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Record W4281976994 · doi:10.1101/2022.05.25.22275586

Noninvasive ventilation strategies for patients with severe or critical COVID-19: A rapid review of clinical outcomes

2022· review· en· W4281976994 on OpenAlexafffund
Shannon Kelly, George A. Wells

Bibliographic record

VenuemedRxiv · 2022
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchUniversity of OttawaWorld Health Organization
KeywordsMedicineRandomized controlled trialMechanical ventilationIntensive care medicineContinuous positive airway pressureIntensive care unitCoronavirus disease 2019 (COVID-19)Ventilation (architecture)Oxygen therapyEmergency medicineAnesthesiaInternal medicineDisease

Abstract

fetched live from OpenAlex

Abstract Objectives To examine whether high flow nasal oxygen (HFNO), continuous positive airway pressure (CPAP), or noninvasive ventilation (NIV) strategies impact mortality, the need for invasive mechanical ventilation (IMV), or hospital and intensive care unit (ICU) length of stay compared to standard oxygen therapy (SOT) or each other in patients with severe or critical COVID-19 with acute hypoxemic respiratory failure. Methods A rapid review of randomized controlled trials (RCTs) identified through published systematic and rapid reviews supplemented with a search of bibliographic databases. RCTs were eligible if they compared HFNO, CPAP, or NIV to SOT or another ventilation strategy. Studies were screened, selected, and extracted by a single reviewer and checked by a second reviewer. We assessed risk of bias of included studies using the Cochrane ‘Risk of bias’ tool and used the grading of recommendations, assessment, development, and evaluation (GRADE) approach to judge the certainty of the evidence for mortality, need for IMV, and hospital and ICU length of stay. We sought RCT evidence for non-COVID-19 patients with acute hypoxemic respiratory failure and acute respiratory distress to inform additional comparisons and to supplement the available data for COVID-19. Results A total of 5 RCTs comparing ventilation strategies in patients with severe or critical COVID-19 were included. Patient and study characteristics were extracted and evidence and certainty of evidence assessments were completed for comparisons of HFNO and CPAP to standard oxygen therapy and NIV and CPAP to HFNO. An additional 22 RCTs of non-COVID-19 patients were also included and considered. Results from meta-analysis suggest reductions in mortality and IMV with HFNO ( RR mortality 0 . 87 (0 . 66-1 . 13), IMV 0 . 89 (0 . 77-1 . 03); low quality evidence ) or CPAP ( RR mortality 0 . 87 (0 . 64-1 . 18) low quality evidence, IMV 0 . 81 (0 . 67-0 . 98) moderate quality evidence) compared to SOT. Helmet NIV may reduce IMV ( RR 0 . 69 (0 . 43-1 . 09)) and CPAP may reduce IMV (RR 0 . 69 (0 . 43-1 . 09)) and hospital (1 . 67 days fewer (5 . 43 fewer-2 . 09 more) or ICU length of stay (1 . 02 days fewer (3 . 97 fewer-1 . 93 more)) compared to HFNO (low quality evidence) . Conclusions This rapid systematic review highlights the available evidence to support the use of noninvasive ventilation strategies including high flow nasal oxygen, noninvasive ventiltaion (e.g., BiPAP), or CPAP in hospitalized patients with severe or critical COVID-19 and acute hypoxemic respiratory failure who do not need emergent intubation. Findings based on moderate to very low certainty evidence suggest that noninvasive ventilation may be considered as an alternative to standard oxygen therapy to reduce hypoxemia and dyspnea. Additional high quality RCTs are warranted to reduce uncertainty and to fill in important knowledge gaps.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.104
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.104
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.013
Bibliometrics0.0150.011
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.216
GPT teacher head0.470
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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