Fractional exhaled nitric oxide (FeNO) as inflammatory biomarker in chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap (ACO)
Bibliographic record
Abstract
Background and Objectives: Chronic obstructive pulmonary disease (COPD) and asthma are the most common inflammatory airway diseases. Some individuals share features of both asthma and COPD called asthma-COPD overlap (ACO) syndrome (ACOS). These individuals have worse symptoms and health status as well as lower pulmonary function than COPD-only. There is a remarkable need to have access to a biomarker that could be used in a clinical setting to be able to differentiate ACO(S) from COPD-only. Fractional exhaled nitric oxide (FeNO) is a promising biomarker identifying eosinophilic and T-helper cell 2 (Th2)-mediated airway inflammation in asthma. Its measurement is easy, sensitive, reproducible, and non-invasive. The exact role of FeNO in COPD and in differentiating COPD from ACO(S) is still unclear and needs to be defined. We aimed to systematically search the literature to present an overview of the existing literature in a field of interest, i.e., FeNO in COPD, and as well synthesize and aggregate findings from different studies. Furthermore, we conducted a study embedded in the Canadian Cohort Obstructive Lung Disease (CanCOLD) to evaluate the role of FeNO and determine if there is a cut-off value that can differentiate ACO(S) from COPD-only. Methods: Firstly, we conducted a systematic scoping review to determine key concepts, and to explore gaps within a developing field of research. Secondly, we carried out a study embedded in CanCOLD with new measurement including FeNO level. The COPD participants were divided into ACO and COPD-only (non-ACO). The different levels of FeNO and its utility were assessed between ACO and COPD-only. The optimal cut-off values and the receiver operating characteristic (ROC) curves were obtained to evaluate the clinical utility of FeNO in diagnosing ACO(S). Results: From the scoping review, 38 studies were selected, 24 were on modifying factors in FeNO measurement in COPD patients, 18 were on FeNO in COPD and compared to healthy subjects, 22 on FeNO and disease severity or progression, 7 on FeNO and ACO(S), 12 on FeNO and biomarkers, and 8 on FeNO and treatment response. From the original study embedded in CanCOLD, a total of 169 subjects were enrolled, of those 95 were COPD with ACO, N=46, and COPD-only, N=49. The mean FeNO level was higher but not statistically significant between ACO and COPD-only. The significant optimal cut-off values to differentiate ACO from COPD-only was for Def 1 FeNO ≥36 ppb with the sensitivity of 39%, specificity of 88% and AUC of 0.63, p=0.046, and Def 3 FeNO ≥23.5 ppb with the sensitivity of 80% and specificity of 50%, and area under the curve of 0.65, p=0.047.Conclusion: From the scoping review when measuring FeNO, the evidence is still lacking preventing us from recommending the general use of FeNO in clinical practice for COPD patients. Although FeNO level is higher in ACO(S) patients than COPD-only, it is still unclear if there is a FeNO cut-off that can be used to make the diagnosis of ACO(S) and/or to guide therapy with inhaled corticosteroids/glucocorticoids in COPD patients. After studying FeNO in a population-based sample of COPD, we were not able to show that FeNO levels could be used as a biomarker for differentiating ACO from COPD-only, and it is still too soon to be able to make a recommendation of using it in clinical practice.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".