Genetics and Genomics of Pulmonary Fibrosis: Charting the Molecular Landscape and Shaping Precision Medicine.

Recent genetic and genomic advancements have elucidated the complex etiology of idiopathic pulmonary fibrosis (IPF) and other progressive fibrotic interstitial lung diseases (ILDs), emphasizing the contribution of heritable factors. This state-of-the-art review synthesizes evidence on significant genetic contributors to pulmonary fibrosis (PF), including rare genetic variants and common single nucleotide polymorphisms (SNPs). The MUC5B promoter variant is unusual, a common SNP that markedly elevates the risk of early and established PF. We address the utility of genetic variation in enhancing understanding of disease pathogenesis, clinical phenotypes, improving disease definitions, and informing prognosis and treatment response. Critical research gaps are highlighted, particularly the underrepresentation of non-European ancestries in PF genetic studies and the exploration of PF phenotypes beyond usual interstitial pneumonia (UIP)/IPF. We discuss the role of telomere length, often critically short in PF, and its link to progression and mortality, underscoring the genetic complexity involving telomere biology genes (TERT, TERC) and others like SFTPC and MUC5B. Additionally, we address the potential of gene-by-environment interactions to modulate disease manifestation, advocating for precision medicine in PF. Insights from gene expression profiling studies and multi-omic analyses highlight the promise for understanding disease pathogenesis and offer new approaches to clinical care, therapeutic drug development, and biomarker discovery. Finally, we discuss the ethical, legal, and social implications of genomic research and therapies in PF, stressing the need for sound practices and informed clinical genetic discussions. Looking forward, we advocate for comprehensive genetic testing panels and polygenic risk scores to improve the management of PF and related ILDs across diverse populations.

A multidimensional classifier to support lung transplant referral in patients with pulmonary fibrosis.

BACKGROUND: Lung transplantation remains the sole curative option for patients with idiopathic pulmonary fibrosis (IPF), but donor organs remain scarce, and many eligible patients die before transplant. Tools to optimize the timing of transplant referrals are urgently needed. METHODS: Least absolute shrinkage and selection operator was applied to clinical and proteomic data generated as part of a prospective cohort study of interstitial lung disease (ILD) to derive clinical, proteomic, and multidimensional logit models of near-term death or lung transplant within 18 months of blood draw. Model-fitted values were dichotomized at the point of maximal sensitivity and specificity, and decision curve analysis was used to select the best-performing classifier. We then applied this classifier to independent IPF and non-IPF ILD cohorts to determine test performance characteristics. Cohorts were restricted to patients aged ≤72 years with body mass index 18 to 32 to increase the likelihood of transplant eligibility. RESULTS: IPF derivation, IPF validation, and non-IPF ILD validation cohorts consisted of 314, 105, and 295 patients, respectively. A multidimensional model comprising 2 clinical variables and 20 proteins outperformed stand-alone clinical and proteomic models. Following dichotomization, the multidimensional classifier predicted near-term outcome with 70% sensitivity and 92% specificity in the IPF validation cohort and 70% sensitivity and 80% specificity in the non-IPF ILD validation cohort. CONCLUSIONS: A multidimensional classifier of near-term outcomes accurately discriminated this end-point with good test performance across independent IPF and non-IPF ILD cohorts. These findings support refinement and prospective validation of this classifier in transplant-eligible individuals.

Use of Race-Specific Equations in Pulmonary Function Tests Impedes Potential Eligibility for Care and Treatment of Pulmonary Fibrosis.

RATIONALE: The use of race-specific reference values to evaluate pulmonary function has long been embedded into clinical practice; however, there is a growing consensus that this practice may be inappropriate and that use of race-neutral equations should be adopted to improve access to healthcare. OBJECTIVES: To evaluate if using race-neutral equations to assess percent predicted forced vital capacity (FVC%pred) impacts eligibility for clinical trials, antifibrotic therapy and referral for lung transplantation in Black, Hispanic/Latino, and White patients with interstitial lung disease (ILD) Methods: FVC%pred values for patients from the Pulmonary Fibrosis Foundation Patient Registry were calculated using race-specific (Hankinson 1999), race-agnostic (Global Lung Function Initiative [GLI]-2012), and race-neutral (GLI-2022 or GLI-Global) equations. Eligibility for ILD clinical trials (FVC%pred >45% and <90%), antifibrotic therapy (FVC%pred >55% and <82%), and lung transplantation referral (FVC%pred <70%) using GLI-2022 and GLI-2012 were compared with that of Hankinson 1999. RESULTS: Baseline characteristics were available for 1882 patients (Black, n=104; Hispanic/Latino, n=103; White, n=1675), and outcomes were evaluated in 1531 patients with FVC%pred within ±90 days of Registry enrollment (Black, n=78; Hispanic/Latino, n=72; White, n=1381). Black patients were younger at consent and more likely to be female than Hispanic/Latino or White patients. Compared with GLI-2022, Hankinson's 1999 criteria misclassified 22% of Black patients, 14% of Hispanic/Latino patients, and 12% of White patients for ILD clinical trial eligibility; misclassified 21% of Black patients, 17% of Hispanic/Latino patients, and 19% of White patients for antifibrotic therapy eligibility; and misclassified 6% of Black patients, 14% of Hispanic/Latino patients, and 12% of White patients for lung transplantation referral. Similar trends were observed when comparing GLI-2012 with Hankinson 1999. CONCLUSIONS: Misclassification of patients for critical interventions is highly prevalent when using Hankinson's 1999 criteria and highlights the need to consider adopting the race-neutral GLI-2022 for enhanced accuracy and more equitable representation in pulmonary healthcare. Our results make a compelling case for re-evaluating the use of race as a physiological variable and emphasize the pressing need for continuous innovation to ensure equal and optimal care for all patients, regardless of their race or ethnicity. CLINICAL TRIAL REGISTRATION: NCT02758808 Primary source of funding: This analysis was sponsored by F. Hoffmann-La Roche, Ltd./Genentech, Inc.

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: Identify proteins that separate and classify CTD-ILD from IPF patients. METHODS: Four registries with 1247 IPF and 352 CTD-ILD patients were included in analyses. Plasma samples were subjected to high-throughput proteomics assays. Protein features were prioritized using Recursive Feature Elimination (RFE) to construct a proteomic classifier. Multiple machine learning models, including Support Vector Machine, LASSO regression, Random Forest (RF), and imbalanced-RF, were trained and tested in independent cohorts. The validated models were used to classify each case iteratively in external datasets. MEASUREMENT AND MAIN RESULTS: A classifier with 37 proteins (PC37) was enriched in 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 Area-Under-Curve 0.85-0.90 in 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 test cohort, 93.5%-96.1% sensitivity and 69.5%-77.6% specificity in single-sample classification dataset. Composite analysis of all machine learning models confirmed 78.2% (194/248) accuracy in test cohort and 82.9% (208/251) in single-sample classification dataset. CONCLUSIONS: Multiple machine learning models trained with large cohort proteomic datasets consistently distinguished CTD-ILD from IPF. Identified proteins 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.

Association study of human leukocyte antigen variants and idiopathic pulmonary fibrosis.

Introduction: Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial pneumonia marked by progressive lung fibrosis and a poor prognosis. Recent studies have highlighted the potential role of infection in the pathogenesis of IPF, and a prior association of the HLA-DQB1 gene with idiopathic fibrotic interstitial pneumonia (including IPF) has been reported. Owing to the important role that the human leukocyte antigen (HLA) region plays in the immune response, here we evaluated if HLA genetic variation was associated specifically with IPF risk. Methods: We performed a meta-analysis of associations of the HLA region with IPF risk in individuals of European ancestry from seven independent case-control studies of IPF (comprising 5159 cases and 27 459 controls, including a prior study of fibrotic interstitial pneumonia). Single nucleotide polymorphisms, classical HLA alleles and amino acids were analysed and signals meeting a region-wide association threshold of p<4.5×10-4 and a posterior probability of replication >90% were considered significant. We sought to replicate the previously reported HLA-DQB1 association in the subset of studies independent of the original report. Results: The meta-analysis of all seven studies identified four significant independent single nucleotide polymorphisms associated with IPF risk. However, none met the posterior probability for replication criterion. The HLA-DQB1 association was not replicated in the independent IPF studies. Conclusion: Variation in the HLA region was not consistently associated with risk in studies of IPF. However, this does not preclude the possibility that other genomic regions linked to the immune response may be involved in the aetiology of IPF.

Home Oxygen After Hospitalization for COVID-19: Results From the Multi-Center OXFORD Study.

BACKGROUND: In the first months of the pandemic, prior to the introduction of proven-effective treatments, 15-37% of patients hospitalized with COVID-19 were discharged on home oxygen. After proven-effective treatments for acute COVID-19 were established by evidence-based guidelines, little remains known about home oxygen requirements following hospitalization for COVID-19. METHODS: This was a retrospective, multi-center cohort study of subjects hospitalized for COVID-19 between October 2020-September 2021 at 3 academic health centers. Information was abstracted from electronic health records at the index hospitalization and for 60 d after discharge. The World Health Organization COVID-19 Clinical Progression Scale score was used to identify patients with severe COVID-19. RESULTS: Of 517 subjects (mean age 58 y, 47% female, 42% Black, 36% Hispanic, 22% with severe COVID-19), 81% were treated with systemic corticosteroids, 61% with remdesivir, and 2.5% with tocilizumab. About one quarter of subjects were discharged on home oxygen (26% [95% CI 22-29]). Older age (adjusted odds ratio [aOR] 1.02 per 5 y [95% CI 1.02-1.02]), higher body mass index (aOR 1.02 per kg/m2 [1.00-1.04]), diabetes (yes vs no, aOR 1.73 [1.46-2.02]), severe COVID-19 (vs moderate, aOR 3.19 [2.19-4.64]), and treatment with systemic corticosteroids (yes vs no, aOR 30.63 [4.51-208.17]) were associated with an increased odds of discharge on home oxygen. Comorbid hypertension (yes vs no, aOR 0.71 [0.66-0.77) was associated with a decreased odds of home oxygen. Within 60 d of hospital discharge, 50% had documentation of pulse oximetry; in this group, home oxygen was discontinued in 46%. CONCLUSIONS: About one in 41 subjects were prescribed home oxygen after hospitalization for COVID-19, even after guidelines established proven-effective treatments for acute illness. Evidence-based strategies to reduce the requirement for home oxygen in patients hospitalized for COVID-19 are needed.

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.

Genome-wide SNP-sex interaction analysis of susceptibility to idiopathic pulmonary fibrosis.

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic lung condition that is more prevalent in males than females. The reasons for this are not fully understood, with differing environmental exposures due to historically sex-biased occupations, or diagnostic bias, being possible explanations. To date, over 20 independent genetic variants have been identified to be associated with IPF susceptibility, but these have been discovered when combining males and females. Our aim was to test for the presence of sex-specific associations with IPF susceptibility and assess whether there is a need to consider sex-specific effects when evaluating genetic risk in clinical prediction models for IPF. Methods: We performed genome-wide single nucleotide polymorphism (SNP)-by-sex interaction studies of IPF risk in six independent IPF case-control studies and combined them using inverse-variance weighted fixed effect meta-analysis. In total, 4,561 cases (1,280 females and 2,281 males) and 23,500 controls (8,360 females and 14,528 males) of European genetic ancestry were analysed. We used polygenic risk scores (PRS) to assess differences in genetic risk prediction between males and females. Findings: Three independent genetic association signals were identified. All showed a consistent direction of effect across all individual IPF studies and an opposite direction of effect in IPF susceptibility between females and males. None had been previously identified in IPF susceptibility genome-wide association studies (GWAS). The predictive accuracy of the PRSs were similar between males and females, regardless of whether using combined or sex-specific GWAS results. Interpretation: We prioritised three genetic variants whose effect on IPF risk may be modified by sex, however these require further study. We found no evidence that the predictive accuracy of common SNP-based PRSs varies significantly between males and females.

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.

Treatment of Systemic Sclerosis-associated Interstitial Lung Disease: Evidence-based Recommendations. An Official American Thoracic Society Clinical Practice Guideline.

Background: Interstitial lung disease (ILD) is a significant cause of morbidity and mortality in patients with systemic sclerosis (SSc). To date, clinical practice guidelines regarding treatment for patients with SSc-ILD are primarily consensus based. Methods: An international expert guideline committee composed of 24 individuals with expertise in rheumatology, SSc, pulmonology, ILD, or methodology, and with personal experience with SSc-ILD, discussed systematic reviews of the published evidence assessed using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Predetermined conflict-of-interest management strategies were applied, and recommendations were made for or against specific treatment interventions exclusively by the nonconflicted panelists. The confidence in effect estimates, importance of outcomes studied, balance of desirable and undesirable consequences of treatment, cost, feasibility, acceptability of the intervention, and implications for health equity were all considered in making the recommendations. This was in accordance with the American Thoracic Society guideline development process, which is in compliance with the Institute of Medicine standards for trustworthy guidelines. Results: For treatment of patients with SSc-ILD, the committee: 1) recommends the use of mycophenolate; 2) recommends further research into the safety and efficacy of (a) pirfenidone and (b) the combination of pirfenidone plus mycophenolate; and 3) suggests the use of (a) cyclophosphamide, (b) rituximab, (c) tocilizumab, (d) nintedanib, and (e) the combination of nintedanib plus mycophenolate. Conclusions: The recommendations herein provide an evidence-based clinical practice guideline for the treatment of patients with SSc-ILD and are intended to serve as the basis for informed and shared decision making by clinicians and patients.

A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia.

BACKGROUND: Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed. RESEARCH QUESTION: Does a deep learning (DL)-based classifier for usual interstitial pneumonia (UIP) derived using CT scan features accurately discriminate radiologist-determined visual UIP? STUDY DESIGN AND METHODS: A retrospective cohort study was performed. Chest CT scans acquired in individuals with and without ILD were drawn from a variety of public and private data sources. Using radiologist-determined visual UIP as ground truth, a convolutional neural network was used to learn discrete CT scan features of UIP, with outputs used to predict the likelihood of UIP using a linear support vector machine. Test performance characteristics were assessed in an independent performance cohort and multicenter ILD clinical cohort. Transplant-free survival was compared between UIP classification approaches using the Kaplan-Meier estimator and Cox proportional hazards regression. RESULTS: A total of 2,907 chest CT scans were included in the training (n = 1,934), validation (n = 408), and performance (n = 565) data sets. The prevalence of radiologist-determined visual UIP was 12.4% and 37.1% in the performance and ILD clinical cohorts, respectively. The DL-based UIP classifier predicted visual UIP in the performance cohort with sensitivity and specificity of 93% and 86%, respectively, and in the multicenter ILD clinical cohort with 81% and 77%, respectively. DL-based and visual UIP classification similarly discriminated survival, and outcomes were consistent among cases with positive DL-based UIP classification irrespective of visual classification. INTERPRETATION: A DL-based classifier for UIP demonstrated good test performance across a wide range of UIP prevalence and similarly discriminated survival when compared with radiologist-determined UIP. This automated tool could efficiently screen for UIP in patients undergoing chest CT scan and identify a high-risk phenotype among those with known ILD.

Associations of Plasma Omega-3 Fatty Acids With Progression and Survival in Pulmonary Fibrosis.

BACKGROUND: Preclinical experiments suggest protective effects of omega-3 fatty acids and their metabolites in lung injury and fibrosis. Whether higher intake of omega-3 fatty acids is associated with disease progression and survival in humans with pulmonary fibrosis is unknown. RESEARCH QUESTION: What are the associations of plasma omega-3 fatty acid levels (a validated marker of omega-3 nutritional intake) with disease progression and transplant-free survival in pulmonary fibrosis? STUDY DESIGN AND METHODS: Omega-3 fatty acid levels were measured from plasma samples of patients with clinically diagnosed pulmonary fibrosis from the Pulmonary Fibrosis Foundation Patient Registry (n = 150), University of Virginia (n = 58), and University of Chicago (n = 101) cohorts. The N-3 index (docosahexaenoic acid + eicosapentaenoic acid) was the primary exposure variable of interest. Linear-mixed effects models with random intercept and slope were used to examine associations of plasma omega-3 fatty acid levels with changes in FVC and diffusing capacity for carbon monoxide over a period of 12 months. Cox proportional hazards models were used to examine transplant-free survival. Stratified analyses by telomere length were performed in the University of Chicago cohort. RESULTS: Most of the cohort were patients with idiopathic pulmonary fibrosis (88%) and male patients (74%). One-unit increment in log-transformed N-3 index plasma level was associated with a change in diffusing capacity for carbon monoxide of 1.43 mL/min/mm Hg per 12 months (95% CI, 0.46-2.41) and a hazard ratio for transplant-free survival of 0.44 (95% CI, 0.24-0.83). Cardiovascular disease history, smoking, and antifibrotic usage did not significantly modify associations. Omega-3 fatty acid levels were not significantly associated with changes in FVC. Higher eicosapentaenoic acid plasma levels were associated with longer transplant-free survival among University of Chicago participants with shorter telomere length (P value for interaction = .02). INTERPRETATION: Further research is needed to investigate underlying biological mechanisms and whether omega-3 fatty acids are a potential disease-modifying therapy.

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.

Peripheral Blood Single-Cell Sequencing Uncovers Common and Specific Immune Aberrations in Fibrotic Lung Diseases.

Rationale and Objectives: The extent and commonality of peripheral blood immune aberrations in fibrotic interstitial lung diseases are not well characterized. In this study, we aimed to identify common and distinct immune aberrations in patients with idiopathic pulmonary fibrosis (IPF) and fibrotic hypersensitivity pneumonitis (FHP) using cutting-edge single-cell profiling technologies. Methods: Single-cell RNA sequencing was performed on patients and healthy controls' peripheral blood and bronchoalveolar lavage samples using 10X Genomics 5' gene expression and V(D)J profiling. Cell type composition, transcriptional profiles, cellular trajectories and signaling, and T and B cell receptor repertoires were studied. The standard Seurat R pipeline was followed for cell type composition and differential gene expression analyses. Transcription factor activity was imputed using the DoRothEA-VIPER algorithm. Pseudotime analyses were conducted using Monocle3, while RNA velocity analyses were performed with Velocyto, scVelo, and CellRank. Cell-cell connectomics were assessed using the Connectome R package. V(D)J analyses were conducted using CellRanger and Immcantation frameworks. Across all analyses, disease group differences were assessed using the Wilcoxon rank-sum test. Measurements and Main Results: 327,990 cells from 83 samples were profiled. Overall, changes in monocytes were common to IPF and FHP, whereas lymphocytes exhibited disease-specific aberrations. Both diseases displayed enrichment of CCL3 hi /CCL4 hi CD14+ monocytes (p<2.2e-16) and S100A hi CD14+ monocytes (p<2.2e-16) versus controls. Trajectory and RNA velocity analysis suggested that pro-fibrotic macrophages observed in BAL originated from peripheral blood monocytes. Lymphocytes exhibited disease-specific aberrations, with CD8+ GZMK hi T cells and activated B cells primarily enriched in FHP patients. V(D)J analyses revealed unique T and B cell receptor complementarity-determining region 3 (CDR3) amino acid compositions (p<0.05) in FHP and significant IgA enrichment in IPF (p<5.2e-7). Conclusions: We identified common and disease-specific immune mechanisms in IPF and FHP; S100A hi monocytes and SPP1 hi macrophages are common to IPF and FHP, whereas GMZK hi T lymphocytes and T and B cell receptor repertoires were unique in FHP. Our findings open novel strategies for the diagnosis and treatment of IPF and FHP.

Computer-Aided Pulmonary Fibrosis Detection Leveraging an Advanced Artificial Intelligence Triage and Notification Software.

Background: Improvement in recognition and referral of pulmonary fibrosis (PF) is vital to improving patient outcomes within interstitial lung disease. We determined the performance metrics and processing time of an artificial intelligence triage and notification software, ScreenDx-LungFibrosis™, developed to improve detection of PF. Methods: ScreenDx-LungFibrosis™ was applied to chest computed tomography (CT) scans from multisource data. Device output (+/- PF) was compared to clinical diagnosis (+/- PF), and diagnostic performance was evaluated. Primary endpoints included device sensitivity and specificity > 80% and processing time < 4.5 min. Results: Of 3,018 patients included, PF was present in 22.9%. ScreenDx-LungFibrosis™ detected PF with a sensitivity and specificity of 91.3% (95% confidence interval (CI): 89.0-93.3%) and 95.1% (95% CI: 94.2-96.0%), respectively. Mean processing time was 27.6 s (95% CI: 26.0 - 29.1 s). Conclusions: ScreenDx-LungFibrosis™ accurately and reliably identified PF with a rapid per-case processing time, underscoring its potential for transformative improvement in PF outcomes when routinely applied to chest CTs.