Phage Immunoprecipitation-Sequencing Reveals CDHR5 Autoantibodies in Select Patients With Interstitial Lung Disease.

OBJECTIVE: Interstitial lung diseases (ILDs) are a heterogeneous group of disorders that can develop in patients with connective tissue diseases. Establishing autoimmunity in ILD impacts prognosis and treatment. Patients with ILD are screened for autoimmunity by measuring antinuclear autoantibodies, rheumatoid factors, and other nonspecific tests. However, this approach may miss autoimmunity that manifests as autoantibodies to tissue antigens not previously defined in ILD. METHODS: We use Phage Immunoprecipitation-Sequencing (PhIP-Seq) to conduct an autoantibody discovery screen of patients with ILD and controls. We screened for novel autoantigen candidates using PhIP-Seq. We next developed a radio-labeled binding assay and validated the leading candidate in 398 patients with ILD recruited from two academic medical centers and 138 blood bank individuals that formed our reference cohort. RESULTS: PhIP-Seq identified 17 novel autoreactive targets, and machine learning classifiers derived from these targets discriminated ILD serum from controls. Among the 17 candidates, we validated CDHR5 and found CDHR5 autoantibodies in patients with rheumatologic disorders and importantly, patients not previously diagnosed with autoimmunity. Using survival and transplant free-survival data available from one of the two centers, patients with CDHR5 autoantibodies showed worse survival compared with other patients with connective tissue disease ILD. CONCLUSION: We used PhIP-Seq to define a novel CDHR5 autoantibody in a subset of select patients with ILD. Our data complement a recent study showing polymorphisms in the CDHR5-IRF7 gene locus strongly associated with titer of anticentromere antibodies in systemic sclerosis, creating a growing body of evidence suggesting a link between CDHR5 and autoimmunity.

Hypersensitivity Pneumonitis on Thin-Section Chest CT Scans: Diagnostic Performance of the ATS/JRS/ALAT versus ACCP Imaging Guidelines.

Purpose To compare the diagnostic performance of the American Thoracic Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax (ATS/JRS/ALAT) versus the American College of Chest Physicians (ACCP) imaging classifications for hypersensitivity pneumonitis (HP). Materials and Methods Patients in the institutional review board-approved Interstitial Lung Disease (ILD) registry referred for multidisciplinary discussion (MDD) at the authors' institution (January 1, 2006-April 1, 2021) were included in this retrospective study when ILD was diagnosed at MDD. MDD diagnoses included HP, connective tissue disease-ILD, and idiopathic pulmonary fibrosis. Retrospective review of thin-section CT images was performed in consensus by two cardiothoracic radiologists blinded to the diagnosis. Diagnostic patterns were determined for thin-section CT images using both classifications. Discordance rates were determined. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were assessed using MDD diagnosis as the reference standard. Results A total of 297 patients were included in the study: 200 (67%) with HP, 49 (16%) with connective tissue disease-ILD, and 48 (16%) with idiopathic pulmonary fibrosis at MDD. The discordance rate between the two classifications was 21%. Assuming low HP prevalence (10%), ATS/JRS/ALAT classification outperformed ACCP classification, with greater accuracy (92.3% vs 87.6%) and greater positive predictive value (60.7% vs 42.9%). Assuming high prevalence (50%), accuracy and negative predictive value were superior using ACCP classification (81.7% vs 79.7% and 77.7% vs 72.6%, respectively), and positive predictive value was superior using ATS/JRS/ALAT classification (93.3% vs 87.1%). Conclusion Accuracy of the ATS/JRS/ALAT and ACCP HP classifications was greater in settings with low and high HP prevalence, respectively. Diagnostic performance of both classifications was discordant in a minority of cases. Keywords: CT, Thorax, Hypersensitivity Pneumonitis, Interstitial Lung Disease Supplemental material is available for this article. © RSNA, 2024.

The impact of antifibrotic use on long-term clinical outcomes in the pulmonary fibrosis foundation registry.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease (ILD) with a high mortality rate. The antifibrotic medications pirfenidone and nintedanib have been in use since 2014 for this disorder and are associated with improved rate of lung function decline. Less is known about their long-term outcomes outside of the clinical trial context. METHODS: The Pulmonary Fibrosis Foundation Patient Registry was used for this study. Patients with an IPF diagnosis made within a year of enrollment were included. The treated group was defined as patients receiving either pirfenidone or nintedanib for at least 180 days. The untreated group did not have any record of antifibrotic use. Demographic data, comorbidities, serial lung function, hospitalization, and vital status data were collected from the registry database. The primary outcomes were transplant-free survival, time to first respiratory hospitalization, and time to 10% absolute FVC decline. Time-to-event analyses were performed utilizing Cox proportional hazards models and the log-rank test. Model covariates included age, gender, smoking history, baseline lung function, comorbidities, and oxygen use. RESULTS: The registry contained 1212 patients with IPF; ultimately 288 patients met inclusion criteria for the treated group, and 101 patients were designated as untreated. Patients treated with antifibrotics were significantly younger (69.8 vs. 72.6 years, p = 0.008) and less likely to have smoked (61.1% ever smokers vs. 72.3% never smokers, p = 0.04). No significant differences were seen in race, gender, comorbidities, or baseline pulmonary function between groups. The primary outcome of transplant-free survival was not significantly different between the two groups (adjusted HR 0.799, 95% CI 0.534-1.197, p = 0.28). Time to respiratory hospitalization was significantly shorter in the treated group (adjusted HR 2.12, 95% CI 1.05-4.30, p = 0.04). No significant difference in time to pulmonary function decline was seen between groups. CONCLUSIONS: This multicenter study demonstrated 63% of newly diagnosed IPF patients had continuous antifibrotic usage. Antifibrotics were not associated with improved transplant-free survival or pulmonary function change but was associated with an increased hazard of respiratory hospitalization. Future studies should further investigate the role of antifibrotic therapy in clinically important outcomes in real-world patients with IPF and other progressive ILDs.

Scoping review of exposure questionnaires and surveys in interstitial lung disease.

BACKGROUND: Many interstitial lung diseases (ILDs) have clear causal relationships with environmental and occupational exposures. Exposure identification can assist with diagnosis, understanding disease pathogenesis, prognostication and prevention of disease progression and occurrence in others at risk. Despite the importance of exposure identification in ILD, there is no standardised assessment approach. Many questionnaires are in clinical and research use, yet their utility, applicability, relevance and performance characteristics are unknown. OBJECTIVES: This scoping review aimed to summarise the available evidence relating to ILD exposure assessment questionnaires, identify research gaps and inform the content for a future single evidence-based ILD questionnaire. METHODS: A scoping review based on Arksey and O'Malley's methodological framework was conducted. ELIGIBILITY CRITERIA: Any questionnaire that elicited exposures specific to ILD was included. A modified COSMIN Risk of Bias Framework was used to assess quality. SOURCES OF EVIDENCE: Relevant articles were identified from MEDLINE and EMBASE up to 23 July 2023. RESULTS: 22 exposure questionnaires were identified, including 15 generally pertaining to ILD, along with several disease-specific questionnaires for hypersensitivity pneumonitis (n=4), chronic beryllium disease, sarcoidosis and silicosis (1 questionnaire each). For most questionnaires, quality was low, whereby the methods used to determine exposure inclusion and questionnaire validation were not reported or not performed. Collectively the questionnaires covered 158 unique exposures and at-risk occupations, most commonly birds, mould/water damage, wood dust, asbestos, farming, automotive mechanic and miners. Only five questionnaires also provided free-text fields, and 13 queried qualifiers such as temporality or respiratory protection. CONCLUSIONS: Designing a robust ILD-specific questionnaire should include an evidence-based and relevance-based approach to exposure derivation, with clinicians and patients involved in its development and tested to ensure relevance and feasibility.

Machine Learning of Plasma Proteomics Classifies Diagnosis of Interstitial Lung Disease.

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.

Risk Factors for Hospital Readmission in Patients With Interstitial Lung Disease.

BACKGROUND: Little is known about the rates, causes, or risk factors for hospital readmission among patients with interstitial lung disease (ILD). We investigated the prevalence, features, and comorbidities of subjects hospitalized with ILD and their subsequent re-hospitalizations in this retrospective study. METHODS: A retrospective analysis of subjects enrolled in the University of Chicago ILD Natural History registry was conducted. Demographic data, comorbidities, and timing and cause of subsequent hospitalizations were collected from the medical record. The primary outcome was time to first readmission via a cause-specific Cox hazards model with a sensitivity analysis with the Fine-Gray cumulative hazard model; the secondary outcome was the number of hospitalizations per subject via a Poisson multivariable model. RESULTS: Among 1,796 patients with ILD, 443 subjects were hospitalized, with 978 total hospitalizations; 535 readmissions were studied, 282 (53%) for a respiratory indication. For the outcome of time to readmission, Black race was the only subject characteristic associated with an increased hazard of readmission in the Cox model (hazard ratio 1.50, P = .03) while Black race, hypersensitivity pneumonitis, and sarcoidosis were associated with increased hazard of readmission in the Fine-Gray model. Black race, female sex, atrial fibrillation, obstructive lung disease, and pulmonary hypertension were associated with an increased number of hospitalizations in the Poisson model. CONCLUSIONS: We demonstrated that hospital readmission from any cause was a common occurrence in subjects with ILD. Further efforts to improve quality of life among these subjects could focus on risk scores for readmission, mitigating racial health disparities, and treatment of comorbidities.

Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes.

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

Proteomic Biomarkers of Survival in Idiopathic Pulmonary Fibrosis.

RATIONALE: Idiopathic pulmonary fibrosis (IPF) causes progressive lung scarring and high mortality. Reliable and accurate prognostic biomarkers are urgently needed. OBJECTIVE: To identify and validate circulating protein biomarkers of IPF survival. METHODS: High-throughput proteomic data were generated using prospectively collected plasma samples from patients with IPF from the Pulmonary Fibrosis Foundation Patient Registry (discovery cohort) and the Universities of California-Davis, Chicago, and Virginia (validation cohort). Proteins associated with three-year transplant-free survival (TFS) were identified using multivariable Cox proportional hazards regression. Those associated with TFS after adjustment for false discovery in the discovery cohort were advanced for testing in the validation cohort, with proteins maintaining TFS association with consistent effect direction considered validated. After combining cohorts, functional analyses were performed, and machine learning used to derive a proteomic signature of TFS. MAIN RESULTS: Of 2921 proteins tested in the discovery cohort (n=871), 231 were associated with differential TFS. Of these, 140 maintained TFS association with consistent effect direction in the validation cohort (n=355). After combining cohorts, validated proteins with strongest TFS association were latent-transforming growth factor beta-binding protein 2 (HR 2.43, 95% CI 2.09-2.82), collagen alpha-1(XXIV) chain (HR 2.21; 95% CI 1.86-2.39) and keratin 19 (HR 1.60; 95% CI 1.47-1.74). In decision curve analysis, a proteomic signature of TFS outperformed a similarly derived clinical prediction model. CONCLUSIONS: In largest proteomic investigation of IPF outcomes performed to date, we identified and validated 140 protein biomarkers of TFS. These results shed important light on potential drivers of IPF progression.

Tobacco smoking is associated with combined pulmonary fibrosis and emphysema and worse outcomes in interstitial lung disease.

Tobacco smoking is an established cause of pulmonary disease whose contribution to interstitial lung disease (ILD) is incompletely characterized. We hypothesized that compared with nonsmokers, subjects who smoked tobacco would differ in their clinical phenotype and have greater mortality. We performed a retrospective cohort study of tobacco smoking in ILD. We evaluated demographic and clinical characteristics, time to clinically meaningful lung function decline (LFD), and mortality in patients stratified by tobacco smoking status (ever vs. never) within a tertiary center ILD registry (2006-2021) and replicated mortality outcomes across four nontertiary medical centers. Data were analyzed by two-sided t tests, Poisson generalized linear models, and Cox proportional hazard models adjusted for age, sex, forced vital capacity (FVC), diffusion capacity of the lung for carbon monoxide (DLCO), ILD subtype, antifibrotic therapy, and hospital center. Of 1,163 study participants, 651 were tobacco smokers. Smokers were more likely to be older, male, have idiopathic pulmonary fibrosis (IPF), coronary artery disease, CT honeycombing and emphysema, higher FVC, and lower DLCO than nonsmokers (P < 0.01). Time to LFD in smokers was shorter (19.7 ± 20 mo vs. 24.8 ± 29 mo; P = 0.038) and survival time was decreased [10.75 (10.08-11.50) yr vs. 20 (18.67-21.25) yr; adjusted mortality HR = 1.50, 95%CI 1.17-1.92; P < 0.0001] compared with nonsmokers. Smokers had 12% greater odds of death for every additional 10 pack yr of smoking (P < 0.0001). Mortality outcomes remained consistent in the nontertiary cohort (HR = 1.51, 95%CI = 1.03-2.23; P = 0.036). Tobacco smokers with ILD have a distinct clinical phenotype strongly associated with the syndrome of combined PF and emphysema, shorter time to LFD, and decreased survival. Smoking prevention may improve ILD outcomes.NEW & NOTEWORTHY Smoking in ILD is associated with combined pulmonary fibrosis and emphysema and worse clinical outcomes.