Predictors of failure with high-flow nasal oxygen therapy in COVID-19 patients with acute respiratory failure: a multicenter observational study
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Bibliographic record
Abstract
PURPOSE: We aimed to describe the use of high-flow nasal oxygen (HFNO) in patients with COVID-19 acute respiratory failure and factors associated with a shift to invasive mechanical ventilation. METHODS: This is a multicenter, observational study from a prospectively collected database of consecutive COVID-19 patients admitted to 36 Spanish and Andorran intensive care units (ICUs) who received HFNO on ICU admission during a 22-week period (March 12-August 13, 2020). Outcomes of interest were factors on the day of ICU admission associated with the need for endotracheal intubation. We used multivariable logistic regression and mixed effects models. A predictive model for endotracheal intubation in patients treated with HFNO was derived and internally validated. RESULTS: From a total of 259 patients initially treated with HFNO, 140 patients (54%) required invasive mechanical ventilation. Baseline non-respiratory Sequential Organ Failure Assessment (SOFA) score [odds ratio (OR) 1.78; 95% confidence interval (CI) 1.41-2.35], and the ROX index calculated as the ratio of partial pressure of arterial oxygen to inspired oxygen fraction divided by respiratory rate (OR 0.53; 95% CI: 0.37-0.72), and pH (OR 0.47; 95% CI: 0.24-0.86) were associated with intubation. Hospital site explained 1% of the variability in the likelihood of intubation after initial treatment with HFNO. A predictive model including non-respiratory SOFA score and the ROX index showed excellent performance (AUC 0.88, 95% CI 0.80-0.96). CONCLUSIONS: Among adult critically ill patients with COVID-19 initially treated with HFNO, the SOFA score and the ROX index may help to identify patients with higher likelihood of intubation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it