Chronic obstructive pulmonary disease (COPD): bridging the knowledge gap for early intervention and prevention of disease progression.
Notice bibliographique
Résumé
Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disorder, the leading cause of non-parturition hospital stay in Canada and the third leading cause of death globally, known for heterogeneity in its development, presentation, and progression. Treatment planning targets prevention and management of exacerbations since these aggressively impact lung function deterioration even in mild-moderate disease severity stage.There are gaps in our knowledge, among those with mild-moderate COPD, to support the detection of rapid decliners and the development of targeted therapeutics. Prevalent knowledge has evolved mainly through studies in severely ill patients and is not generalizable to milder stages. The overarching goal of this thesis is to bridge some of these pressing knowledge gaps. The Canadian Cohort of Obstructive Lung Disease (CanCOLD) participants are reflective of patients at family medicine practices with mild-moderate COPD and, hence, were selected to study characteristics of those likely to experience rapid decline. Clinically important deterioration (CID), a composite measure; the recently recalibrated Acute COPD Exacerbation Prediction Tool (ACCEPT) 2.0; and the ratio of biomarkers Advanced Glycation Endproducts (AGE)/ soluble receptor for AGE (sRAGE) were assessed for the first time for use in this population.In Manuscript 1, short-term CID (2 definitions) was examined as an indicator of deterioration in disease and dyspnea in the following short-term period. This was assessed via suitable models adjusted for age, sex, BMI, and pack-years alongside a second set of models controlled additionally for comorbidity and biomarkers. The outcomes of a) ≥100 and 200 mL declines in forced expiratory volume in 1 second (FEV1), worsening health status [≥ 4 and 8 unit increases in St. George respiratory Questionnaire score, and ≥2 and 4 unit in COPD Assessment Test] and dyspnea (≥1 unit increase in Medical Research Council score) were analyzed using logistic regression models; b) new moderate/severe exacerbations using Cox Proportional Hazards models; and c) the incidence of such exacerbations using Poisson regression models. Results show that while composite CID definition will need to be adapted for this population, health status measure and exacerbation were informative components (third component: FEV1 decline). A study to validate the findings is underway using the United Kingdom primary care data (protocol included).In Manuscript 2, the ACCEPT 2.0 model was compared to the exacerbation history (last 12 months) in the CanCOLD cohort. The observed discrimination for the ACCEPT 2.0 model was superior to the adapted exacerbation definitions used in the study. Area under the time-dependent Receiver Operating Characteristic Curve was compared using the DeLong Test, and calibration plots were reviewed. The findings support a future study in a larger cohort to recalibrate the model for the mild-moderate COPD population. Biomarkers are clinically informative and included in prediction models to improve accuracy. The pathophysiology of AGE-RAGE stress and AGE/sRAGE ratio as a disease activity marker in COPD is reviewed in Manuscript 3. Manuscript 4 reports and discusses the serum concentrations and correlations of AGE, sRAGE, and AGE/sRAGE in a CanCOLD sub-cohort with clearly defined 3 groups: healthy controls excluding conditions and drugs known to influence the biomarker levels; non-COPD smokers; and those with COPD. The ratio was significantly higher in the at-risk and COPD groups (compared to the healthy group). The data suggests the potential for AGE/sRAGE as a promising new biomarker in mild-moderate COPD. However, further evaluations are needed to explore the correlations observed here and with other available markers of COPD.The gaps identified and studies conducted in this thesis add important knowledge that dovetails toward the goal of personalized care in mild-moderate COPD
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 | 0,013 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».