
Abstract
This thesis consisted of three related studies presented as three separate manuscripts (one published in a peer-reviewed journal, one submitted for publication to a peer reviewed journal and one ongoing). The overarching theme of this thesis was to assess helmet non-invasive ventilation (NIV) as a new modality of NIV for the treatment of acute respiratory failure in the intensive care (ICU) setting. Our first manuscript is a published systematic review and meta-analysis that compared helmet NIV to facemask NIV and high flow nasal cannula (HFNC) in adult patients with acute respiratory failure. We performed an extensive search and included 16 randomized control trials (RCTs) and 8 observational studies, the results of which we pooled separately. We assessed certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Pooled data from RCTs suggested that helmet NIV may reduce mortality and intubation when compared to facemask NIV, albeit based on low certainty evidence. Data from observational studies supported this finding but were of even lower certainty. Given the above findings, we concluded that a large randomized control trial (RCT) that compared helmet to facemask NIV for patients with acute respiratory failure (ARF) is needed. Since helmet NIV is a new technology, before embarking on this large trial, we recognized that it was necessary to establish familiarity with the device and conduct a pilot feasibility trial. Manuscript # 2 was a 16 patient case series that characterized introduction of the helmet NIV at 2 centres (one in Canada and one in the United States). All patients who were admitted with ARF over a 7 month time period and for whom the clinical team determined that helmet NIV may be beneficial were enrolled in the trial. The most common reason for helmet NIV usage was pneumonia, especially due to COVID-19. Most patients tolerated helmet NIV and no adverse events were recorded with the device. This case series has been submitted to a peer reviewed journal for review. After the case series had concluded and staff were introduced to helmet NIV, we initiated an ongoing single centre pilot feasibility RCT comparing helmet to facemask NIV in patients with ARF (manuscript #3). This pragmatic, unblinded, concealed allocation, parallel-group trial is currently being implementedat the Juravinski Hospital and will include 50 patients. The pilot trial will examine feasibility outcomes includingrecruitment rate and protocol adherence rate. While still ongoing, current feasibility goals have been met, although we noted a high crossover rate and decreased enrollment during the peak of the most recent COVID wave. These considerations have necessitated modifications to the protocol including planned enrollment of a second site, increased site education and new incentives for healthcare workers to enroll patients in the study. Funding for this trial was provided by securing 2 peer reviewed grants totalling $75,000. Together, all 3 manuscripts represent an ongoing, cohesive research program aimed at assessing implementation and adoption of new technology in the ICU. Further projects examining the cost-effectiveness of this new technology, qualitative patients and healthcare worker experiences, as well as the aforementioned large multicentre RCT are planned moving forward.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".