Comparison of Pi10 and Pi-Slope Calculation Methods and Association With Lung Function: Findings from the Canadian Chronic Obstructive Lung Disease (CanCOLD) Study
Notice bibliographique
Résumé
Abstract Introduction: The average wall thickness of a theoretical airway with a lumen perimeter of 10 mm (Pi10) measured using computed tomography (CT) images is a biomarker for airway remodelling in Chronic Obstructive Pulmonary Disease (COPD). Several methods have been used in the literature to calculate Pi10 leading to significant variability across studies. The objective of this study was to evaluate the consistency between Pi10 calculation methods and their association with lung function. A secondary objective was to extract a parameter called Pi-Slope, investigating whether Pi-Slope provides improved association with lung function.Methods: Participants from CanCOLD were used to calculate Pi10 and Pi-Slope. CT images were acquired at full-inspiration and airway segmentation was performed by VIDA Diagnostics Inc. Luminal perimeter and airway wall thickness were quantified using ten methods from the literature (Table 1). Pi10 was derived by plotting perimeter against the square root of the wall area using linear regression, where the slope represented Pi-Slope. The pairwise Intraclass Correlation Coefficient (ICC) assessed consistency of Pi10 and Pi-Slope between methods yielding 45 comparisons. ICC values were defined as excellent (>0.90), good (0.75-0.90), moderate (0.50-0.75), and poor (<0.50). Multivariate regression models assessed associations for Pi10 and Pi-Slope with Forced Expiratory Volume in 1 second (FEV1) and FEV1/Forced Vital Capacity (FVC), adjusted for age, sex, BMI, smoking status, pack-year, total lung capacity, and CT scanner. Statistical significance was defined using P<0.05.Results: A total of 1,351 participants with and without COPD were evaluated. The pairwise ICC results demonstrated excellent consistency for Pi10 and Pi-Slope across four methods (Patel, Nakano, Jobst, and Bhatt). In contrast, Pi10 from the Park's method showed moderate consistency and Pi10 from Gietema and Telenga's methods displayed poor consistency with those four consistent methods. For Pi-Slope, Park and Telenga's methods exhibited poor consistency and Gietema's method showed moderate consistency with the four consistent methods. Interestingly, these three methods (Gietema, Park, and Telenga) had significantly fewer CT segmented airways than the other methods (p<0.001). In multivariable analyses, five methods demonstrated negative and significant associations between Pi10 and lung function (p<0.05), while the others did not (Table 1). Pi-Slope showed significant associations with lung function across all methods.Conclusion: This study found excellent consistency among Pi10 methods that included a greater number of airways, while those incorporating fewer airways showed greater variability. Additionally, Pi-Slope demonstrated improved association with lung function assessment compared to Pi10 particularly in those methods that included fewer airways.
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 | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».