RESUMEN
Rationale and objective: Disease-specific health-related quality of life (HRQOL) instruments enable us to capture domains that are most relevant to specific patient populations and are useful when a more individualised approach to patient assessment is desired. In this study, we assessed the validity and reliability of the first instrument specifically developed to measure HRQOL in hypersensitivity pneumonitis (HP). Methods: A 39-item HP-HRQOL instrument and several anchors were collected from a cohort of patients with HP. Exploratory factor analysis and item reduction were utilised to construct a shortened version of the instrument. Several validity and reliability analyses were conducted on this version of the HP-HRQOL. Measurements and main results: 59 patients with HP completed the study. The revised HP-HRQOL instrument comprises 15 items composing two factors (domains): 1) impacts on daily life; and 2) mental wellbeing. Internal consistency reliability was strong for Factor 1 (Cronbach's α=0.94, 95% CI 0.92-0.96) and Factor 2 (Cronbach's α=0.89, 95% CI 0.85-0.94). Test-retest reliability was strong (ICC 0.94, 95% CI 0.89-0.97). The HP-HRQOL strongly correlated with other validated patient-reported outcome measures and moderately correlated with % predicted forced vital capacity. The HP-HRQOL distinguished between those with different severities of HP as determined by lung function and supplemental oxygen use. Conclusions: The HP-HRQOL, the first patient-reported outcome instrument specific to adults with HP, possesses strong validity and reliability characteristics for measuring disease-specific HRQOL and distinguishes among patients with different severities of disease.
RESUMEN
Rationale: Distinguishing connective tissue disease-associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF) can be clinically challenging. Objectives: To identify proteins that separate and classify patients with CTD-ILD and those with IPF. Methods: Four registries with 1,247 patients with IPF and 352 patients with CTD-ILD were included in analyses. Plasma samples were subjected to high-throughput proteomics assays. Protein features were prioritized using recursive feature elimination to construct a proteomic classifier. Multiple machine learning models, including support vector machine, LASSO (least absolute shrinkage and selection operator) regression, random forest, and imbalanced Random Forest, were trained and tested in independent cohorts. The validated models were used to classify each case iteratively in external datasets. Measurements and Main Results: A classifier with 37 proteins (proteomic classifier 37 [PC37]) was enriched in the biological process of bronchiole development and smooth muscle proliferation and immune responses. Four machine learning models used PC37 with sex and age score to generate continuous classification values. Receiver operating characteristic curve analyses of these scores demonstrated consistent areas under the curve of 0.85-0.90 in the test cohort and 0.94-0.96 in the single-sample dataset. Binary classification demonstrated 78.6-80.4% sensitivity and 76-84.4% specificity in the test cohort and 93.5-96.1% sensitivity and 69.5-77.6% specificity in the single-sample classification dataset. Composite analysis of all machine learning models confirmed 78.2% (194 of 248) accuracy in the test cohort and 82.9% (208 of 251) in the single-sample classification dataset. Conclusions: Multiple machine learning models trained with large cohort proteomic datasets consistently distinguished CTD-ILD from IPF. Many of the identified proteins are involved in immune pathways. We further developed a novel approach for single-sample classification, which could facilitate honing the differential diagnosis of ILD in challenging cases and improve clinical decision making.