Single Cell Analysis Reveals Novel Immune Perturbations in Fibrotic Hypersensitivity Pneumonitis.

Rationale: Fibrotic hypersensitivity pneumonitis is a debilitating interstitial lung disease driven by incompletely understood immune mechanisms. Objectives: To elucidate immune aberrations in fibrotic hypersensitivity pneumonitis in single-cell resolution. Methods: Single-cell 5' RNA sequencing was conducted on peripheral blood mononuclear cells and bronchoalveolar lavage cells obtained from 45 patients with fibrotic hypersensitivity pneumonitis, 63 idiopathic pulmonary fibrosis, 4 non-fibrotic hypersensitivity pneumonitis, and 36 healthy controls in the United States and Mexico. Analyses included differential gene expression (Seurat), transcription factor activity imputation (DoRothEA-VIPER), and trajectory analyses (Monocle3/Velocyto-scVelo-CellRank). Measurements and Main Results: Overall, 501,534 peripheral blood mononuclear cells from 110 patients and controls and 88,336 bronchoalveolar lavage cells from 19 patients were profiled. Compared to controls, fibrotic hypersensitivity pneumonitis has elevated classical monocytes (adjusted-p=2.5e-3) and are enriched in CCL3hi/CCL4hi and S100Ahi classical monocytes (adjusted-p<2.2e-16). Trajectory analyses demonstrate that S100Ahi classical monocytes differentiate into SPP1hi lung macrophages associated with fibrosis. Compared to both controls and idiopathic pulmonary fibrosis, fibrotic hypersensitivity pneumonitis patient cells are significantly enriched in GZMhi cytotoxic T cells. These cells exhibit transcription factor activities indicative of TGFß and TNFα/NFκB pathways. These results are publicly available at https://ildimmunecellatlas.org. Conclusions: Single-cell transcriptomics of fibrotic hypersensitivity pneumonitis patients uncovered novel immune perturbations, including previously undescribed increases in GZMhi cytotoxic CD4+ and CD8+ T cells - reflecting this disease's unique inflammatory T-cell driven nature - as well as increased S100Ahi and CCL3hi/CCL4hi classical monocytes also observed in idiopathic pulmonary fibrosis. Both cell populations may guide the development of new biomarkers and therapeutic interventions.

Participant flow diagrams for health equity in AI.

Selection bias can arise through many aspects of a study, including recruitment, inclusion/exclusion criteria, input-level exclusion and outcome-level exclusion, and often reflects the underrepresentation of populations historically disadvantaged in medical research. The effects of selection bias can be further amplified when non-representative samples are used in artificial intelligence (AI) and machine learning (ML) applications to construct clinical algorithms. Building on the "Data Cards" initiative for transparency in AI research, we advocate for the addition of a participant flow diagram for AI studies detailing relevant sociodemographic and/or clinical characteristics of excluded participants across study phases, with the goal of identifying potential algorithmic biases before their clinical implementation. We include both a model for this flow diagram as well as a brief case study explaining how it could be implemented in practice. Through standardized reporting of participant flow diagrams, we aim to better identify potential inequities embedded in AI applications, facilitating more reliable and equitable clinical algorithms.

Risk Factors for Recurrent Exacerbations in the General-Practitioner-Based Swiss Chronic Obstructive Pulmonary Disease (COPD) Cohort.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often suffer from acute exacerbations. Our objective was to describe recurrent exacerbations in a GP-based Swiss COPD cohort and develop a statistical model for predicting exacerbation. METHODS: COPD cohort demographic and medical data were recorded for 24 months, by means of a questionnaire-based COPD cohort. The data were split into training (75%) and validation (25%) datasets. A negative binomial regression model was developed using the training dataset to predict the exacerbation rate within 1 year. An exacerbation prediction model was developed, and its overall performance was validated. A nomogram was created to facilitate the clinical use of the model. RESULTS: Of the 229 COPD patients analyzed, 77% of the patients did not experience exacerbation during the follow-up. The best subset in the training dataset revealed that lower forced expiratory volume, high scores on the MRC dyspnea scale, exacerbation history, and being on a combination therapy of LABA + ICS (long-acting beta-agonists + Inhaled Corticosteroids) or LAMA + LABA (Long-acting muscarinic receptor antagonists + long-acting beta-agonists) at baseline were associated with a higher rate of exacerbation. When validated, the area-under-curve (AUC) value was 0.75 for one or more exacerbations. The calibration was accurate (0.34 predicted exacerbations vs 0.28 observed exacerbations). CONCLUSION: Nomograms built from these models can assist clinicians in the decision-making process of COPD care.