Impact of Stewardship on Inhaled Nitric Oxide Utilization in a Neonatal ICU
Bibliographic record
Abstract
OBJECTIVES: Inhaled nitric oxide (iNO) remains the "gold standard" therapy for hypoxemic respiratory failure in newborns. Despite good quality evidence to guide iNO use in this population, we observed considerable practice variation, particularly in timing and rate of weaning. To promote evidence-based practice, we launched an iNO stewardship program in April 2013. Our objective was to determine whether iNO stewardship led to changes in iNO utilization and weaning. METHODS: We conducted a quality improvement project in an outborn quaternary NICU, targeting improved iNO guideline compliance. We compared patterns of iNO utilization between 2 cohorts: prestewardship (April 2011-March 2013; retrospective data collection) and poststewardship (April 2013-March 2015; prospective data collection). RESULTS: Eighty-seven neonates received 88 courses of iNO in the 2 years prestewardship, and 64 neonates received 64 courses of iNO in the 2 years poststewardship. There were no significant differences (P > .05) in patient demographics, in the proportion of patients receiving iNO "off-label," in proportion initiated at the referring hospital, or in outcomes (death or extracorporeal membrane oxygenation). There were significant (P < .05) reductions in median total hours on iNO per patient (47 vs 20; P < .001), in iNO hours per patient from maximum dose to initial wean (28 vs 9; P < .01), and in hours from initial wean to discontinuation (14 vs 8; P < .05). CONCLUSIONS: The introduction of iNO stewardship was associated with improved adherence to evidence-based guidelines and an overall reduction in total and per-patient iNO use.
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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 | 0.008 | 0.052 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".