Real-world use of inhaled nitric oxide therapy in patients with COVID-19 and mild-to-moderate acute respiratory distress syndrome
Bibliographic record
Abstract
Background: Inhaled nitric oxide (iNO) has been studied in patients with severe acute respiratory distress syndrome (ARDS) due to COVID-19 when it may be too late to impact disease course.This article aims to describe real-world iNO use and outcomes in patients with COVID-19 with mild-to-moderate ARDS in the United States.Methods: This was a retrospective medical chart review study that included patients who were ≥18 years old, hospitalized for COVID-19, met the Berlin ARDS definition, received iNO for ≥24 hours continuously during hospitalization, and had a partial pressure of oxygen (PaO 2 )/fraction of inspired oxygen (FiO 2 ) ratio (P/F ratio) of >100 to ≤300 mmHg at iNO initiation.Outcomes included oxygenation parameters, physician-rated Clinical Global Impression-Improvement (CGI-I) scale scores, and adverse events.Response to iNO was defined as >20% improvement in P/F ratio.Results: Thirty-seven patients at six sites were included.A P/F ratio of ≤100 was the most common reason for exclusion (n=146; 83% of excluded patients).The mean P/F ratio (SD) increased from 136.7 (34.4) at baseline to 140.3 (53.2) at 48 hours and 151.8 (50.0) at 72 hours after iNO initiation.The response rate was 62% (n=23).During hospitalization, no patient experienced adverse events, including methemoglobinaemia, airway injury, or worsening pulmonary oedema associated with iNO.At discharge, 54.0% (n=20) of patients improved or remained stable according to the CGI-I. Conclusion:In patients hospitalized with COVID-19 and mildto-moderate ARDS, iNO was associated with improvement in the P/F ratio with no reported toxicity.This study provides additional evidence supporting a favourable benefit-risk profile for iNO in the treatment of mild-to-moderate ARDS in patients with COVID-19 infection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".