Peak Inspiratory Flow and Inhaler Prescription Strategies in a Specialized COPD Clinical Program
Bibliographic record
Abstract
Background COPD inhaler regimens should be appropriate for the patient's peak inspiratory flow (PIF) and should ideally consist of single or similar device(s). Research Questions In a subspecialized COPD clinic: (1) What is the prevalence of patients with suboptimal PIF and with inappropriate device(s) for measured PIF? (2) Are there patient-related risk factors associated with suboptimal PIF? (3) What is the prevalence of patients with non-single inhaler therapy (SIT)/nonsimilar devices? (4) Does point-of-care PIF affect clinical decision-making? Study Design and Methods In this single-center real-world observational study, PIF was measured systematically at every outpatient visit in a subspecialized COPD clinic, and point-of-care results were provided to the clinician. Coprimary outcomes were the prevalence of outpatients with suboptimal PIF and with inappropriate devices for measured PIF. Secondary outcomes were patient-related risk factors associated with suboptimal PIF, the prevalence of non-SIT/nonsimilar devices, the prevalence of regimens consisting of either inappropriate device(s) for measured PIF and/or non-SIT/nonsimilar devices, and the effect of point-of-care PIF on clinical decision-making. Results Suboptimal PIF was identified in 45 of 161 participants (28%), and inappropriate device(s) for measured PIF were identified in 18 participants (11.2%). Significant associations were observed between suboptimal PIF and age (1.09; 95% CI, 1.04-1.15), female sex (10.30; 95% CI, 4.45-27.10), height (0.92; 95% CI, 0.88-0.96), BMI (0.90; 95% CI, 0.84-0.96), and FEV 1 (0.09; 95% CI, 0.03-0.26). After adjustment for age and sex, the association between suboptimal PIF and BMI, but not height, remained significant. Non-SIT and/or nonsimilar devices were identified in 50 participants (31.1%). Regimens consisting of either inappropriate device(s) for measured PIF and/or non-SIT/nonsimilar devices were observed in 59 participants (36.6%). Inhaler prescription changes were observed in this latter group (3.39; 95% CI, 1.76-6.64), as well as in patients with suboptimal PIF who already had SIT/similar regimens (2.93; 95% CI, 1.07-7.92). Interpretation Suboptimal PIF and inappropriate devices for measured PIF were highly prevalent among outpatients from a subspecialized COPD clinic. Our results show that female sex, reduced FEV 1 , and low BMI are important, readily identifiable risk factors for suboptimal PIF, and point-of-care PIF can inform clinical decision-making.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".