Neighborhood Disadvantage Associations With Radiologic Features and Patterns in Fibrotic Interstitial Lung Disease
Notice bibliographique
Résumé
Abstract Rationale: Higher neighborhood-level disadvantage is associated with lower baseline lung function in patients with fibrotic interstitial lung disease (fILD), but the association of disadvantage with radiologic features and patterns remains unknown. Methods: Patients with fILD enrolled in the Canadian Registry for Pulmonary Fibrosis were evaluated in standardized multi-disciplinary discussion (MDD). Expert chest radiologists blinded to clinical data evaluated baseline computed tomography scans, visually quantifying the percentage of lung parenchyma affected by honeycombing, reticulation, ground glass opacities (GGO), and hypoattenuating lung and determining the top radiologic pattern (usual interstitial pneumonia=UIP, fibrotic hypersensitivity pneumonitis=fHP, non-specific interstitial pneumonia=NSIP, “no confident pattern”). Clinical data was introduced, and a top clinical diagnosis was assigned by the radiologist and an ILD clinician. Neighborhood disadvantage was assigned to patient residential locations using the Canadian Index of Multiple Deprivation (CIMD) score and its 4 domains (residential instability, ethnocultural composition, economic dependency, situational vulnerability), with higher scores reflecting greater disadvantage. Linear models adjusted for age, sex, smoking, race, and baseline lung function evaluated associations of CIMD score with percentage of radiologic features. Multinomial models adjusting for the same covariates determined associations of CIMD score with radiologic pattern, using UIP as the reference. Results: Of 1473 patients included, the most common diagnoses were CTD-ILD (48%), IPF (29%), and fHP (16%). Higher CIMD score (range: -1.07 to 2.57) was associated with 0.62% increased honeycombing (95%CI=0.02-1.23, p=0.04, Table). Higher ethnocultural composition score (indicating neighborhoods with higher foreign-born, immigrant, minoritized, or linguistically-isolated individuals) was associated with less reticulations and increased pure GGO. Higher CIMD score was not associated with greater odds of fHP or NSIP patterns as compared with UIP, although higher ethnocultural composition score was associated with higher odds of NSIP (OR=1.18, 95%CI=1.01-1.39, p=0.04). Within patients with a top MDD diagnosis of IPF, higher total CIMD and ethnocultural scores were associated with more honeycombing. In CTD-ILD, higher residential instability was associated with more honeycombing and higher ethnocultural composition score with more hypoattenuating lung. In fHP, none of the scores were associated with worse honeycombing, reticulations, GGO, or hypoattenuating lung. Conclusions: Neighborhood disadvantage is associated with worse honeycombing, supporting prior findings that patients living in more disadvantaged areas present to tertiary ILD care with greater disease burden. Increased ethnocultural composition scores were associated with increased extent of inflammatory features in CTD-ILD and higher odds of an NSIP pattern, suggesting possible demographic, geographic, and/or genetic risk factors for this type of fILD.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».