Respiratory Support Techniques for COVID-19-Related ARDS in a Sub-Saharan African Country
Bibliographic record
Abstract
Background Limited data from low-income countries report on respiratory support techniques in COVID-19-associated ARDS. Research Question Which respiratory support techniques are used in patients with COVID-19-associated ARDS in Uganda? Study Design and Methods A multicenter, prospective, observational study was conducted at 13 Ugandan hospitals during the pandemic and included adults with COVID-19-associated ARDS. Patient characteristics, clinical and laboratory data, initial and most advanced respiratory support techniques, and 28-day mortality were recorded. Standard tests, log-rank tests, and logistic regression analyses were used for statistical analyses. Results Four hundred ninety-nine patients with COVID-19-associated ARDS (mild, n = 137; moderate, n = 247; and severe, n = 115) were included (ICU admission, 38.9%). Standard oxygen therapy (SOX), high-flow nasal oxygen (HFNO), CPAP, noninvasive ventilation (NIV), and invasive mechanical ventilation (IMV) was used as the first-line (most advanced) respiratory support technique in 37.3% (35.3%), 10% (9.4%), 11.6% (4.8%), 23.4% (14.4%), and 17.6% (36.6%) of patients, respectively. The first-line respiratory support technique was escalated in 19.8% of patients. Twenty-eight-day mortality was 51.9% (mild ARDS, 13.1%; moderate ARDS, 62.3%; severe ARDS, 75.7%; P < .001) and was associated with respiratory support techniques as follows: SOX, 19.9%; HFNO, 31.9%; CPAP, 58.3%; NIV 61.1%; and IMV, 83.9% ( P < .001). Proning was used in 79 patients (15.8%; 59 of 79 awake) and was associated with lower mortality (40.5% vs 54%; P = .03). The oxygen saturation to Fio 2 ratio (OR, 0.99; 95% CI, 0.98-0.99; P < . 001) and respiratory rate (OR, 1.07; 95% CI, 1.03-1.12; P = . 002) at admission and NIV (OR, 6.31; 95% CI, 2.29-17.37; P < . 001) or IMV (OR, 8.08; 95% CI, 3.52-18.57; P < . 001) use were independent risk factors for death. Interpretation SOX, HFNO, CPAP, NIV, and IMV were used as respiratory support techniques in patients with COVID-19-associated ARDS in Uganda. Although these data are observational, they suggest that the use of SOX and HFNO therapy as well as awake proning are associated with a lower mortality resulting from COVID-19-associated ARDS in a resource-limited setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".