Prone Positioning of Nonintubated Patients With Coronavirus Disease 2019—A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Several studies have reported prone positioning of nonintubated patients with coronavirus diseases 2019-related hypoxemic respiratory failure. This systematic review and meta-analysis evaluated the impact of prone positioning on oxygenation and clinical outcomes. DESIGN AND SETTING: We searched PubMed, Embase, and the coronavirus diseases 2019 living systematic review from December 1, 2019, to November 9, 2020. SUBJECTS AND INTERVENTION: Studies reporting prone positioning in hypoxemic, nonintubated adult patients with coronavirus diseases 2019 were included. MEASUREMENTS AND MAIN RESULTS: Data on prone positioning location (ICU vs non-ICU), prone positioning dose (total minutes/d), frequency (sessions/d), respiratory supports during prone positioning, relative changes in oxygenation variables (peripheral oxygen saturation, Pao2, and ratio of Pao2 to the Fio2), respiratory rate pre and post prone positioning, intubation rate, and mortality were extracted. Twenty-five observational studies reporting prone positioning in 758 patients were included. There was substantial heterogeneity in prone positioning location, dose and frequency, and respiratory supports provided. Significant improvements were seen in ratio of Pao2 to the Fio2 (mean difference, 39; 95% CI, 25-54), Pao2 (mean difference, 20 mm Hg; 95% CI, 14-25), and peripheral oxygen saturation (mean difference, 4.74%; 95% CI, 3-6%). Respiratory rate decreased post prone positioning (mean difference, -3.2 breaths/min; 95% CI, -4.6 to -1.9). Intubation and mortality rates were 24% (95% CI, 17-32%) and 13% (95% CI, 6-19%), respectively. There was no difference in intubation rate in those receiving prone positioning within and outside ICU (32% [69/214] vs 33% [107/320]; p = 0.84). No major adverse events were recorded in small subset of studies that reported them. CONCLUSIONS: Despite the significant variability in frequency and duration of prone positioning and respiratory supports applied, prone positioning was associated with improvement in oxygenation variables without any reported serious adverse events. The results are limited by a lack of controls and adjustments for confounders. Whether this improvement in oxygenation results in meaningful patient-centered outcomes such as reduced intubation or mortality rates requires testing in well-designed randomized clinical trials.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".