Utility of the ROX Index in Predicting Intubation for Patients With COVID-19–Related Hypoxemic Respiratory Failure Receiving High-Flow Nasal Therapy: Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Use of high flow nasal therapy (HFNT) to treat COVID-19 pneumonia has been greatly debated around the world due to concern for increased healthcare worker transmission and delays in invasive mechanical Ventilation (IMV). Methods A retrospective analysis of consecutive patients admitted to Temple University Hospital in Philadelphia, Pennsylvania, from March 10, 2020, to May 17, 2020 with moderate to severe respiratory failure treated with High Flow nasal therapy (HFNT). HFNT patients were divided into two groups: HFNT only and HFNT progressed to IMV. The primary outcome was the ability of the ROX index to predict the need of IMV. Results Of the 837 patients with COVID-19, 129 met inclusion criteria. The mean age was 60.8 (±13.6) years, BMI 32.6 (±8), 58 (45 %) were female, 72 (55.8%) were African American, 40 (31%) Hispanic. 48 (37.2%) were smokers. Mean time to intubation was 2.5 days (± 3.3). ROX index of less than 5 at HFNT initiation was predictive of progression to IMV (OR = 2.137, p = 0,052). Any decrease in ROX index after HFNT initiation was predictive of intubation (OR= 14.67, p <0.0001). Δ ROX (<=0 versus >0), peak D-dimer >4000 and admission GFR < 60 ml/min were very strongly predictive of need for IMV (ROC = 0.86, p=). Mortality was 11.2% in HFNT only group versus 47.5% in the HFNT progressed to IMV group (p,0.0001). Mortality and need for pulmonary vasodilators were higher in the HNFT progressed to IMV group. Conclusion ROX index is a valuable, noninvasive tool to evaluate patients with moderate to severe hypoxemic respiratory failure in COVID-19 treated with HFNT. ROX helps predicts need for IMV and thus limiting morbidity and mortality associated with IMV.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".