Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE)

Plasma IgG Glycosylation Profiling Reveals the Biological Features of Early Chronic Obstructive Pulmonary Disease

Chronic inflammatory and immune dysregulation are critical drivers of the development and progression of chronic obstructive pulmonary disease (COPD). Posttranslational modifications, such as glycosylation of Immunoglobulin G (IgG), are crucial in modulating systemic inflammatory homeostasis. This study aims to profile plasma IgG glycopeptides (IgGPs) in COPD patients to uncover new insights into their pathogenesis and to identify novel biomarkers. An integrated platform that combines Fe3O4@PDA@DETA nanospheres enrichment with high-resolution mass spectrometry measurement was employed to analyze plasma IgG N-glycopeptides from 90 COPD patients, 45 clinically defined early COPD (CECOPD) patients, and 90 healthy individuals. To explore the underlying mechanism of COPD progression, correlations between IgG N-glycoforms and clinical parameters were assessed. Disease-specific IgGPs were identified in both the ECOPD and COPD cohorts. Notably, it was the IgG glycopattern, rather than the IgG levels themselves, that underwent changes as the disease progressed. In early COPD patients, there was a decrease in bisection, accompanied by an increase in site-specific afucosylated galactosylation and fucosylation of IgG, indicating an anti-inflammatory state. Conversely, in COPD patients, an increase in inflammation was observed, which was characterized by reduced galactosylation and sialylation. Interestingly, a subset of healthy controls displayed IgGP patterns similar to those of early COPD, possibly reflecting the impact of smoking and the associated immune responses. We finally identified 6 anti-inflammatory and 2 pro-inflammatory IgGPs as ECOPD-specific IgGP indicators. Collectively, these findings suggest that plasma IgG glycosylation holds great potential as a biomarker for early COPD diagnosis, providing valuable insights into the immune system changes during disease progression. The raw data files are publicly accessible via the ProteomeXchange Consortium with the identifier PXD056374.

Analysis of research trends and hot spots on COPD biomarkers from the perspective of bibliometrics

BACKGROUND

Chronic obstructive pulmonary disease (COPD), a chronic respiratory condition with airflow limitation, is the fourth leading global cause of death. Biomarkers are key for classifying COPD, detecting exacerbations, guiding treatment, and prognosis. This article uses bibliometrics and visualization to analyze COPD biomarker research trends, providing insights for future studies.

METHODS

This study adopts a range of literature analysis tools, including HistCite, VOSviewer, and CiteSpace, to systematically analyze literature on COPD biomarkers within the Web of Science Core Collection database from 2005 to 2024.

RESULTS

A total of 1835 papers or reviews related to COPD biomarkers are included in this study. Since 2003, the number of publications in this field has been on an upward trajectory. The United States being most influential in this field (n = 415, TLCS = 2319). Prominent institutions such as the University of British Columbia consistently deliver high-quality research results. Tal-Singer R, Sin DD, and Vestbo J have made significant contributions to COPD biomarker research. The journal American Journal of Respiratory and Critical Care Medicine is the most authoritative choice for researchers in the field.This research has long focused on biomarkers associated with the inflammatory response (C-reactive protein, eosinophils, etc.), pulmonary function, induced sputum, and computed tomography. Looking ahead, biomarkers such as microRNA, exosomes, DNA methylation, and microbiomics are likely to become popular topics, particularly regarding their roles in the prognosis and mechanisms of COPD.

CONCLUSION

Bibliometric analysis suggests that future research on COPD biomarkers will focus on advanced fields, such as microRNA, exosomes, DNA methylation, and microbiomics.

Automated CT-based measurements of radial and longitudinal expansion of airways due to breathing-related lung volume change

BACKGROUND

Respiratory function is impaired in chronic obstructive pulmonary disease (COPD). Automation of multi-volume CT-based measurements of different components of breathing-related airway deformations will help understand multi-pathway impairments in respiratory mechanics in COPD.

PURPOSE

To develop and evaluate multi-volume chest CT-based automated measurements of breathing-related radial and longitudinal expansion of individual airways between inspiratory and expiratory lung volumes.

METHODS

We developed a method to compute breathing-related airway deformation metrics and applied it to total lung capacity (TLC) and functional residual capacity (FRC) chest CT scans. The computational pipeline involves: (1) segmentation of airways; (2) skeletonization of airways; (3) labeling of anatomical airway segments at TLC and FRC; and (4) computation of radial and longitudinal expansion metrics of individual airways across lung volumes. Radial expansion (∆CSA) of an airway is computed as the percent change of its cross-sectional area (CSA) between two lung volumes. Longitudinal expansion (∆L) of an airway is computed as the percent change in its airway path-length from the carina between lung volumes. These measures are summarized at different airway anatomic generations. Agreement of automated measures with their manually derived values was examined in terms of concordance correlation coefficient (CCC) of automated measures with those derived using manual outlining. Intra-class correlation coefficient (ICC) of automated measures from repeat CT scans (n = 37) was computed to assess repeatability. The method was also applied to a set of participants from the Genetic Epidemiology of COPD (COPDGene) Iowa cohort, distributed across COPD severity groups (n = 4 × 60).

RESULTS

The CCC values for the automated ∆CSA measure with manually derived values were 0.930 at the trachea, 0.898 at primary bronchi, and greater than 0.95 at pre-segmental and segmental airways; these CCC values were consistently greater than 0.95 for ∆L at all airway generations. ICC values for repeatability of ∆CSA were 0.974, 0.950, 0.943, and 0.901 at trachea, primary bronchi, pre-segmental, and segmental airways, respectively; these ICC values for ∆L were 0.973, 0.954, and 0.952 at primary bronchi, pre-segmental, and segmental airways, respectively. ∆CSA values were significantly reduced (p < 0.001) with increasing COPD severity at each of primary bronchi, pre-segmental, and segmental airways. Significantly lower ∆L values were observed for moderate (p = 0.042 at pre-segmental and p = 0.037 at segmental) and severe (p = 0.019 at pre-segmental and p < 0.001 at segmental) COPD groups as compared to the preserved lung function group. Body mass index (BMI) and smoking status were found to significantly associate with ∆CSA at segmental airways (r = 0.17 and -0.19, respectively; significance threshold = 0.13), while age and sex were significantly associated with ∆L (r = -0.21 and -0.17, respectively); COPD severity was significantly associated with both ∆CSA and ∆L (r = -0.35 and -0.22, respectively).

CONCLUSION

Our CT-based automated measures of breathing-related radial and longitudinal expansion of airways are repeatable and in agreement with manually derived values. Automation of different airway mechanical biomarkers and their observed significant associations with age, sex, BMI, smoking, and COPD severity establish an effective tool to investigate multi-pathway impairments of respiratory mechanics in COPD and other lung diseases.

Pulmonary outcomes of incretin-based therapies in COPD patients receiving single-inhaler triple therapy

Background

Patients with COPD on triple therapy often face exacerbations and comorbidities. Emerging evidence suggests that glucagon-like peptide-1 (GLP-1) analogues may reduce the risk of exacerbation in patients with COPD and type 2 diabetes mellitus (T2DM). This study investigates the impact of GLP-1 analogues on pulmonary outcomes in patients with COPD on single-inhaler triple therapy (SITT) and T2DM.

Methods

We conducted a retrospective cohort study using the TriNetX database and analysed adult patients with COPD and T2DM who received SITT between April 2005 and July 2023. Patients were categorised into GLP-1 analogue and dipeptidyl peptidase-4 inhibitor (DPP4i) cohorts. The primary efficacy outcome was COPD exacerbation, and the secondary efficacy outcomes were pneumonia, acute respiratory distress syndrome, intubation, oxygen dependence and all-cause mortality. The secondary outcomes were serious gastrointestinal adverse events.

Results

We included 6898 patients, with 4184 receiving GLP-1 analogues and 2714 receiving DPP4i. After matching, 1751 GLP-1 analogue users were matched with 1751 DPP4i users. GLP-1 analogue users had an 18% lower risk of COPD exacerbation (hazard ratio (HR) 0.82 (95% CI 0.71-0.94)), a 28% reduced risk of pneumonia (HR 0.72 (95% CI 0.61-0.85)), a 34% reduced risk of oxygen dependence (HR 0.66 (95% CI 0.47-0.91)) and a 40% decreased risk of all-cause mortality (HR 0.60 (95% CI 0.47-0.77)). No significant serious gastrointestinal adverse events were observed.

Conclusion

GLP-1 analogues may be associated with reduced COPD exacerbations, pulmonary comorbidities and mortality in patients with COPD receiving SITT and T2DM, with no significant serious gastrointestinal safety concerns.

Is Disease Stability an Attainable Chronic Obstructive Pulmonary Disease Treatment Goal?

Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition characterized by progressive airflow obstruction. Despite advancements in diagnosis and treatment, the disease burden remains high; although clinical trials have shown improvements in outcomes such as exacerbations, quality of life, and lung function, improvement may not be attainable for many patients. For patients who do experience improvement, it is challenging to set management goals given the progressive nature of COPD. We therefore propose disease stability as an appropriate and attainable treatment goal. Other disease areas have developed definitions of no disease activity or remission, which provide relevant information for defining and achieving stability for patients with COPD. Disease stability builds on related concepts already defined in COPD, such as clinical control and clinically important deterioration. Current components that could form part of a disease stability definition include exacerbations, health status (including quality of life and symptoms), and lung function. Considerations should be given to intervals over which stability is defined and assessed, appropriate thresholds, and defining a composite. Ensuring a holistic approach, objective measurements, and harmonious, clear communication between patients and physicians can further support establishing disease stability. Here we propose a preliminary definition of disease stability, informed by existing research in COPD. Further research will be needed to validate the framework for use in clinical and research settings. Exploring disease stability as a goal, however, is an opportunity to develop and validate an attainable treatment target to advance the standard of care for patients with COPD.

Bronchodilator reversibility and eosinophilic biomarkers in chronic obstructive pulmonary disease patients

BACKGROUND AND OBJECTIVES

Chronic Obstructive Pulmonary Disease (COPD) is now one of the top three causes of death worldwide. Recently, increased focus has been on COPD patients displaying eosinophilic inflammation and asthma-like features of bronchial hyperreactivity and bronchodilator responsiveness. The objective of the study was to measure the proportion of chronic obstructive pulmonary disease patients with bronchodilator reversibility and to compare the eosinophilic biomarkers between the bronchodilator non-reversible and reversible groups.

MATERIALS AND METHODS

This hospital-based cross-sectional study included COPD patients who visited the Pulmonary Medicine OPD at the All-India Institute of Medical Sciences, Patna. Spirometry and eosinophilic biomarkers such as blood eosinophil, sputum eosinophil, FeNO, and serum IL-5 were measured. All statistical calculations were conducted using SPSS (Statistical Package for the Social Science 22 version (SPSS, Chicago, IL, United States).

RESULTS

A total of 160 COPD patients were included in the study. The mean age of the study population was 61 (±10) years. Males (68.1%) and non-smokers (55%) respectively were predominant. The prevalence of bronchodilator reversibility was found to be 32%. There was a significant difference in eosinophil biomarker levels, of sputum eosinophil count, peripheral eosinophil count, and FeNO levels between the bronchodilator non-reversible and bronchodilator reversible groups. Serum IL-5 levels were higher and more significant in GOLD group D patients.

CONCLUSION

Eosinophils are crucial to the underlying inflammatory response in this subset of COPD patients, as evidenced by the observation that eosinophil biomarkers were significantly higher in COPD patients with bronchodilator reversibility. Also, sputum eosinophil levels had a better correlation in comparison to peripheral eosinophil level in this subset.

Novel risk loci encompassing genes influencing STAT3, GPCR, and oxidative stress signaling are associated with co-morbid GERD and COPD

Chronic obstructive pulmonary disease (COPD) is a leading cause of death globally. Gastroesophageal reflux disease (GERD) is a common comorbidity in COPD associated with worse pulmonary symptoms, reduced quality of life, and increased exacerbations and hospitalizations. GERD treatment in COPD is associated with a lower risk of exacerbations and mortality; however, it is not clear whether these findings can be attributed to aging populations where both diseases are likely to co-occur or reflect shared etiology. To test for the influence of common etiology in both diseases, we aimed to identify shared genetic etiology between GERD and COPD. We performed the first whole-genome sequence association analysis of comorbid GERD and COPD in 12,438 multi-ancestry participants. The co-heritability of GERD and COPD was 39.7% (h2 = 0.397, SE = 0.074) and we identified several ancestry-independent loci associated with co-morbid GERD and COPD (within LINC02493 and FRYL) known to be involved in oxidative stress and G protein-coupled receptor (GPCR) signaling mechanisms. We found several loci associated with co-morbid GERD and COPD previously associated with GERD or COPD individually, including HCG17, which plays a role in oxidative stress mechanisms. Gene set enrichment identified GPCR signaling pathways in co-morbid GERD and COPD loci. Rare variants in ZFP42, encoding key regulators of the IL6/STAT3 pathway, have been previously implicated with GI disorders and were associated with co-morbid GERD and COPD. We identified common genetic etiology for GERD in COPD which begins to provide a mechanistic foundation for the potential therapeutic utility of STAT3, oxidation, and GPCR signaling pathway modulators in both GERD and COPD.

Novel Lobe-based Transformer model (LobTe) to predict emphysema progression in Alpha-1 Antitrypsin Deficiency

Emphysema, marked by irreversible lung tissue destruction, poses challenges in progression prediction due to its heterogeneity. Early detection is particularly critical for patients with Alpha-1 Antitrypsin Deficiency (AATD), a genetic disorder reducing ATT protein levels. Heterozygous carriers (PiMS and PiMZ) have variable AAT levels thus complicating their prognosis. This study introduces a novel prognostic model, the Lobe-based Transformer encoder (LobTe), designed to predict the annual change in lung density (ΔALD [g/L-yr]) using CT scans. Utilizing a global self-attention mechanism, LobTe specifically analyzes lobar tissue destruction to forecast disease progression. In parallel, we developed and compared a second model utilizing an LSTM architecture that implements a local subject-specific attention mechanism. Our methodology was validated on a cohort of 2,019 participants from the COPDGene study. The LobTe model demonstrated a small root mean squared error (RMSE=1.73 g/L-yr) and a notable correlation coefficient (ρ=0.61), explaining over 35% of the variability in ΔALD (R2= 0.36). Notably, it achieved a higher correlation coefficient of 0.68 for PiMZ heterozygous carriers, indicating its effectiveness in detecting early emphysema progression among smokers with mild to moderate AAT deficiency. The presented models could serve as a tool for monitoring disease progression and informing treatment strategies in carriers and subjects with AATD. Our code is available at github.com/acil-bwh/LobTe.

Optimizing convolutional neural networks for Chronic Obstructive Pulmonary Disease detection in clinical computed tomography imaging

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7194 contrast-enhanced CT images (3597 with COPD; 3597 healthy controls) from 78 subjects were selected retrospectively (01.2018-12.2021) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline 'full-range' window setting. Class-balanced train, validation, and test sets contained 3392, 1114, and 2688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] was utilized to compare model variations. Repeated inference (n = 7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89]. By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range.

NTHi killing activity is reduced in COPD patients and is associated with a differential microbiome

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease characterized by airway obstruction and inflammation. Non-typeable Haemophilus influenzae (NTHi) lung infections are common in COPD, promoting frequent exacerbations and accelerated lung function decline. The relationship with immune responses and NTHi are poorly understood. Herein, we comprehensively characterized the respiratory microbiome and mycobiome of patients while investigating microbial dynamics and host immune changes attributable to NTHi killing activity. Mild-to-moderate COPD patients encompassing frequent and infrequent exacerbators and healthy volunteers (HV) were enrolled. Microbial composition, proteomics and NTHi killing activity was analyzed using bronchoalveolar lavage fluid (BALF). In addition, antigen-antibody titers in sera to COPD pathogens were determined using a multiplex assay. Differential abundance analysis revealed an enrichment of Actinobacteria and Bacteroidetes in the BALF of COPD and HV subjects respectively. Significant differences in the IgA titer response were observed against NTHi antigens in COPD vs. HV. Notably, there was also significantly greater killing activity against NTHi in BALF from COPD vs. HV subjects (OR = 5.64; 95% CI = 1.75-20.20; p = 0.001). Stratification of COPD patients by NTHi killing activity identified unique microbial and protein signatures wherein Firmicutes, Actinobacteria and haptoglobin were enriched in patients with killing activity. We report that differences in host immune responses and NTHi-killing activity are associated with microbiome changes in mild-to-moderate COPD. This is suggestive of a potential link between the respiratory microbiome and immune activity against NTHi in the context of COPD pathogenesis even at this disease stage.

The use of the Prospector calculator reduces antibiotic therapy in exacerbations of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) exacerbations frequently cause patient consultations in both out- and inpatient settings. Recent data suggest that only 40-60% of exacerbations are of bacterial origin and mandate antibiotic treatment. However, a reliable tool to justify prescribing antibiotics for COPD exacerbation is still lacking. This study was designed to explore the hypothesis that utilization of a novel decision-making tool called Prospector would lead to lower consumption of antibiotics and provide a more rational approach to managing COPD exacerbations versus standard therapy in patients with COPD. The study included 77 COPD patients who experienced a COPD exacerbation and were treated in outpatient settings. The Prospector group (PG) (n = 40) were treated by the study author using the Prospector calculator (a tool designed by the first author that translates: patient symptoms, exacerbation, and medical history of COPD into a decision on the use of antibiotics in COPD exacerbation treatment). Other primary care specialists treated the control group (CG) (n = 37) in the same outpatient clinic; antibiotic therapies were implemented at the physician's discretion, most often using Anthonisen's criteria. All other medications were administered at the physician's discretion. Safety endpoints were set as: death, hospitalization, and number of exacerbations. Antibiotics were administered in 32.8% and 81.2% of exacerbations in the PG and CG, respectively (p < 0.0001). A comparable percentage was verified positively in both PG patient subsets: those that did and did not receive antibiotics at visit 1 (94.7% and 94.9%, respectively). Twenty-eight patients in the PG and 37 in the CG were followed for up to 35 months. Failure to recover (defined as deterioration or lack of improvement) in 30 days following exacerbation was 10.7% in the PG and 47.2% in the CG. In the CG, the failure rate was significantly higher (p = 0.0043). Hospitalization rates in the PG and the CG were 42.9% and 94.4%, respectively. In the CG, the hospitalization rate was significantly higher (p < 0.0001). COPD hospitalization rates in the PG and the CG were 17.9% and 33.3%, respectively (p = 0.1643). This preliminary study suggests that using the Prospector calculator results in markedly reduced antibiotic prescription for COPD exacerbations. No new safety signals have been identified for the method.

GOLD grade-specific characterization of COPD in the COSYCONET multi-center trial: comparison of semiquantitative MRI and quantitative CT

OBJECTIVES

We hypothesized that semiquantitative visual scoring of lung MRI is suitable for GOLD-grade specific characterization of parenchymal and airway disease in COPD and that MRI scores correlate with quantitative CT (QCT) and pulmonary function test (PFT) parameters.

METHODS

Five hundred ninety-eight subjects from the COSYCONET study (median age = 67 (60-72)) at risk for COPD or with GOLD1-4 underwent PFT, same-day paired inspiratory/expiratory CT, and structural and contrast-enhanced MRI. QCT assessed total lung volume (TLV), emphysema, and air trapping by parametric response mapping (PRMEmph, PRMfSAD) and airway disease by wall percentage (WP). MRI was analyzed using a semiquantitative visual scoring system for parenchymal defects, perfusion defects, and airway abnormalities. Descriptive statistics, Spearman correlations, and ANOVA analyses were performed.

RESULTS

TLV, PRMEmph, and MRI scores for parenchymal and perfusion defects were all higher with each GOLD grade, reflecting the extension of emphysema (all p < 0.001). Airway analysis showed the same trends with higher WP and higher MRI large airway disease scores in GOLD3 and lower WP and MRI scores in GOLD4 (p = 0.236 and p < 0.001). Regional heterogeneity was less evident on MRI, while PRMEmph and MRI perfusion defect scores were higher in the upper lobes, and WP and MRI large airway disease scores were higher in the lower lobes. MRI parenchymal and perfusion scores correlated moderately with PRMEmph (r = 0.61 and r = 0.60) and moderately with FEV1/FVC (r = -0.56).

CONCLUSION

Multi-center semiquantitative MRI assessments of parenchymal and airway disease in COPD matched GOLD grade-specific imaging features on QCT and detected regional disease heterogeneity. MRI parenchymal disease scores were correlated with QCT and lung function parameters.

KEY POINTS

Question Do MRI-based scores correlate with QCT and PFT parameters for GOLD-grade specific disease characterization of COPD? Findings MRI can visualize the parenchymal and airway disease features of COPD. Clinical relevance Lung MRI is suitable for GOLD-grade specific disease characterization of COPD and may serve as a radiation-free imaging modality in scientific and clinical settings, given careful consideration of its potential and limitations.

Contrasting the clinical and biological characteristics of young and old COPD patients

Background

The ECLIPSE study was a large, international, prospective, controlled, observational study that included COPD patients (Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 2-4), as well as smoking and non-smoking participants with normal spirometry, aged 40-75 years, who were followed-up regularly for 3 years. Here we sought to contrast the clinical and biological characteristics of young COPD versus controls of similar age and older COPD patients included in ECLIPSE.

Methods

We compared 106 young (<50 years) and 488 old (>70 years) COPD patients, as well as 119 young smokers and 92 nonsmoker controls (<50 years) with normal spirometry.

Results

Young COPD patients: 1) were more symptomatic than young controls, often reported a family history of chronic bronchitis, emphysema and asthma, as well as a personal history of asthma and bronchitis, and suffered from a similar disease burden to older patients; 2) were at higher risk of substantial forced expiratory volume in 1 s decline over time; and 3) had reduced serum levels of CC16 (a lung-derived anti-inflammatory protein that relates to lung damage) and, at the same time, reduced pro-inflammatory markers compared to older COPD patients.

Conclusions

Young COPD patients suffer from significant disease burden, display an altered biomarker and disease progression profile reflected by an accelerated risk of lung function decline highlighting the need for early life diagnosis, prevention approaches and treatment.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies

BACKGROUND

Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

METHODS

We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups.

FINDINGS

We examined two high-risk omics-defined groups in non-overlapping test sets (n = 1133 NHW COPDGene, n = 299 African American (AA) COPDGene, n = 468 ECLIPSE). We defined "high activity" (low PRS, high TRS) and "severe risk" (high PRS, high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signalling processes compared to a low-risk (low PRS, low TRS) subgroup. "High activity" but not "severe risk" participants had greater prospective FEV1 decline (COPDGene: -51 mL/year; ECLIPSE: -40 mL/year) and proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors.

INTERPRETATION

Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

FUNDING

National Institutes of Health.

Multi-ancestry genome-wide association study reveals novel genetic signals for lung function decline

Rationale

Accelerated decline in lung function contributes to the development of chronic respiratory disease. Despite evidence for a genetic component, few genetic associations with lung function decline have been identified.

Objectives

To evaluate genome-wide associations and putative downstream functionality of genetic variants with lung function decline in diverse general population cohorts.

Methods

We conducted genome-wide association study (GWAS) analyses of decline in the forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), and their ratio (FEV1/FVC) in participants across six cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. Genotypes were imputed to TOPMed (CHARGE cohorts) or Haplotype Reference Consortium (HRC) (UK Biobank) reference panels, and GWAS analyses used generalized estimating equation models with robust standard error. Models were stratified by cohort, ancestry, and sex, and adjusted for important lung function confounders and genotype principal components. Results were combined in cross-ancestry and ancestry-specific meta-analyses. Selected top variants were tested for replication in two independent COPD-enriched cohorts.

Measurements and Main Results

Our discovery analyses included 52,056 self-reported White (N=44,988), Black (N=5,788), Hispanic (N=550), and Chinese American (N=730) participants with a mean of 2.3 spirometry measurements and 8.6 years of follow-up. Functional mapping of GWAS meta-analysis results identified 361 distinct genome-wide significant (p<5E-08) variants in one or more of the FEV1, FVC, and FEV1/FVC decline phenotypes, which overlapped with previously reported genetic signals for several related pulmonary traits. Of these, 8 variants, or 20.5% of the variant set available for replication testing, were nominally associated (p<0.05) with at least one decline phenotype in COPD-enriched cohorts (White [N=4,778] and Black [N=1,118]). Using the GWAS results, gene-level analysis implicated 38 genes, including eight (XIRP2, GRIN2D, SATB1, MARCHF4, SIPA1L2, ANO5, H2BC10, and FAF2) with consistent associations across ancestries or decline phenotypes. Annotation class analysis revealed significant enrichment of several regulatory processes for corticosteroid biosynthesis and metabolism. Drug repurposing analysis identified 43 approved compounds targeting eight of the implicated 38 genes.

Conclusions

Our multi-ancestry GWAS meta-analyses identified numerous genetic loci associated with lung function decline. These findings contribute knowledge to the genetic architecture of lung function decline, provide evidence for a role of endogenous corticosteroids in the etiology of lung function decline, and identify drug targets that merit further study for potential repurposing to slow lung function decline and treat lung disease.

Stakeholder-informed positivity thresholds for disease markers and risk scores: a methodological framework and an application in obstructive lung disease

OBJECTIVES

A positivity threshold is often applied to markers or predicted risks to guide disease management. These thresholds are often decided exclusively by clinical experts despite being sensitive to the preferences of patients and general public as ultimate stakeholders.

STUDY DESIGN AND SETTING

We propose an analytical framework for quantifying the net benefit (NB) of an evidence-based positivity threshold based on combining preference-sensitive (eg, how individuals weight benefits and harms of treatment) and preference-agnostic (eg, the magnitude of benefit and the risk of harm) parameters. We propose parsimonious choice experiments to elicit preference-sensitive parameters from stakeholders, and targeted evidence synthesis to quantify the value of preference-agnostic parameters. We apply this framework to maintenance of azithromycin therapy for chronic obstructive pulmonary disease using a discrete choice experiment to estimate preference weights for attribute level associated with treatment. We identify the positivity threshold on 12-month moderate or severe exacerbation risk that would maximize the NB of treatment in terms of severe exacerbations avoided.

RESULTS

In the case study, the prevention of moderate and severe exacerbations (benefits) and the risk of hearing loss and gastrointestinal symptoms (harms) emerged as important attributes. Four hundred seventy seven respondents completed the discrete choice experiment survey. Relative to each percent risk of severe exacerbation, preference weights for each percent risk of moderate exacerbation, hearing loss, and gastrointestinal symptoms were 0.395 (95% confidence interval [CI] 0.338-0.456), 1.180 (95% CI 1.071-1.201), and 0.253 (95% CI 0.207-0.299), respectively. The optimal threshold that maximized NB was to treat patients with a 12-month risk of moderate or severe exacerbations ≥12%.

CONCLUSION

The proposed methodology can be applied to many contexts where the objective is to devise positivity thresholds that need to incorporate stakeholder preferences. Applying this framework to chronic obstructive pulmonary disease pharmacotherapy resulted in a stakeholder-informed treatment threshold that was substantially lower than the implicit thresholds in contemporary guidelines.

An integrative network-based approach to identify driving gene communities in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is an etiologically complex disease characterized by acute exacerbations and stable phases. We aimed to identify biological functions modulated in specific COPD conditions, using whole blood samples collected in the AERIS clinical study (NCT01360398). Considered conditions were exacerbation onset, severity of airway obstruction, and presence of respiratory pathogens in sputum samples. With an integrative multi-network gene community detection (MNGCD) approach, we analyzed expression profiles to identify communities of correlated genes. The approach combined different layers of gene interactions for each explored condition/subset of samples: gene expression similarity, protein-protein interactions, transcription factors, and microRNAs validated regulons. Heme metabolism, interferon-alpha, and interferon-gamma pathways were modulated in patients at both exacerbation and stable-state visits, but with the involvement of distinct sets of genes. An important gene community was enriched with G2M checkpoint, E2F targets, and mitotic spindle pathways during exacerbation. Targets of TAL1 regulator and hsa-let-7b - 5p microRNA were modulated with increasing severity of airway obstruction. Bacterial infections with Moraxella catarrhalis and, particularly, Haemophilus influenzae triggered a specific cellular and inflammatory response in acute exacerbations, indicating an active reaction of the host to infections. In conclusion, COPD is a complex multifactorial disease that requires in-depth investigations of its causes and features during its evolution and whole blood transcriptome profiling can contribute to capturing some relevant regulatory mechanisms associated with this disease. In this work, we explored multi-network modeling that integrated diverse layers of regulatory gene networks and enhanced our comprehension of the biological functions implicated in the COPD pathogenesis.

Genetically Predicted Body Mass Index and Mortality in Chronic Obstructive Pulmonary Disease

Rationale: Body mass index (BMI) is associated with chronic obstructive pulmonary disease (COPD) mortality, but the underlying mechanisms are unclear. The effect of genetic variants aggregated into a polygenic score may elucidate the causal mechanisms and predict risk. Objectives: To examine the associations of genetically predicted BMI with all-cause and cause-specific mortality in COPD. Methods: We developed a polygenic score (PGS) for BMI (PGSBMI) and tested for associations of the PGSBMI with all-cause, respiratory, and cardiovascular mortality in participants with COPD from the COPDGene (Genetic Epidemiology of COPD), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points), and Framingham Heart studies. We calculated the difference between measured BMI and PGS-predicted BMI (BMIdiff) and categorized participants into groups of discordantly low (BMIdiff <20th percentile), concordant (BMIdiff between the 20th and 80th percentiles), and discordantly high (BMIdiff >80th percentile) BMI. We applied Cox models, examined potential nonlinear associations of the PGSBMI and BMIdiff with mortality, and summarized results with meta-analysis. Measurements and Main Results: We observed significant nonlinear associations of measured BMI and BMIdiff, but not PGSBMI, with all-cause mortality. In meta-analyses, a one-standard deviation increase in the PGSBMI was associated with an increased hazard for cardiovascular mortality (hazard ratio [HR], 1.29; 95% confidence interval [CI], 1.12-1.49), but not for respiratory or all-cause mortality. Compared with participants with concordant measured and genetically predicted BMI, those with discordantly low BMI had higher risks for all-cause mortality (HR, 1.57; 95% CI, 1.41-1.74) and respiratory death (HR, 2.01; 95% CI, 1.61-2.51). Conclusions: In people with COPD, a higher genetically predicted BMI is associated with higher cardiovascular mortality but not respiratory mortality. Individuals with a discordantly low BMI have higher all-cause and respiratory mortality rates than those with a concordant BMI.

Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling

Background

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality.

Methods

In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45-80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45-80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE.

Findings

Graphical models, applied on the COPDGene Phase 1 samples (n = 8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n = 3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n = 2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions.

Interpretation

Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them.

Funding

This research was supported by NIH [NHLBI, NLM]. The COPDGene study is supported by the COPD Foundation, through grants from AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion.

The long-term clinical and economic benefits of treating advanced COPD patients with single-inhaler triple therapy in Quebec, Canada - The IMPACT trial

BACKGROUND

This cost-utility analysis assessed the long-term clinical and economic benefits of fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) triple therapy vs FF/VI or UMEC/VI from a Quebec societal perspective in patients with chronic obstructive pulmonary disease (COPD) with ≥1 moderate/severe exacerbation in the previous year.

METHODS

The validated GALAXY disease progression model was utilized, with parameters set to baseline and efficacy data from IMPACT. Treatment costs (2017 Canadian dollars [C$]) were estimated using Quebec-specific unit costs. Costs and health outcomes were discounted at 1.5 %/year. A willingness-to-pay threshold of C$50,000/quality-adjusted life year (QALY) was considered cost-effective. Outcomes modeled were exacerbation rates, QALYs, life years (LYs), costs and incremental cost-effectiveness ratios (ICERs). Subgroup analyses were performed according to prior treatment, exacerbation history in the previous year, and baseline lung function.

RESULTS

Over a lifetime horizon, FF/UMEC/VI resulted in more QALYs and LYs gained, at a small incremental cost compared with FF/VI and UMEC/VI. From a societal perspective, the estimated ICER for the base case was C$18,152/QALY vs FF/VI, and C$15,847/QALY vs UMEC/VI. For the subgroup analyses (FF/UMEC/VI compared with FF/VI and UMEC/VI), ICERs ranged from: C$17,412-25,664/QALY and C$16,493-18,663/QALY (prior treatment); C$15,247-19,924/QALY and C$15,444-28,859/QALY (exacerbation history); C$14,025-34,154/QALY and C$16,083-17,509/QALY (baseline lung function).

INTERPRETATION

FF/UMEC/VI was predicted to improve outcomes and be cost-effective vs both comparators in the base case and all subgroup analyses, and based on this analysis would be an appropriate investment of health service funds in Quebec.

CLINICAL TRIAL REGISTRATION NUMBER

IMPACT trial NCT02164513.

Phenotyping of COPD with MRI in comparison to same-day CT in a multi-centre trial

OBJECTIVES

A prospective, multi-centre study to evaluate concordance of morphologic lung MRI and CT in chronic obstructive pulmonary disease (COPD) phenotyping for airway disease and emphysema.

METHODS

A total of 601 participants with COPD from 15 sites underwent same-day morpho-functional chest MRI and paired inspiratory-expiratory CT. Two readers systematically scored bronchial wall thickening, bronchiectasis, centrilobular nodules, air trapping and lung parenchyma defects in each lung lobe and determined COPD phenotype. A third reader acted as adjudicator to establish consensus. Inter-modality and inter-reader agreement were assessed using Cohen's kappa (im-κ and ir-κ).

RESULTS

The mean combined MRI score for bronchiectasis/bronchial wall thickening was 4.5/12 (CT scores, 2.2/12 for bronchiectasis and 6/12 for bronchial wall thickening; im-κ, 0.04-0.3). Expiratory right/left bronchial collapse was observed in 51 and 47/583 on MRI (62 and 57/599 on CT; im-κ, 0.49-0.52). Markers of small airways disease on MRI were 0.15/12 for centrilobular nodules (CT, 0.34/12), 0.94/12 for air trapping (CT, 0.9/12) and 7.6/12 for perfusion deficits (CT, 0.37/12 for mosaic attenuation; im-κ, 0.1-0.41). The mean lung defect score on MRI was 1.3/12 (CT emphysema score, 5.8/24; im-κ, 0.18-0.26). Airway-/emphysema/mixed COPD phenotypes were assigned in 370, 218 and 10 of 583 cases on MRI (347, 218 and 34 of 599 cases on CT; im-κ, 0.63). For all examined features, inter-reader agreement on MRI was lower than on CT.

CONCLUSION

Concordance of MRI and CT for phenotyping of COPD in a multi-centre setting was substantial with variable inter-modality and inter-reader concordance for single diagnostic key features.

CLINICAL RELEVANCE STATEMENT

MRI of lung morphology may well serve as a radiation-free imaging modality for COPD in scientific and clinical settings, given that its potential and limitations as shown here are carefully considered.

KEY POINTS

• In a multi-centre setting, MRI and CT showed substantial concordance for phenotyping of COPD (airway-/emphysema-/mixed-type). • Individual features of COPD demonstrated variable inter-modality concordance with features of pulmonary hypertension showing the highest and bronchiectasis showing the lowest concordance. • For all single features of COPD, inter-reader agreement was lower on MRI than on CT.

Genome-wide association study identifies novel susceptible loci and evaluation of polygenic risk score for chronic obstructive pulmonary disease in a Taiwanese population

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) describes a group of progressive lung diseases causing breathing difficulties. While COPD development typically involves a complex interplay between genetic and environmental factors, genetics play a role in disease susceptibility. This study used genome-wide association studies (GWAS) and polygenic risk score (PRS) to elucidate the genetic basis for COPD in Taiwanese patients.

RESULTS

GWAS was performed on a Taiwanese COPD case-control cohort with a sample size of 5,442 cases and 17,681 controls. Additionally, the PRS was calculated and assessed in our target groups. GWAS results indicate that although there were no single nucleotide polymorphisms (SNPs) of genome-wide significance, prominent COPD susceptibility loci on or nearby genes such as WWTR1, EXT1, INTU, MAP3K7CL, MAMDC2, BZW1/CLK1, LINC01197, LINC01894, and CFAP95 (C9orf135) were identified, which had not been reported in previous studies. Thirteen susceptibility loci, such as CHRNA4, AFAP1, and DTWD1, previously reported in other populations were replicated and confirmed to be associated with COPD in Taiwanese populations. The PRS was determined in the target groups using the summary statistics from our base group, yielding an effective association with COPD (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.02-1.17, p = 0.011). Furthermore, replication a previous lung function trait PRS model in our target group, showed a significant association of COPD susceptibility with PRS of Forced Expiratory Volume in one second (FEV1)/Forced Vital Capacity (FCV) (OR 0.89, 95% CI 0.83-0.95, p = 0.001).

CONCLUSIONS

Novel COPD-related genes were identified in the studied Taiwanese population. The PRS model, based on COPD or lung function traits, enables disease risk estimation and enhances prediction before suffering. These results offer new perspectives on the genetics of COPD and serve as a basis for future research.

Impact of Smoking and Chronic Obstructive Pulmonary Disease on All-Cause, Respiratory, and Cardio-Cerebrovascular Mortality

Introduction

Mortality differences in chronic obstructive pulmonary disease (COPD) between nonsmokers and smokers remain unclear. We compared the risk of death associated with smoking and COPD on mortality.

Methods

The study included participants aged ≥40 years who visited pulmonary clinics and were categorised into COPD or non-COPD and smoker or nonsmoker on the basis of spirometry results and cigarette consumption. Mortality rates were compared between groups using statistical analysis for all-cause mortality, respiratory disease-related mortality, and cardiocerebrovascular disease-related mortality.

Results

Among 5811 participants, smokers with COPD had a higher risk of all-cause (adjusted hazard ratio (aHR), 1.69; 95% confidence interval (CI), 1.23-2.33) and respiratory disease-related mortality (aHR, 2.14; 95% CI, 1.20-3.79) than nonsmokers with COPD. Non-smokers with and without COPD had comparable risks of all-cause mortality (aHR, 1.39; 95% CI, 0.98-1.97) and respiratory disease-related mortality (aHR, 1.77; 95% CI, 0.85-3.68). However, nonsmokers with COPD had a higher risk of cardiocerebrovascular disease-related mortality than nonsmokers without COPD (aHR, 2.25; 95% CI, 1.15-4.40).

Conclusion

The study found that smokers with COPD had higher risks of all-cause mortality and respiratory disease-related mortality compared to nonsmokers with and without COPD. Meanwhile, nonsmokers with COPD showed comparable risks of all-cause and respiratory mortality but had a higher risk of cardiocerebrovascular disease-related mortality compared to nonsmokers without COPD.

Multi-center Korean cohort study based on RNA-sequencing data targeting COPD patients

In 2023, WHO ranked chronic obstructive pulmonary disease (COPD) as the third leading cause of death, with 3.23 million fatalities in 2019. The intricate nature of the disease, which is influenced by genetics, environment, and lifestyle, is evident. The effect of air pollution and changes in atmospheric substances because of global warming highlight the need for this research. These environmental shifts are associated with the emergence of various respiratory infections such as COVID-19. RNA sequencing is pivotal in airway diseases, including COPD, as it enables comprehensive transcriptome analysis, biomarker discovery, and uncovers novel pathways. It facilitates personalized medicine by tracking dynamic changes in gene expression in response to various triggers. However, the limited research on East Asian populations may overlook the unique nuances of COPD development and progression. Bridging this gap and using peripheral blood samples for systemic analysis are crucial for comprehensive and globally applicable COPD diagnosis and treatment.

Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity

RATIONALE AND OBJECTIVES

Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO≥75%pred and DLCO<75%pred).

MATERIALS AND METHODS

In this retrospective study, 71 ex-smokers (50-85yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure.

RESULTS

Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p < .001), residual-volume to total-lung-capacity ratio (p = .003), St. George's Respiratory Questionnaire (p = .029), and six-minute-walk distance (6MWD) (p < .001), but similar relative area of the lung < -950 Hounsfield-units (RA950) (p = .9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC=0.81), which correlated with DLCO (ρ = -0.29, p = .02), MRI ADC (ρ = 0.23, p = .048), and 6MWD (ρ = -0.25, p = .02).

CONCLUSION

In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements.

Factors Affecting Survival in Severe and Very Severe COPD after Admission in ICUs of Tertiary Care Centers of India (FAST COPD): Study Protocol for a Multicentric Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. However, there is a lack of comprehensive data from low- and middle-income countries (LMICs) regarding factors influencing COPD outcomes, particularly in regions where biomass exposure is prevalent.

Objective

The Factors Affecting Survival in Severe and Very Severe COPD Patients Admitted to Tertiary Centers of India (FAST) study aims to address this gap by evaluating factors impacting survival and exacerbation rates among COPD patients in LMICs like India, with a specific focus on biomass exposure, clinical phenotypes, and nutritional status in patients admitted to the Intensive Care Unit (ICU).

Methods

The FAST study is an observational cohort study conducted in university teaching hospitals across India. The study aims to enroll 1000 COPD patients admitted to the ICU meeting specific inclusion criteria, with follow-up assessments conducted every 6 months over a 2-year period. Data collection includes demographic information, clinical manifestations, laboratory investigations, pulmonary function tests, medications, nutritional status, mental health, and health-related quality of life. Adjudication of exacerbations and mortality will also be undertaken. The FAST study seeks to provide crucial insights into COPD outcomes in LMICs, informing more precise management strategies and mitigating the burden of COPD in these settings. By evaluating factors such as biomass exposure, clinical phenotypes, and nutritional status, the study aims to address key knowledge gaps in COPD research.

How to cite this article

Arunachala S, Devapal S, Swamy DSN, Greeshma MV, Ul Hussain I, Siddaiah JB, et al. Factors Affecting Survival in Severe and Very Severe COPD after Admission in ICUs of Tertiary Care Centers of India (FAST COPD): Study Protocol for a Multicentric Cohort Study. Indian J Crit Care Med 2024;28(6):552-560.

Development and validation of a prediction score to assess the risk of incurring in COPD-related exacerbations: a population-based study in primary care

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is the fourth most important cause of death in high-income countries. Inappropriate use of COPD inhaled therapy, including the low adherence (only 10 %-40 % of patients reporting an adequate compliance) may shrink or even nullify the proven benefits of these medications. As such, an accurate prediction algorithm to assess at national level the risk of COPD exacerbation might be relevant for general practictioners (GPs) to improve patient's therapy.

METHODS

We formed a cohort of patients aged 45 years or older being diagnosed with COPD in the period between January 2013 to December 2021. Each patient was followed until occurrence of COPD exacerbation up to the end of 2021. Sixteen determinants were adopted to assemble the CopdEX(CEX)-Health Search(HS)core, which was therefore developed and validated through the related two sub-cohorts.

RESULTS

We idenfied 63763 patients aged 45 years or older being diagnosed with COPD (mean age: 67.8 (SD:11.7); 57.7 % males).When the risk of COPD exacerbation was estimated via CEX-HScore, its predicted value was equal to 14.22 % over a 6-month event horizon. Discrimination accuracy and explained variation were equal to 66 % (95 % CI: 65-67 %) and 10 % (95 % CI: 9-11 %), respectively. The calibration slope did not significantly differ from the unit (p = 0.514).

CONCLUSIONS

The CEX-HScore was featured by fair accuracy for prediction of COPD-related exacerbations over a 6-month follow-up. Such a tool might therefore support GPs to enhance COPD patients' care, and improve their outcomes by facilitating personalized approaches through a score-based decision support system.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features

BACKGROUND

Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD.

PURPOSE

We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts.

METHODS

A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated.

RESULTS

The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants.

CONCLUSIONS

Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Blood eosinophils levels in a Colombian cohort of biomass-and tobacco-related COPD patients

Introduction

Chronic obstructive pulmonary disease (COPD) is a major cause of illness and death among adults. In 2019, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) strategy incorporated blood eosinophils as a biomarker to identify patients at increased risk of exacerbations which, with the history of exacerbations during the previous year, allows identification of patients who would benefit from anti-inflammatory treatment to reduce the risk of future exacerbations. The aim of this study was to describe demographic and clinical characteristics, eosinophil counts, and exacerbations in a cohort of COPD patients stratified by clinical phenotypes (non-exacerbator, frequent exacerbator, asthma-COPD overlap) in a Colombian cohort at 2600 meters above sea level.

Methods

A descriptive analysis of a historical cohort of patients with a confirmed diagnosis of moderate to severe COPD (FEV1/FVC < 0.7 and at least one risk factor for COPD) from two specialized centers with comprehensive disease management programs was performed from January 2015 to March 2019. Data were extracted from medical records 1 year before and after the index date.

Results

200 patients were included (GOLD B: 156, GOLD E: 44; 2023 GOLD classification); mean age was 77.9 (SD 7.9) years; 48% were women, and 52% had biomass exposure as a COPD risk factor. The mean FEV1/FVC was 53.4% (SD 9.8), with an FEV1 of 52.7% (20.7). No differences were observed between clinical phenotypes in terms of airflow limitation. The geometric mean of absolute blood eosinophils was 197.58 (SD 2.09) cells/μL (range 0 to 3,020). Mean blood eosinophil count was higher in patients with smoking history and frequent exacerbators. At least one moderate and one severe exacerbation occurred in the previous year in 44 and 8% of patients, respectively; during the follow-up year 152 exacerbations were registered, 122 (80%) moderate and 30 (20%) severe. The highest rate of exacerbations in the follow-up year occurred in the subgroup of patients with the frequent exacerbator phenotype and eosinophils ≥300 cells/μL.

Discussion

In this cohort, the frequency of biomass exposure as a risk factor is considerable. High blood eosinophil count was related to smoking, and to the frequent exacerbator phenotype.

Factors Associated with the Discrepancy between Exercise Capacity and Airflow Limitation in Patients with Chronic Obstructive Pulmonary Disease

BACKGROUND

Exercise capacity is associated with lung function decline in chronic obstructive pulmonary disease (COPD) patients, but a discrepancy between exercise capacity and airflow limitation exists. This study aimed to explore factors contributing to this discrepancy in COPD patients.

METHODS

Data for this prospective study were obtained from the Korean COPD Subgroup Study. The exercise capacity and airflow limitation were assessed using the 6-minute walk distance (6-MWD; m) and forced expiratory volume in 1 second (FEV1). Participants were divided into four groups: FEV1 >50%+6-MWD >350, FEV1 >50%+6- MWD ≤350, FEV1 ≤50%+6-MWD >350, and FEV1 ≤50%+6-MWD ≤350 and their clinical characteristics were compared.

RESULTS

A total of 883 patients (male:female, 822:61; mean age, 68.3±7.97 years) were enrolled. Among 591 patients with FEV1 >50%, 242 were in the 6-MWD ≤350 group, and among 292 patients with FEV1 ≤50%, 185 were in the 6-MWD >350 group. The multiple regression analyses revealed that male sex (odds ratio [OR], 8.779; 95% confidence interval [CI], 1.539 to 50.087; p=0.014), current smoking status (OR, 0.355; 95% CI, 0.178 to 0.709; p=0.003), and hemoglobin levels (OR, 1.332; 95% CI, 1.077 to 1.648; p=0.008) were significantly associated with discrepancies in exercise capacity and airflow limitation in patients with FEV1 >50%. Meanwhile, in patients with FEV1 ≤50%, diffusion capacity of carbon monoxide (OR, 0.945; 95% CI, 0.912 to 0.979; p=0.002) was significantly associated with discrepancies between exercise capacity and airflow limitation.

CONCLUSION

The exercise capacity of COPD patients may be influenced by factors other than airflow limitation, so these aspects should be considered when assessing and treating patients.

The Natural History of Severe Chronic Obstructive Pulmonary Disease: The SPOCCAT Study Protocol

Introduction

Patients with severe chronic obstructive pulmonary disease (COPD) are often underrepresented in cohorts, creating uncertainty about the natural history and prognostic factors of this subgroup. Our goal was to describe the SPOCCAT (Severe COPD: Prospective Observational study of COPD in Catalonia) study protocol.

Material and methods

SPOCCAT is a non-interventional, multicenter, prospective cohort study of patients with severe COPD (FEV1% predicted < 50%). The study aims to: (1) establish a five-year prospective cohort; (2) identify demographic and clinical characteristics; (3) describe treatment patterns; (4) better understand the natural history of severe COPD, including lung function decline, exacerbation rates, and mortality; and (5) identify prognostic factors for poor outcomes.Recruitment began in January 2024, and the cohort will be followed for a minimum of five years (or until death or lung transplant) with follow-up visits every 12 months. Baseline data include demographics, laboratory analyses, comorbidities, lung function, respiratory symptoms, respiratory disease exacerbations and etiology, quality of life, physical activity, chest computed tomography, and treatment. Annual follow-up visits will assess changes in treatment, exacerbation frequency and severity, microbiological outcomes, complementary tests, and mortality. Participation requires written informed consent from all patients, with data collected in an anonymized electronic Case Report Form.

Results

The results of the SPOCCAT study will provide relevant information about the characteristics, treatment, and prognostic factors of severe COPD.

Conclusions

SPOCCAT has the potential to enhance understanding of severe COPD, exploring innovative aspects and establishing a robust research framework for future COPD-related projects.

Sex-differences in COPD: from biological mechanisms to therapeutic considerations

Chronic obstructive pulmonary disease (COPD) is a heterogeneous respiratory condition characterized by symptoms of dyspnea, cough, and sputum production. We review sex-differences in disease mechanisms, structure-function-symptom relationships, responses to therapies, and clinical outcomes in COPD with a specific focus on dyspnea. Females with COPD experience greater dyspnea and higher morbidity compared to males. Imaging studies using chest computed tomography scans have demonstrated that females with COPD tend to have smaller airways than males as well as a lower burden of emphysema. Sex-differences in lung and airway structure lead to critical respiratory mechanical constraints during exercise at a lower absolute ventilation in females compared to males, which is largely explained by sex differences in maximum ventilatory capacity. Females experience similar benefit with respect to inhaled COPD therapies, pulmonary rehabilitation, and smoking cessation compared to males. Ongoing re-assessment of potential sex-differences in COPD may offer insights into the evolution of patterns of care and clinical outcomes in COPD patients over time.

Delphi Consensus on Clinical Applications of GOLD 2023 Recommendations in COPD Management: How Aligned are Recommendations with Clinical Practice?

INTRODUCTION

The objective of this Delphi study was to understand and assess the level of consensus among respiratory experts on the clinical application of GOLD 2023 recommendations in management of patients with chronic obstructive pulmonary disease (COPD).

METHODS

The study comprised two online surveys and a participant meeting with 34 respiratory experts from 16 countries. Responses of 73 questions were recorded using a Likert scale ranging from 0 (disagreement) to 9 (agreement). The consensus threshold was 75%.

RESULTS

Survey 1 and survey 2 had 34 and 32 participants, respectively; and 25 attended the participant meeting. Consensus was reached on survey 1: 28/42; survey 2: 18/30 close-ended questions. A consensus was reached on the clinical relevance of most updates in definitions and diagnosis of COPD. Mixed results for the treatment recommendations by GOLD were noted: 74% agreed with the recommendation to initiate treatment with dual bronchodilators for group E patients; 63% agreed for including inhaled corticosteroids (ICS)/long-acting β2 agonist(LABA)/ Long-acting muscarinic receptor antagonists (LAMA) as a treatment option for GOLD B patients. Also, consensus lacked on removing ICS + LABA as an initial therapeutic option, in countries with challenges in access to other treatment option;. 88% agreed that they use GOLD recommendations in their daily clinical practice.

CONCLUSIONS

This Delphi study demonstrated a high level of consensus regarding key concepts of GOLD 2023 report, with most participants favoring recent updates in definitions, diagnosis, management, and prevention of COPD. More evidence on the etiotype based management and treatment options for group B and E are required which could further strengthen clinical application of the GOLD report.

Accelerated elastin degradation by age-disease interaction: a common feature in age-related diseases

Aging is a major driving force for many diseases but the relationship between chronological age, the aging process and age-related diseases is not fully understood. Fragmentation and loss of ultra-long-lived elastin are key features in aging and several age-related diseases leading to increased mortality. By comparing the relationship between age and elastin turnover with healthy volunteers, we show that accelerated elastin turnover by age-disease interaction is a common feature of age-related diseases.

Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema

Rationale: Emphysema is a chronic obstructive pulmonary disease phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies. Objectives: To discover blood omics biomarkers for chest computed tomography-quantified emphysema and develop predictive biomarker panels. Methods: Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training sample of 2,370 COPDGene participants with available blood RNA sequencing, plasma proteomics, and clinical data. Internal validation was conducted in a COPDGene testing sample (n = 1,016), and external validation was done in the ECLIPSE study (n = 526). Because low body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Elastic net models with bootstrapping were also developed in the training sample sequentially using clinical, blood cell proportions, RNA-sequencing, and proteomic biomarkers to predict quantitative emphysema. Model accuracy was assessed by the area under the receiver operating characteristic curves for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: Totals of 3,829 genes, 942 isoforms, 260 exons, and 714 proteins were significantly associated with emphysema (false discovery rate, 5%) and yielded 11 biological pathways. Seventy-four percent of these genes and 62% of these proteins showed mediation by BMI. Our prediction models demonstrated reasonable predictive performance in both COPDGene and ECLIPSE. The highest-performing model used clinical, blood cell, and protein data (area under the receiver operating characteristic curve in COPDGene testing, 0.90; 95% confidence interval, 0.85-0.90). Conclusions: Blood transcriptome and proteome-wide analyses revealed key biological pathways of emphysema and enhanced the prediction of emphysema.

Endotypes of Paediatric Cough-Do They Exist and Finding New Techniques to Improve Clinical Outcomes

Chronic cough is a common symptom of many childhood lung conditions. Given the phenotypic heterogeneity of chronic cough, better characterization through endotyping is required to provide diagnostic certainty, precision therapies and to identify pathobiological mechanisms. This review summarizes recent endotype discoveries in airway diseases, particularly in relation to children, and describes the multi-omic approaches that are required to define endotypes. Potential biospecimens that may contribute to endotype and biomarker discoveries are also discussed. Identifying endotypes of chronic cough can likely provide personalized medicine and contribute to improved clinical outcomes for children.

Cell-free DNA levels associate with COPD exacerbations and mortality

THE QUESTION ADDRESSED BY THE STUDY

Good biological indicators capable of predicting chronic obstructive pulmonary disease (COPD) phenotypes and clinical trajectories are lacking. Because nuclear and mitochondrial genomes are damaged and released by cigarette smoke exposure, plasma cell-free mitochondrial and nuclear DNA (cf-mtDNA and cf-nDNA) levels could potentially integrate disease physiology and clinical phenotypes in COPD. This study aimed to determine whether plasma cf-mtDNA and cf-nDNA levels are associated with COPD disease severity, exacerbations, and mortality risk.

MATERIALS AND METHODS

We quantified mtDNA and nDNA copy numbers in plasma from participants enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE, n = 2,702) study and determined associations with relevant clinical parameters.

RESULTS

Of the 2,128 participants with COPD, 65% were male and the median age was 64 (interquartile range, 59-69) years. During the baseline visit, cf-mtDNA levels positively correlated with future exacerbation rates in subjects with mild/moderate and severe disease (Global Initiative for Obstructive Lung Disease [GOLD] I/II and III, respectively) or with high eosinophil count (≥ 300). cf-nDNA positively associated with an increased mortality risk (hazard ratio, 1.33 [95% confidence interval, 1.01-1.74] per each natural log of cf-nDNA copy number). Additional analysis revealed that individuals with low cf-mtDNA and high cf-nDNA abundance further increased the mortality risk (hazard ratio, 1.62 [95% confidence interval, 1.16-2.25] per each natural log of cf-nDNA copy number).

ANSWER TO THE QUESTION

Plasma cf-mtDNA and cf-nDNA, when integrated into quantitative clinical measurements, may aid in improving COPD severity and progression assessment.

Impact of Applying the Global Lung Initiative Criteria for Airway Obstruction in GOLD Defined COPD Cohorts: The BODE and CHAIN Experience

INTRODUCTION

The Global Lung Function Initiative (GLI) has proposed new criteria for airflow limitation (AL) and recommends using these to interpret spirometry. The objective of this study was to explore the impact of the application of the AL GLI criteria in two well characterized GOLD-defined COPD cohorts.

METHODS

COPD patients from the BODE (n=360) and the COPD History Assessment In SpaiN (CHAIN) cohorts (n=722) were enrolled and followed. Age, gender, pack-years history, BMI, dyspnea, lung function measurements, exercise capacity, BODE index, history of exacerbations and survival were recorded. CT-detected comorbidities were registered in the BODE cohort. The proportion of subjects without AL by GLI criteria was determined in each cohort. The clinical, CT-detected comorbidity, and overall survival of these patients were evaluated.

RESULTS

In total, 18% of the BODE and 15% of the CHAIN cohort did not meet GLI AL criteria. In the BODE and CHAIN cohorts respectively, these patients had a high clinical burden (BODE≥3: 9% and 20%; mMRC≥2: 16% and 45%; exacerbations in the previous year: 31% and 9%; 6MWD<350m: 15% and 19%, respectively), and a similar prevalence of CT-diagnosed comorbidities compared with those with GLI AL. They also had a higher rate of long-term mortality - 33% and 22% respectively.

CONCLUSIONS

An important proportion of patients from 2 GOLD-defined COPD cohorts did not meet GLI AL criteria at enrolment, although they had a significant burden of disease. Caution must be taken when applying the GLI AL criteria in clinical practice.

A fully automated pipeline for the extraction of pectoralis muscle area from chest computed tomography scans

Background

Computed tomography (CT)-derived pectoralis muscle area (PMA) measurements are prognostic in people with or at-risk of COPD, but fully automated PMA extraction has yet to be developed. Our objective was to develop and validate a PMA extraction pipeline that can automatically: 1) identify the aortic arch slice; and 2) perform pectoralis segmentation at that slice.

Methods

CT images from the Canadian Cohort of Obstructive Lung Disease (CanCOLD) study were used for pipeline development. Aorta atlases were used to automatically identify the slice containing the aortic arch by group-based registration. A deep learning model was trained to segment the PMA. The pipeline was evaluated in comparison to manual segmentation. An external dataset was used to evaluate generalisability. Model performance was assessed using the Dice-Sorensen coefficient (DSC) and PMA error.

Results

In total 90 participants were used for training (age 67.0±9.9 years; forced expiratory volume in 1 s (FEV1) 93±21% predicted; FEV1/forced vital capacity (FVC) 0.69±0.10; 47 men), and 32 for external testing (age 68.6±7.4 years; FEV1 65±17% predicted; FEV1/FVC 0.50±0.09; 16 men). Compared with manual segmentation, the deep learning model achieved a DSC of 0.94±0.02, 0.94±0.01 and 0.90±0.04 on the true aortic arch slice in the train, validation and external test sets, respectively. Automated aortic arch slice detection obtained distance errors of 1.2±1.3 mm and 1.6±1.5 mm on the train and test data, respectively. Fully automated PMA measurements were not different from manual segmentation (p>0.05). PMA measurements were different between people with and without COPD (p=0.01) and correlated with FEV1 % predicted (p<0.05).

Conclusion

A fully automated CT PMA extraction pipeline was developed and validated for use in research and clinical practice.

Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD

Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.

Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis

BACKGROUND

Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps.

METHODS

We conducted a systematic review and meta-analysis to compare the performance of machine learning and deep learning prognostic models and identify pathways for future research. We searched PubMed, Embase, the Cochrane Library, ProQuest, Scopus, and Web of Science from database inception to April 6, 2023, for studies in English using machine learning or deep learning to predict patient outcomes at least 6 months after initial clinical presentation in those with COPD. We included studies comprising human adults aged 18-90 years and allowed for any input modalities. We reported area under the receiver operator characteristic curve (AUC) with 95% CI for predictions of mortality, exacerbation, and decline in forced expiratory volume in 1 s (FEV1). We reported the degree of interstudy heterogeneity using Cochran's Q test (significant heterogeneity was defined as p≤0·10 or I2>50%). Reporting quality was assessed using the TRIPOD checklist and a risk-of-bias assessment was done using the PROBAST checklist. This study was registered with PROSPERO (CRD42022323052).

FINDINGS

We identified 3620 studies in the initial search. 18 studies were eligible, and, of these, 12 used conventional machine learning and six used deep learning models. Seven models analysed exacerbation risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·69-0·85]) and there was significant heterogeneity (I2 97%, p<0·0001). 11 models analysed mortality risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·74-0·80]) with significant degrees of heterogeneity (I2 60%, p=0·027). Two studies assessed decline in lung function and were unable to be pooled. Machine learning and deep learning models did not show significant improvement over pre-existing disease severity scores in predicting exacerbations (p=0·24). Three studies directly compared machine learning models against pre-existing severity scores for predicting mortality and pooled performance did not differ (p=0·57). Of the five studies that performed external validation, performance was worse than or equal to regression models. Incorrect handling of missing data, not reporting model uncertainty, and use of datasets that were too small relative to the number of predictive features included provided the largest risks of bias.

INTERPRETATION

There is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores. More rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility.

FUNDING

None.

Baseline characteristics from a 3-year longitudinal study to phenotype subjects with COPD: the FOOTPRINTS study

BACKGROUND

FOOTPRINTS® is a prospective, longitudinal, 3-year study assessing the association between biomarkers of inflammation/lung tissue destruction and chronic obstructive pulmonary disease (COPD) severity and progression in ex-smokers with mild-to-severe COPD. Here, we present baseline characteristics and select biomarkers of study subjects.

METHODS

The methodology of FOOTPRINTS® has been published previously. The study population included ex-smokers with a range of COPD severities (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stages 1-3), ex-smokers with COPD and alpha-1-antitrypsin deficiency (A1ATD) and a control group of ex-smokers without airflow limitation (EwAL). At study entry, data were collected for: demographics, disease characteristics, history of comorbidities and COPD exacerbations, symptoms, lung function and volume, exercise capacity, soluble biomarkers, and quantitative and qualitative computed tomography. Baseline data are presented with descriptive statistical comparisons for soluble biomarkers in the individual GOLD and A1ATD groups versus EwAL.

RESULTS

In total, 463 subjects were enrolled. The per-protocol set comprised 456 subjects, mostly male (64.5%). The mean (standard deviation) age was 60.7 (6.9) years. At baseline, increasing pulmonary symptoms, worse lung function, increased residual volume, reduced diffusing capacity of the lung for carbon monoxide (DLco) and greater prevalence of centrilobular emphysema were observed with increasing disease severity amongst GOLD 1-3 subjects. Subjects with A1ATD (n = 19) had similar lung function parameters to GOLD 2-3 subjects, a high residual volume comparable to GOLD 3 subjects, and similar air trapping to GOLD 2 subjects. Compared with EwAL (n = 61), subjects with A1ATD had worse lung function, increased residual volume, reduced DLco, and a greater prevalence of confluent or advanced destructive emphysema. The soluble inflammatory biomarkers white blood cell count, fibrinogen, high-sensitivity C-reactive protein and plasma surfactant protein were higher in GOLD 1-3 groups than in the EwAL group. Interleukin-6 was expressed less often in EwAL subjects compared with subjects in the GOLD and A1ATD groups. Soluble receptor for advanced glycation end product was lowest in GOLD 3 subjects, indicative of more severe emphysema.

CONCLUSIONS

These findings provide context for upcoming results from FOOTPRINTS®, which aims to establish correlations between biomarkers and disease progression in a representative COPD population.

TRIAL REGISTRATION NUMBER

NCT02719184, study start date 13/04/2016.

A Survey on Artificial Intelligence in Pulmonary Imaging

Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.

Research on the effectiveness and safety of bronchial thermoplasty in patients with chronic obstructive pulmonary disease

OBJECTIVES

To investigate the clinical efficacy and safety of bronchial thermoplasty (BT) in treating patients with chronic obstructive pulmonary disease (COPD).

METHODS

Clinical data of 57 COPD patients were randomized into the control (n = 29, conventional inhalation therapy) or intervention group (n = 28, conventional inhalation therapy plus BT). Primary outcomes were differences in clinical symptom changes, pulmonary function-related indicators, modified Medical Research Council (mMRC), 6-min walk test (6MWT), COPD assessment test (CAT) score and acute exacerbation incidence from baseline to an average of 3 and 12 months. Safety was assessed by adverse events.

RESULTS

FEV1, FEV1(%, predicted) and FVC in both groups improved to varying degrees post-treatment compared with those pre-treatment (P < 0.05). The Intervention group showed greater improving amplitudes of FEV1 (Ftime × between groups = 21.713, P < 0.001) and FEV1(%, predicted) (Ftime × between groups = 31.216, P < 0.001) than the control group, and there was no significant difference in FVC variation trend (Ftime × between groups = 1.705, P = 0.193). mMRC, 6MWT and CAT scores of both groups post-treatment improved to varying degrees (Ps < 0.05), but the improving amplitudes of mMRC (Ftime × between groups = 3.947, P = 0.025), 6MWT (Ftime × between groups = 16.988, P < 0.001) and CAT score (Ftime × between groups = 16.741, P < 0.001) in the intervention group were greater than the control group. According to risk assessment of COPD acute exacerbation, the proportion of high-risk COPD patients with acute exacerbation in the control and intervention groups at 1 year post-treatment (100% vs 65%, 100% vs 28.6%), inpatient proportion (100% vs 62.1%; 100% vs 28.6%), COPD acute exacerbations [3.0 (2.50, 5.0) vs 1.0 (1.0, 2.50); 3.0(3.0, 4.0) vs 0 (0, 1.0)] and hospitalizations [2.0 (2.0, 3.0) vs 1.0 (0, 2.0); 2.0 (2.0, 3.0) vs 0 (0, 1.0)] were significantly lower than those pre-treatment (P < 0.05). Besides, data of the intervention group were significantly lower than the control group at each timepoint after treatment (P < 0.05).

CONCLUSIONS

Combined BT therapy is superior to conventional medical treatment in improving lung function and quality of life of COPD patients, and it also significantly reduces the COPD exacerbation risk without causing serious adverse events.

Update on metabolomic findings in COPD patients

COPD is a heterogeneous disorder that shows diverse clinical presentations (phenotypes and "treatable traits") and biological mechanisms (endotypes). This heterogeneity implies that to carry out a more personalised clinical management, it is necessary to classify each patient accurately. With this objective, and in addition to clinical features, it would be very useful to have well-defined biological markers. The search for these markers may either be done through more conventional laboratory and hypothesis-driven techniques or relatively blind high-throughput methods, with the omics approaches being suitable for the latter. Metabolomics is the science that studies biological processes through their metabolites, using various techniques such as gas and liquid chromatography, mass spectrometry and nuclear magnetic resonance. The most relevant metabolomics studies carried out in COPD highlight the importance of metabolites involved in pathways directly related to proteins (peptides and amino acids), nucleic acids (nitrogenous bases and nucleosides), and lipids and their derivatives (especially fatty acids, phospholipids, ceramides and eicosanoids). These findings indicate the relevance of inflammatory-immune processes, oxidative stress, increased catabolism and alterations in the energy production. However, some specific findings have also been reported for different COPD phenotypes, demographic characteristics of the patients, disease progression profiles, exacerbations, systemic manifestations and even diverse treatments. Unfortunately, the studies carried out to date have some limitations and shortcomings and there is still a need to define clear metabolomic profiles with clinical utility for the management of COPD and its implicit heterogeneity.

Cost-Effectiveness of Single-Inhaler Triple Therapy (FF/UMEC/VI) versus Tiotropium Monotherapy in Patients with Symptomatic Moderate-to-Very Severe COPD in the UK

Purpose

For patients with chronic obstructive pulmonary disease (COPD) who remain symptomatic despite maintenance treatment, clinical management guidelines recommend a stepwise escalation from monotherapy to dual therapy, and from dual therapy to triple therapy. However, in clinical practice, patients are often escalated directly from monotherapy to triple therapy based on disease severity. This study evaluated the cost-effectiveness of once-daily, single-inhaler fluticasone furoate, umeclidinium, and vilanterol (FF/UMEC/VI) triple therapy compared with long-acting muscarinic antagonist monotherapy with once-daily tiotropium (TIO) in patients with symptomatic moderate-to-very severe COPD, from a UK National Health Service perspective.

Patients and Methods

The validated GALAXY-COPD disease progression model was populated with patient baseline characteristics and treatment effect data from the 12-week GSK Study 207626 comparing FF/UMEC/VI with TIO in patients with moderate-to-very severe COPD. UK unit costs and drug costs (British Pound, 2021) were applied to healthcare resource utilization and treatments. The base case analysis was conducted over a lifetime horizon, and costs and health outcomes (except for life years [LYs]) were discounted at 3.5% per year. Model outputs included exacerbation rates, healthcare costs, LYs, quality-adjusted LYs (QALYs), and incremental cost-effectiveness ratios.

Results

Overall, treatment with FF/UMEC/VI resulted in increased clinical benefit (reduction in total exacerbations and increased overall survival and QALYs), coupled with cost savings (derived from lower maintenance and exacerbation healthcare costs) compared with TIO monotherapy. In the base case analysis, FF/UMEC/VI provided an additional 0.393 LYs (95% range: 0.176, 0.655) and 0.443 QALYs (0.246, 0.648), at a cost saving of £880 (£54, £1608) versus TIO. FF/UMEC/VI remained the cost-effective (dominant) treatment option across sensitivity and scenario analyses.

Conclusion

FF/UMEC/VI offers greater clinical benefits and is a cost-effective treatment option compared with TIO for the treatment of adult patients with COPD with persistent symptoms and/or who are at risk of exacerbation in the UK.

Blood Gene Expression and Immune Cell Subtypes Associated with Chronic Obstructive Pulmonary Disease Exacerbations

Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AE-COPDs) are associated with a significant disease burden. Blood immune phenotyping may improve our understanding of a COPD endotype at increased risk of exacerbations. Objective: To determine the relationship between the transcriptome of circulating leukocytes and COPD exacerbations. Methods: Blood RNA sequencing data (n = 3,618) from the COPDGene (Genetic Epidemiology of COPD) study were analyzed. Blood microarray data (n = 646) from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study were used for validation. We tested the association between blood gene expression and AE-COPDs. We imputed the abundance of leukocyte subtypes and tested their association with prospective AE-COPDs. Flow cytometry was performed on blood in SPIROMICS (Subpopulations and Intermediate Outcomes in COPD Study) (n = 127), and activation markers for T cells were tested for association with prospective AE-COPDs. Measurements and Main Results: Exacerbations were reported 4,030 and 2,368 times during follow-up in COPDGene (5.3 ± 1.7 yr) and ECLIPSE (3 yr), respectively. We identified 890, 675, and 3,217 genes associated with a history of AE-COPDs, persistent exacerbations (at least one exacerbation per year), and prospective exacerbation rate, respectively. In COPDGene, the number of prospective exacerbations in patients with COPD (Global Initiative for Chronic Obstructive Lung Disease stage ⩾2) was negatively associated with circulating CD8+ T cells, CD4+ T cells, and resting natural killer cells. The negative association with naive CD4+ T cells was replicated in ECLIPSE. In the flow-cytometry study, an increase in CTLA4 on CD4+ T cells was positively associated with AE-COPDs. Conclusions: Individuals with COPD with lower circulating lymphocyte counts, particularly decreased CD4+ T cells, are more susceptible to AE-COPDs, including persistent exacerbations.

Phenotyping asthma with airflow obstruction in middle-aged and older adults: a CADSET clinical research collaboration

BACKGROUND

The prevalence and clinical profile of asthma with airflow obstruction (AO) remain uncertain. We aimed to phenotype AO in population- and clinic-based cohorts.

METHODS

This cross-sectional multicohort study included adults ≥50 years from nine CADSET cohorts with spirometry data (N=69 789). AO was defined as ever diagnosed asthma with pre-BD or post-BD FEV1/FVC <0.7 in population-based and clinic-based cohorts, respectively. Clinical characteristics and comorbidities of AO were compared with asthma without airflow obstruction (asthma-only) and chronic obstructive pulmonary disease (COPD) without asthma history (COPD-only). ORs for comorbidities adjusted for age, sex, smoking status and body mass index (BMI) were meta-analysed using a random effects model.

RESULTS

The prevalence of AO was 2.1% (95% CI 2.0% to 2.2%) in population-based, 21.1% (95% CI 18.6% to 23.8%) in asthma-based and 16.9% (95% CI 15.8% to 17.9%) in COPD-based cohorts. AO patients had more often clinically relevant dyspnoea (modified Medical Research Council score ≥2) than asthma-only (+14.4 and +14.7 percentage points) and COPD-only (+24.0 and +5.0 percentage points) in population-based and clinic-based cohorts, respectively. AO patients had more often elevated blood eosinophil counts (>300 cells/µL), although only significant in population-based cohorts. Compared with asthma-only, AO patients were more often men, current smokers, with a lower BMI, had less often obesity and had more often chronic bronchitis. Compared with COPD-only, AO patients were younger, less often current smokers and had less pack-years. In the general population, AO patients had a higher risk of coronary artery disease than asthma-only and COPD-only (OR=2.09 (95% CI 1.26 to 3.47) and OR=1.89 (95% CI 1.10 to 3.24), respectively) and of depression (OR=1.41 (95% CI 1.19 to 1.67)), osteoporosis (OR=2.30 (95% CI 1.43 to 3.72)) and gastro-oesophageal reflux disease (OR=1.68 (95% CI 1.06 to 2.68)) than COPD-only, independent of age, sex, smoking status and BMI.

CONCLUSIONS

AO is a relatively prevalent respiratory phenotype associated with more dyspnoea and a higher risk of coronary artery disease and elevated blood eosinophil counts in the general population compared with both asthma-only and COPD-only.