Year: 2024
Type: article
Study design: Observational Study
Abstract: 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.
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Architectural structure and regulation of TOLLIP in IPF, Improving Pulmonary Fibrosis Classification with Genomics Informed Phenotypic Clusters, Elucidating the Genomic Determinants of Outcomes in Idiopathic Pulmonary Fibrosis, G2655142861, G5744991491, Prognostic Biomarker Development in Progressive Fibrosing Interstitial Lung Disease, 1/2 Prospective tReatment EffiCacy in IPF uSlng genOtype for Nac Selection (PRECISIONS) trial and Molecular Endophenotyping in Idiopathic Pulmonary Fibrosis and Interstitial Lung Diseases study, Research training in respiratory biology, Proteomic Profiling of Idiopathic Pulmonary Fibrosis Progression Trajectory, Role of polyunsaturated fatty acids in pulmonary fibrosis
OpenAlex ID: W4392281691