Fractional exhaled nitric oxide (FeNO) as inflammatory biomarker in chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap (ACO)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,017 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».