P18 Airwave oscillometry in relation to patient reported outcomes in asthma
Bibliographic record
Abstract
Background Airwave oscillometry (AOS: Tremoflo, Thorasys, Montreal) uses a vibrating mesh to superimpose forced oscillations of sound waves on top of normal tidal breathing to measure respiratory impedance as lung resistance (R) and reactance (X). AOS is able to determine the degree of small airways dysfunction as either peripheral airway resistance (R5-R19) or compliance as area under the reactance curve (AX). We therefore investigated the relationship of AOS to patient reported outcomes of asthma control (ACQ) and quality of life (mAQLQ). In particular, we were interested in ACQ which is a strong predictor of future exacerbation risk. Patients and methods We evaluated 46 patients with persistent asthma: Age 51 yr, FEV1 87%, R5 142%, ICS (BDP equiv) 616 µg, 65% taking LABA, 11%, LAMA, 37% LTRA. Using a cut point for R5-R19 of 0.08 kPa/l/s, there were differences (<0.08 vs ≥0.08 kPa/l/s) in mean ACQ values: 0.99 vs 1.93 (95%CI -1.66, -0.45) (Fig) and in mAQLQ (symptoms): 5.23 vs 4.30 (CI 0.10, 1.74). For AX with a cut point of 1.0 kPa/l there were differences in ACQ: 0.99 vs 1.93 (CI -1.55, -0.33), in mAQLQ symptoms: 5.28 vs 4.42 (CI 0.06, 1.66) and mAQLQ activity: 5.92 vs 5.01 (CI 0.004, 1.81). For the R5-R19 there was also a difference in FeNO: 30 vs 45 ppb (CI 13, 17). For FEV1 cut point of 80% pred differences were seen in ACQ: 2.20 vs 1.27 (CI 0.11, 1.76) and mAQLQ symptoms: 4.05 vs 5.09 (CI -1.93, -0.16) but not FeNO. For FEF25–75 cut point of 50% pred there were differences in ACQ 1.90 vs 1.23 (CI 0.003, 1.34) and FeNO 60 vs 35 ppb (CI 3, 48). Differences for ACQ and mAQLQ all exceeded the respective MCID’s of 0.5. Conclusions Peripheral lung resistance and compliance measured by AOS are related to patient reported outcomes of asthma control and quality of life as well as to type 2 inflammation. We propose that measuring AOS should compliment spirometry as part of the routine work up of asthma patients in a real life clinic setting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".