Investigation of the Clinical, Radiological and Biological Factors Associated with Disease Progression, Phenotypes and Endotypes of COPD in China (COMPASS): study design, protocol and rationale

Clinical Characteristics and Frequency of Chronic Obstructive Pulmonary Disease Exacerbations in Korean Patients: Findings From the KOCOSS Cohort 2012-2021

BACKGROUND

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) exert a substantial burden on patients and healthcare systems; however, data related to the frequency of AECOPD in the Korean population are limited. Therefore, this study aimed to describe the frequency of severe, and moderate or severe AECOPD, as well as clinical and demographic characteristics of patients with chronic obstructive pulmonary disease (COPD) in South Korea.

METHODS

Data from patients aged > 40 years with post-bronchodilator forced expiratory volume in 1 second (FEV1)/forced vital capacity ≤ 70% of the normal predicted value from the Korea COPD Subgroup Study database were analyzed (April 2012 to 2021). The protocol was based on the EXAcerbations of COPD and their OutcomeS International study. Data were collected retrospectively for year 0 (0-12 months before study enrollment) based on patient recall, and prospectively during years 1, 2, and 3 (0-12, 13-24, and 25-36 months after study enrollment, respectively). The data were summarized using descriptive statistics.

RESULTS

Data from 3,477 Korean patients (mean age, 68.5 years) with COPD were analyzed. Overall, most patients were male (92.3%), former or current smokers (90.8%), had a modified Medical Research Council dyspnea scale score ≥ 1 (83.3%), and had moderate airflow limitation (54.4%). The mean body mass index (BMI) of the study population was 23.1 kg/m², and 27.6% were obese or overweight. Hypertension was the most common comorbidity (37.6%). The mean blood eosinophil count was 226.8 cells/μL, with 21.9% of patients having ≥ 300 cells/μL. A clinically insignificant change in FEV1 (+1.4%) was observed a year after enrollment. Overall, patients experienced a mean of 0.2 severe annual AECOPD and approximately 1.1 mean moderate or severe AECOPD. Notably, the rates of severe AECOPD remained generally consistent over time. Compared with patients with no exacerbations, patients who experienced severe exacerbations had a lower mean BMI (21.7 vs. 23.1 kg/m²; P < 0.001) and lower lung function parameters (all P values < 0.001), but reported high rates of depression (25.5% vs. 15.1%; P = 0.044) and anxiety (37.3% vs. 16.7%; P < 0.001) as a comorbidity.

CONCLUSION

Findings from this Korean cohort of patients with COPD indicated a high exacerbation burden, which may be attributable to the unique characteristics of the study population and suboptimal disease management. This highlights the need to align clinical practices with the latest treatment recommendations to alleviate AECOPD burden in Korea.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05750810.

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features.

METHODS

We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC).

RESULTS

The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971.

CONCLUSION

We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings.

TRIAL REGISTRATION

Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.

Changes in the airway microbiome in patients with bronchiectasis

This study used metagenomic next-generation sequencing (mNGS) technology to explore the changes of the microbial characteristics in the lower respiratory tract in patients with acute exacerbations of bronchiectasis (noncystic fibrosis) to guide clinical treatment and improve patients' quality of life and prognosis. This prospective study included 54 patients with acute exacerbation and 46 clinically stable patients admitted to the Respiratory and Critical Care Medicine Center of the People's Hospital of Xinjiang Uygur Autonomous Region from January 2020 to July 2022. Sputum was subjected to routine microbiological tests, and bronchoalveolar lavage fluid (BALF) samples were subjected to microbiological tests and mNGS of BALF before empirical antibiotic therapy. Serum inflammatory markers (white blood cell count, interleukin-6, procalcitonin, and C-reactive protein) were measured. In addition, we evaluated the pathogen of mNGS and compared the airway microbiome composition of patients with acute exacerbation and control patients. The mean age of our cohort was 56 ± 15.2 years. Eighty-nine patients had positive results by mNGS. There was a significant difference in the detection of viruses between the groups (χ2 = 6.954, P < .01). The fungal species Candida albicans, Pneumocystis jirovecii, and Aspergillus fumigatus were significantly more common in patients with acute exacerbations (χ2 = 5.98, P = .014). The bacterial species Acinetobacter baumannii, Mycobacterium tuberculosis, Haemophilus influenzae, Haemophilus parahaemolyticus, Abiotrophia defectiva, and Micromonas micros were significantly more prevalent in patients with acute exacerbations (χ2 = 4.065, P = .044). The most common bacterial species isolated from the sputum and BALF samples of patients with acute exacerbation was A. baumannii. Chlamydia psittaci was found in 4 patients. In addition, of 77 patients with negative sputum culture, 66 had positive results by mNGS, demonstrating the increased sensitivity and accuracy of mNGS. Patients with acute exacerbation of bronchiectasis tend to have mixed infections in the lower respiratory tract. The frequency of viruses, fungi, and Mycoplasma was higher in these patients. Our findings suggest that mNGS could be used to identify pathogenic microorganisms in these patients, increasing the effectiveness of antibiotic therapy.

The utility of quantitative computed tomography in cohort studies of chronic obstructive pulmonary disease: a narrative review

Background and Objective

Chronic obstructive pulmonary disease (COPD) is a significant contributor to global morbidity and mortality. Quantitative computed tomography (QCT), a non-invasive imaging modality, offers the potential to assess lung structure and function in COPD patients. Amidst the coronavirus disease 2019 (COVID-19) pandemic, chest computed tomography (CT) scans have emerged as a viable alternative for assessing pulmonary function (e.g., spirometry), minimizing the risk of aerosolized virus transmission. However, the clinical application of QCT measurements is not yet widespread enough, necessitating broader validation to determine its usefulness in COPD management.

Methods

We conducted a search in the PubMed database in English from January 1, 2013 to April 20, 2023, using keywords and controlled vocabulary related to QCT, COPD, and cohort studies.

Key Content and Findings

Existing studies have demonstrated the potential of QCT in providing valuable information on lung volume, airway geometry, airway wall thickness, emphysema, and lung tissue density in COPD patients. Moreover, QCT values have shown robust correlations with pulmonary function tests, and can predict exacerbation risk and mortality in patients with COPD. QCT can even discern COPD subtypes based on phenotypic characteristics such as emphysema predominance, supporting targeted management and interventions.

Conclusions

QCT has shown promise in cohort studies related to COPD, since it can provide critical insights into the pathogenesis and progression of the disease. Further research is necessary to determine the clinical significance of QCT measurements for COPD management.

Use of CAPTURE to Identify Individuals Who May or May Not Require Treatment for Chronic Obstructive Pulmonary Disease

Rationale: The CAPTURE tool (Chronic Obstructive Pulmonary Disease [COPD] Assessment in Primary Care to Identify Undiagnosed Respiratory Disease and Exacerbation Risk) was developed to identify patients with undiagnosed COPD with an FEV1 <60% predicted or risk of exacerbation as treatment criteria. Objectives: To test the ability of CAPTURE to identify patients requiring treatment because of symptoms or risk of exacerbation or hospitalization. Methods: Data were from COMPASS (Clinical, Radiological and Biological Factors Associated with Disease Progression, Phenotypes and Endotypes of COPD in China), a prospective study of COPD, chronic bronchitis without airflow limitation (postbronchodilator FEV1/FVC ratio ≥0.70), and healthy never-smokers. CAPTURE was tested as questions alone and with peak expiratory flow measurement. Sensitivity, specificity, and positive and negative predicted values (PPV and NPV) were calculated for COPD Assessment Test (CAT) scores ⩾10 versus <10, modified Medical Research Council (mMRC) scores ⩾2 versus <2, and at least one moderate exacerbation or hospitalization in the previous year versus none. Measurements and Main Results: Patients with COPD (n = 1,696) had a mean age of 65 ± 7.5 years, and 90% were male, with a postbronchodilator FEV1 of 66.5 ± 20.1% predicted. Control participants (n = 307) had a mean age of 60.2 ± 7.0 years, and 65% were male, with an FEV1/FVC ratio of 0.78 ± 0.04. CAPTURE using peak expiratory flow showed the best combination of sensitivity and specificity. Sensitivity and specificity were 68.5% and 64.0%, respectively, to detect a CAT score ⩾10; 85.6% and 61.0% to detect an mMRC score ⩾2; 63.5% and 55.6% to detect at least one moderate exacerbation; and 70.2% and 59.4% to detect at least one hospitalization. PPVs ranged from 15.6% (moderate exacerbations) to 47.8% (CAT score). NPVs ranged from 80.8% (CAT score) to 95.6% (mMRC score). Conclusions: CAPTURE has good sensitivity to identify patients with COPD who may require treatment because of increased symptoms or risk of exacerbations or hospitalization, including those with an FEV1 >60% predicted. High NPV values show that CAPTURE can also exclude those who may not require treatment. Clinical trial registered with www.clinicaltrials.gov (NCT04853225).