Early Diagnosis Of Chronic Obstructive Pulmonary Disease Research Articles

Development and validation of a nomogram for predicting COPD: A nationwide population-based study in South Korea.

Chronic obstructive pulmonary disease (COPD) remains a significant global health burden exacerbated by tobacco smoking, occupational exposure, and air pollution. COPD is one of the top 3 causes of death worldwide. In South Korea, the COPD burden is expected to increase due to ongoing exposure to risk factors and the aging population. COPD is extensively underdiagnosed or underestimated, owing to a lack of public awareness. This study aimed to develop and validate a nomogram for COPD by using national data to promote early diagnosis and intervention. This study drew on a dataset from the 7th Korea National Health and Nutrition Examination Survey from 2016 to 2018, including 10,819 subjects aged 40 years or older with spirometry results. Influence of demographic, socioeconomic, and health-related factors on the incidence. Multivariable logistic regression was used to identify the significant predictors of the nomogram. The nomogram was validated using receiver operating characteristic curves, calibration plots, and concordance index (C-index). Internal validation was performed by bootstrapping. In the final analysis, 1059 (14.0%) participants had COPD. Key risk factors associated with increased COPD risk included being male, aged 70 and older, lower educational level, living in a rural area, current smoking status, underweight, and history of tuberculosis and asthma. The area under the curve (AUC) of the model was 0.822 (95% CI: 0.810-0.832), indicating that the nomogram has a high ability to identify COPD. The nomogram demonstrated solid predictive performance, as confirmed by calibration plots with a C-index (of 0.822) for the validation set with 1000 bootstrap samples. In conclusion, we developed a tool for the early detection of COPD with good properties in primary care settings, without spirometry. Appropriate and early diagnosis of COPD can have a crucial impact on public health.

AI-Based Model Design for Prediction of COPD Grade from Chest X-Ray Images: A Model Proposal (COPD-GradeNet)

Chronic Obstructive Pulmonary Disease (COPD) ranks high among the leading causes of death, particularly in middle- and low-income countries. Early diagnosis of COPD is challenging, with limited diagnostic methods currently available. In this study, a artificial intelligence model named COPD-GradeNet is proposed to predict COPD grades from radiographic images. However, the model has not yet been tested on a dataset. Obtaining a dataset including spirometric test results and chest X-ray images for COPD is a challenging process. Once the proposed model is tested on an appropriate dataset, its ability to predict COPD grades can be evaluated and implemented. This study may guide future research and clinical applications, emphasizing the potential of artificial intelligence-based approaches in the diagnosis of COPD.

Participant Selection from the General Japanese Population for Pulmonary Function Tests Using a Questionnaire on Symptoms and Smoking Habits during Annual Health Checkups: The Yamagata-Takahata Study.

Objective Pulmonary function tests are essential for diagnosing respiratory diseases, such as chronic obstructive pulmonary disease (COPD), but are typically not performed in Japan during annual health checkups, which hinders the early diagnosis of respiratory diseases. Methods Individuals who agreed to participate in the Yamagata-Takahata study during medical checkups in Takahata (Yamagata Prefecture, Japan) in 2011 were examined. We interviewed 669 participants (49.0% men; mean age, 67.7 years old) regarding their respiratory symptoms and smoking habits and performed pulmonary function tests during the study. Results Based on pulmonary function test results, 141 participants had pulmonary dysfunction, and 115 had obstructive pulmonary dysfunction. The risk of respiratory dysfunction, particularly obstructive respiratory dysfunction, was examined by referring to a questionnaire tool for an early COPD diagnosis. The associations between age, the smoking history, respiratory symptoms, and obstructive respiratory dysfunction were evaluated. Obstructive respiratory dysfunction was found in 17.6% of participants ≥50 years old and 19.5% ≥60 years old, 30.3% had a smoking history, and 32.8% had respiratory symptoms. Furthermore, the participants with multiple factors had a higher probability of obstructive respiratory dysfunction. Conclusion Subjects with obstructive pulmonary dysfunction are expected to be efficiently identified by extracting individuals by age and smoking habit and through a respiratory symptom questionnaire, although pulmonary function tests cannot be performed for all individuals during health checkups.

A novel method in COPD diagnosing using respiratory signal generation based on CycleGAN and machine learning

Objective The main goal of this research is to use distinctive features in respiratory sounds for diagnosing Chronic Obstructive Pulmonary Disease (COPD). This study develops a classification method by utilizing inverse transforms to effectively identify COPD based on unique respiratory features while comparing the classification performance of various optimal algorithms. Method Respiratory sounds are divided into individual breathing cycles. In the data standardization and augmentation phase, the CycleGAN model enhances data diversity. Comprehensive analyses for these segments are then implemented using various Wavelet families and different spectral transformations representing characteristic signals. Advanced convolutional neural networks, including VGG16, ResNet50, and InceptionV3, are used for the classification task. Results The results of this study demonstrate the effectiveness of the mentioned method. Notably, the best-performing method utilizes Wavelet Bior1.3 after standardization in combination with InceptionV3, achieving a remarkable 99.75% F1-score, the gold standard for classification accuracy. Conclusion Inverse transformation techniques combined with deep learning models show significant accuracy in detecting COPD disease. These findings suggest the feasibility of early COPD diagnosis through AI-powered characterization of acoustic features. Motivation and Significance The motivation behind this research stems from the urgent need for early and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD). COPD is a respiratory disease that poses many difficulties when detected late, potentially causing severe harm to the patient’s quality of life and increasing the healthcare burden. Timely identification and intervention are crucial to reduce the progression of the disease and improve patient outcomes.

Feasibility and performance of the chronic obstructive pulmonary disease population screener and chronic obstructive pulmonary disease screening questionnaire in a Chinese physical examination center.

Chronic obstructive pulmonary disease (COPD) affects up to 13% of the Chinese population, though it is under diagnosed throughout China. Screening among asymptomatic individual as part of routine health checks in China can facilitate early diagnosis and intervention to prevent disease progress. The COPD Population Screener (COPD-PS) or COPD Screening Questionnaire (COPD-SQ) has yet to be applied in Chinese physical examination centers (PECs) for COPD screening, and their feasibility and effectiveness should be clarified before full-scale implementation. This study is the first to apply the COPD-PS and COPD-SQ in a public hospital PEC in China to assess their feasibility and effectiveness and to identify their optimal cutoff values. People aged ≥40 years who attended the Second Affiliated Hospital of Shantou University PECs from September 2021 to December 2022 were asked to complete the COPD-PS and COPD-SQ and to undergo spirometry. The optimal cutoff values of the two questionnaires at the maximal Youden index were found, and the sensitivity and specificity were calculated. Data from 198 participants were analyzed; mean [standard deviation (SD)] age of patients was 63.52 (10.94) years. Twenty-five participants (12.63%) were diagnosed with COPD. The number of COPD patients classified as Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1 to 4 were 8, 12, 4, and 1, respectively. The area under the curves (AUCs) of the COPD-PS and COPD-SQ were 0.730 and 0.738, respectively. The optimal COPD-PS cutoff value of 4 points corresponded to a sensitivity of 72.00% and a specificity of 60.10%. The COPD-SQ optimal cutoff value of 15 points corresponded to a sensitivity of 76.00% and a specificity of 63.60%. Applying the COPD-PS and COPD-SQ in Chinese PECs is feasible, cost-effective and effective. COPD-PS and COPD-SQ can facilitate the early diagnosis of COPD, and whether they can improve the participants' quality of life would benefit a further study. It is recommended that the COPD-PS or COPD-SQ questionnaires be added to the screening of the physical examination program in PECs as part of health checks for people over 40 years old.

Early Diagnosis of Chronic Obstructive Pulmonary Disease in Smokers Using Spirometry

A cross-sectional, observational study was done in the Pulmonology Department of Combined Military Hospital (CMH) Dhaka Cantonment, Bangladesh, from September 2016 to February 2017, to explore the role of spirometry in the early detection of Chronic Obstructive Pulmonary Disease (COPD) among smokers. A total of 70 participants were selected. After taking the detail history, spirometry and chest x-ray were done. The correlation between smoking and COPD was determined. Among the total respondents 44%, 40% and 15.7% had 10-15, 15-20 and >20 pack-year smoking history respectively. Forced expiratory volume in one second/Forced vital capacity (FEV1/FVC) among these three groups of smokers were found 90%/91%, 77%/89%, and 58.4%/88% respectively. Among the participants, 20% had a change in chest X-ray and 23 (32.85%) of respondents had COPD. Among those 23 participants, 13 (18.57%) suffered from mild, 6 (8.57%) suffered from moderate, 3 (4.28%) suffered from severe, and 1 (1.43%) suffered from a very severe level of airway obstruction. Our results suggest that spirometry can be a valuable tool for the early diagnosis of COPD among smokers. Though it was found as a promising option in a small-scale study, further evaluation is necessary through a population screening to confirm its effectiveness. CBMJ 2024 January: vol. 13 no. 01 P: 53-59

Prevalence and timing of chronic obstructive pulmonary disease (COPD) diagnosis among patients newly diagnosed with lung cancer in the US.

118 Background: COPD is one of the most common comorbidities among patients with lung cancer, and an important determinant of their survival. While COPD is frequently under diagnosed in the general population, less is known about the occurrence of COPD underdiagnosis in patients with lung cancer. Objectives: 1) To estimate prevalence and under diagnosis of COPD among patients newly diagnosed with lung cancer. 2) To quantify the association between timing of COPD diagnosis and stage of lung cancer at diagnosis and whether this association is modified by sex, race, and socioeconomic status (SES). Methods: A secondary data analysis was conducted using the SEER-Medicare database. We included Medicare beneficiaries aged 66+years with primary invasive lung cancer diagnosed from 2008 to 2017 who were alive at the time of cancer diagnosis and had continuous Medicare coverage 12 months prior and 3 months after lung cancer diagnosis. Exposure: Comorbid COPD was determined using Medicare claims and classified as early COPD (evidence of COPD related healthcare utilization (HCU) >3 months before lung cancer diagnosis) versus late COPD (evidence of COPD related HCU only during the +/- 3 months from lung cancer diagnosis). Outcome: Early (localized) versus late (regional and distant) stage of lung cancer diagnosis. Statistical analysis: We used generalized linear models to estimate adjusted absolute (prevalence difference PD) and relative (prevalence ratio PR) association. Results: In a population of 185,405 Medicare beneficiaries with lung cancer, 131,230 (71%) had evidence of COPD. Compared to lung cancer patients without COPD, those with COPD were more likely to be younger, male, White, of lower SES, have more comorbidities, higher HCU, and be diagnosed with early stage lung cancer. Patients with early COPD diagnosis constitute 66,867 (51%) of patients with COPD and lung cancer. They were more likely to be older, female, Non-Hispanic White, of lower SES, and to have more comorbidities, acute exacerbation of COPD, and more early-stage lung cancer compared to patients with late COPD diagnosis. We observed a significant positive association between early COPD diagnosis and early-stage lung cancer diagnosis (PR= 1.27, 95% CI= 1.24 to 1.30; PD= 0.062, 95% CI= 0.057 to 0.066) in models adjusted for age, sex, race, SES, HCU, comorbidities, year of cancer diagnosis, SEER region, and histology. In subgroup analyses, this association was stronger for males, Non-Hispanic Black, American Indian Alaskan Native, and patients of lower SES. Conclusions: Approximately seven out of 10 patients with lung cancer have COPD, and late COPD diagnosis is common. Earlier COPD diagnosis is associated with diagnosis of lung cancer at earlier stages. These findings highlight the need for more structured screening of COPD among individuals at high risk for lung cancer.

The correlation between a Th1/Th2 cytokines imbalance and vitamin D level in patients with early chronic obstructive pulmonary disease (COPD), based on screening results.

Objective: This study explored the correlation between a Th1/Th2 cytokines imbalance and 25-hydroxy-vitamin D (vit D) level in early chronic obstructive pulmonary disease (COPD), provided experimental rationales for the role of vit D in the prevention and control of COPD, and elucidated the potential anti-inflammatory mechanism involved. Methods: This study was based on the results of the "Screening and Early Diagnosis of COPD" public health project conducted through Shenzhen Municipal Qianhai Shekou Free Trade Zone Hospital. Patients with early COPD were selected as study participants. A prospective, randomized, and controlled method was employed for assigning eligible participants into three groups, i.e., a COPD lung function (LF) I, COPD LF II, and a healthy group, respectively (n = 40 each). The serum content of tumor necrosis factor alpha (TNF-α), interferon-gamma (IFN-γ), interleukin 4 (IL-4), and IL-6 were measured by enzyme-linked immunosorbent assay, and the ratio of IFN-γ/IL-4 treated as a marker for Th1/Th2. The serum concentration of 25-hydroxyl-vit D (25 [OH]D) was quantified by a chemiluminescence assay. Statistical processing was performed, and the correlations between changes in the above parameters with vit D level and LF parameters were examined. Results: There were differences in FEV1pred%, FEV1/FVC, IFN-γ, IL-4, IL-6 and IFN-γ/IL-4 between the healthy group, the COPD LF I group and the COPD LF II group (p < 0.05). In early COPD, Th1/Th2 cytokines was positively correlated with forced expiratory volume/expected value (FEV1pred%) (r = 0.485, p < 0.001) and forced expiratory volume/forced vital capacity (FEV1/FVC) (r = 0.273, p = 0.018); Th1/Th2 cytokines levels positively correlated with vit D level (r = 0.27, p = 0.02), and 25(OH)D level positively correlated with FEV1pred% (r = 0.695, p < 0.001). Conclusion: Vitamin D deficiency was ubiquitous in patients with early COPD. It was positively correlated with the FEV1pred% and FEV1/FVC LF parameters. Accordingly, this study provides experimental rationales for the role of vit D in the prevention and control of COPD and the potential anti-inflammatory mechanisms involved.

Fractional Dynamics Foster Deep Learning of COPD Stage Prediction.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. The authors address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages-from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals.

Clinical Approaches to Minimize Readmissions of Patients with COPD: A Narrative Review

Abstract: Chronic obstructive pulmonary disease (COPD) is a progressive disease and also a lead-ing cause of morbidity and mortality worldwide. The frequent readmissions of patients with COPD may reduce lung function, mental health, and quality of life; it also increases the cost of treatment and mortality rate. Some common factors that may increase the readmission frequency of COPD pa-tients include delay of diagnosis, advanced lung function decline, lack of adherence for COPD treatment, ineffective management of comorbidities, acute exacerbation or stable COPD, and infec-tions. However, these factors might be well controlled with appropriate approaches to minimize the readmission of patients with COPD. In this review, we propose a strategy with a seven-step ap-proach to reduce the readmission in COPD patients, including early diagnosis of COPD, optimal treatment for stable COPD, targeted management of comorbidities, adequate therapy for acute ex-acerbations, individualized action plans for COPD patients, effective prevention of bacterial and vi-ral infections, and adaptive program of pulmonary rehabilitation. Thus, implementing this approach may reduce the risk of readmission in patients with COPD.

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases.

Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) "Healthy" or "COPD" and (2) "Healthy", "COPD", or "Pneumonia" classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future.

Salivary Peptide Profiling Analysis of Patients with Periodontitis and Chronic Obstructive Pulmonary Disease

To analyze the salivary peptide profiles of patients with periodontitis (PD) and chronic obstructive pulmonary disease (COPD), to identify differentially expressed peptides that are associated with diseases, to explore for biomarkers with potential diagnostic significance, and to probe for new perspectives for the early prevention and treatment of COPD. A total of 10 PD patients (the PD group), 10 PD patients with COPD (the PD plus COPD group), and 8 healthy controls (the Control group) were selected for the study. The clinical data and saliva samples of the subjects were collected. Salivary supernatant samples were separated and purified with weak-cation-exchange magnetic bead-based (WCX-MB). With matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS), the biodata of the samples were obtained and differential salivary peptide profiling was conducted to screen for peptides exhibiting inter-group differences. In addition, all the differentially expressed peptides were examined and verified with liquid chromatography tandem mass spectrometry (LC-MS/MS). An average of 77 peptide mass peaks were detected among three groups, the peaks intensities differed significantly for 10 peptides between PD patients and PD patients with COPD. Among them, eight peptides (1193.5, 1836.2, 1735.1, 1321.3, 1356.8, 2086.8, 1863.6, and 2230.9) showed increased expression and two peptides (1067.3 and 1124.4) showed decreased expression in the PD plus COPD group, in comparison with the PD group. Among the 10 differential peptides, 1193.5 and 1356.8 were identified as histidine-rich protein-1, submaxillary gland androgen-regulated protein 3B, and salivary acidic proline-rich protein 1/2. With WCX-MB and MALDI-TOF-MS, we have identified, from the saliva of patients with concomitant PD and COPD, differentially expressed salivary peptides that were associated with diseases. The differentially expressed peptides thus screened out show promises for being used as auxiliary biomarkers for early diagnosis of COPD.

A Graph Convolutional Multiple Instance Learning on a Hypersphere Manifold Approach for Diagnosing Chronic Obstructive Pulmonary Disease in CT Images.

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic disease with high morbidity and mortality. The early diagnosis of COPD is vital for clinical treatment, which helps patients to have a better quality of life. Because COPD can be ascribed to chronic bronchitis and emphysema, lesions in a computed tomography (CT) image can present anywhere inside the lung with different types, shapes and sizes. Multiple instance learning (MIL) is an effective tool for solving COPD discrimination. In this study, a novel graph convolutional MIL with the adaptive additive margin loss (GCMIL-AAMS) approach is proposed to diagnose COPD by CT. Specifically, for those early stage patients, the selected instance-level features can be more discriminative if they were learned by our proposed graph convolution and pooling with self-attention mechanism. The AAMS loss can utilize the information of COPD severity on a hypersphere manifold by adaptively setting the angular margins to improve the performance, as the severity can be quantified as four grades by pulmonary function test. The results show that our proposed GCMIL-AAMS method provides superior discrimination and generalization abilities in COPD discrimination, with areas under a receiver operating characteristic curve (AUCs) of 0.960 ± 0.014 and 0.862 ± 0.010 in the test set and external testing set, respectively, in 5-fold stratified cross validation; moreover, it demonstrates that graph learning is applicable to MIL and suggests that MIL may be adaptable to graph learning.

Does an early COPD diagnosis improve long-term outcomes?

It depends. A diagnosis of chronic obstructive pulmonary disease (COPD) made using screening spirometry in patients without symptoms does not change the course of the disease or alter smoking rates (strength of recommendation [SOR]: A, preponderance of evidence from multiple randomized controlled trials [RCTs]). However, once a patient develops symptoms of lung disease, a delayed diagnosis is associated with poorer outcomes (SOR: B, cohort studies). Active case finding (including the use of spirometry) is recommended for patients with risk factors for COPD who present with consistent symptoms (SOR: C, expert opinion).

GATA3/long noncoding RNA MHC-R regulates the immune activity of dendritic cells in chronic obstructive pulmonary disease induced by air pollution particulate matter

Chronic obstructive pulmonary disease (COPD) is a heterogeneous illness associated with aberrant inflammatory immune reaction in the lung in response to noxious particles and gases. Our previous epidemiological studies discovered that long-term exposure to air pollution PM was associated with an increase in the incidence of COPD and lung function decline, but the impact of air pollution on the onset of COPD and its pathogenesis remains obscure. In recent years, long noncoding RNAs (lncRNAs) have been documented to have a crucial role in COPD. Our preliminary study found that the expression of lncRNA MHC-R in the lung tissues of rats exposed to air pollution PM was dramatically elevated, and the specific expression was mainly focused on the immune-related MHC I, antigen-presenting, and adaptive immune response. After transcription factor prediction, it was found that GATA3 could be combined with the specific sequence of the lncRNA MHC-R promoter region. Dendritic cells (DCs) are necessary antigen-presenting cells (APCs) with the most potent antigen-presenting function. We proved that GATA3/lncRNA MHC-R might regulate the immune activities of DCs to participate in the pathogenic mechanism of COPD induced by air pollution PM, which opens up a new way for early COPD diagnosis and treatment.

THE ROLE OF COMPUTED TOMOGRAPHY IN THE DIAGNOSIS OF CHOBLE (LITERATURE REVIEW)

Chronic obstructive pulmonary disease (COPD) is a common human disease. Further increase of morbidity rate is predicted in the nearest years. Early diagnosis of COPD is of topical importance, and computed tomography (CT) is generally accepted as the "gold" standard for its diagnosis. Therefore, this publication presents a literature review on CT diagnostics of COPD.

Examining Risk Factors Accelerating Time-to-Chronic Obstructive Pulmonary Disease (COPD) Diagnosis among Asthma Patients

Asthma patients may have an increased risk for diagnosis of chronic obstructive pulmonary disease (COPD). However, risk factors accelerating time-to-COPD diagnosis are unclear. This study aims to estimate risk factors associated with the incidence of COPD diagnosis in asthma patients. Canada’s Population Data BC (PopData BC) was used to identify asthma patients without prior COPD diagnosis between January 1, 1998, to December 31, 1999. Patients were assessed for time-to-incidence of COPD diagnosis from January 1, 2000, to December 31, 2018. The study estimated the effects of several risk factors in predicting the incidence of COPD in asthma patients during the 18-year follow-up period. Patient factors such as Medication Adherence (MA) were assessed by the proportion of days covered (PDC) and the medication possession ratio (MPR). The log-logistic mixed-effects accelerated failure time model was used to estimate the adjusted failure time ratios (aFTR) and 95% Confidence Interval (95% CI) for factors predicting time-to-COPD diagnosis among asthma patients. We identified 68,211 asthma patients with a mean age of 48.2 years included in the analysis. Risk factors accelerating time-to-COPD diagnosis included: male sex (aFTR: 0.62, 95% CI:0.56–0.68), older adults (age > 40 years) [aFTR: 0.03, 95% CI: 0.02–0.04], history of tobacco smoking (aFTR: 0.29, 95% CI: 0.13–0.68), asthma exacerbations (aFTR: 0.81, 95%CI: 0.70, 0.94), frequent emergency admissions (aFTR:0.21, 95% CI: 0.17–0.25), longer hospital stay (aFTR:0.07, 95% CI: 0.06–0.09), patients with increased burden of comorbidities (aFTR:0.28, 95% CI: 0.22–0.34), obese male sex (aFTR:0.38, 95% CI: 0.15–0.99), SABA overuse (aFTR: 0.61, 95% CI: 0.44–0.84), moderate (aFTR:0.23, 95% CI: 0.21–0.26), and severe asthma (aFTR:0.10, 95% CI: 0.08–0.12). After adjustment, MA ≥0.80 was significantly associated with 83% delayed time-to-COPD diagnosis [i.e. aFTR =1.83, 95%CI: 1.54–2.17 for PDC]. However, asthma severity significantly modifies the effect of MA independent of tobacco smoking history. The targeted intervention aimed to mitigate early diagnosis of COPD may prioritize enhancing medication adherence among asthma patients to prevent frequent exacerbation during follow-up.

Certified Smoking Cessation Units in Spain: High Potential for Detection of Undiagnosed Chronic Obstructive Pulmonary Disease and Profile of Newly Diagnosed Cases.

PurposeSmokers attending smoking cessation units (SCUs) may offer a unique opportunity for early recognition of undiagnosed chronic obstructive pulmonary disease (COPD). We aimed at assessing the impact of SCUs on the early diagnosis of COPD and describing the clinical and smoking profile of newly-diagnosed COPD cases at SCUs certified by the Spanish Society of Respiratory Diseases and Thoracic Surgery (SEPAR).Patients and MethodsA multicenter cross-sectional observational cohort study (DIPREPOQ study) was performed in eight SEPAR-certified SCUs in Spain. Adult current smokers with no previously diagnosed respiratory disease and having one o more respiratory symptoms were included. Lung functional tests were performed and previously undiagnosed COPD cases were identified and characterized based on national guidelines.ResultsOut of 401 individuals newly attending the SCUs, 252 participants were included and 73 (28.9%) met the definition of previously undiagnosed COPD. A characterization of patients with COPD being newly recognized in SCUs showed: age (mean±SD) 61±9 years; men 59%; active work status 53.1%; functional class I/II dyspnea 82.8%, GOLD state mild/moderate/severe 57%/31%/12%; non-exacerbators 90%, CAT 14±4; emphysema in X-rays 40%. Most common co-morbidities were cardiovascular and psychiatric (anxiety and depression) ones. Usual smoking history included a lengthy smoking history (41±9 years) and a current consumption of 24±9 cigarettes/day.Conclusions and ImplicationsConsistently certified SCUs can have a substantial contribution to early diagnosis of COPD. A typical profile of newly detected cases is reported, with most patients being men at their early sixties, with mild symptoms and with high and lengthy smoking history. Our study reports a high usefulness of lung functional tests to detect undiagnosed COPD in appropriately selected participants attending SCUs at a large national scale, using a standardized methodology. This is likely to be facilitated by the certification of SCUs using well-defined requirements by national scientific societies.

Utilidad del miniespirómetro COPD-6 en el diagnóstico precoz de EPOC

IntroductionEarly diagnosis and treatment of chronic obstructive pulmonary disease (COPD) helps slow progression and reduce complications. The COPD-6 portable spirometer is a tool that may be useful for screening for this disease. ObjectiveTo evaluate the usefulness of the COPD-6 portable spirometer for COPD screening. MethodsWe performed a systematic review of studies evaluating the diagnostic capacity of COPD-6 in the early diagnosis of COPD. Several databases were reviewed, and the quality of the studies included was evaluated with the QUADAS-2 tool. The ROC curve and a weighted estimate of the diagnostic OR were conducted with a bivariate random effects analysis. Possible sources of heterogeneity were explored using subgroup analysis and bivariate meta-regression. ResultsFourteen studies were retrieved (3,986 patients). Statistical heterogeneity in the calculation of sensitivity and specificity was high, with acceptable overall estimates of sensitivity (0.65 [95% CI 0.63-0.68]) and specificity (0.80 [95% CI 0.78-0.81]). The area under the ROC curve was 0.90 and a threshold effect was seen. There were no significant differences in the subgroup analysis (studies conducted in Europe vs Asia, better vs poorer quality studies, and studies initially aimed at diagnosing COPD vs. those aimed at diagnosing an obstructive pattern). The ability of studies using a cut-off>0.75 to screen for COPD (high sensitivity, lower specificity) was acceptable. ConclusionCOPD-6 is a useful test for the early diagnosis of COPD patients.

Analysis in medication treatment and its related factors among patients with chronic obstructive pulmonary disease aged 40 years or older in China, 2014-2015

Objective: To understand the medication treatment rate and its associated factors among chronic obstructive pulmonary disease (COPD) patients aged ≥40 years in China, and to provide basic data for targeted interventions to improve the diagnosis and treatment of COPD patients. Methods: Data were from COPD surveillance of Chinese residents in 2014-2015. Questionnaire and pre-bronchodilator and post-bronchodilator spirometry were performed on all respondents. Individuals with post-bronchodilator FEV(1)/FVC<70% were diagnosed as COPD patients. A total of 9 120 COPD patients were included in the analysis. Based on the complex sampling design, the medication treatment rate and 95%CI among COPD patients were estimated, and the associated factors were analyzed. Results: The medication treatment rate for COPD patients aged ≥40 years was 11.7% (95%CI: 10.2%-13.0%), the treatment rate with inhaled medication was 3.4% (95%CI: 2.9%-4.0%), and the treatment rate with oral or intravenous medication was 10.4% (95%CI: 9.0%-12.0%). All treatment rates were higher in patients who knew that they had COPD before the investigation. The rate of medication treatment in patients aged ≥60 years was higher than that in patients aged <60 years. Medication treatment rate, and oral or intravenous medication treatment rate among women were higher than those among men. These two treatment rates in patients with harmful occupational exposure were higher than those in patients without exposure. The medication treatment rate, and oral or intravenous medication treatment rate in former smokers were higher than those in current smokers and never smokers. Patients who knew that they had COPD before the investigation had higher rates of three treatments than those who were not aware of their disease conditions. Those with respiratory symptoms had higher three treatments rates than those without symptoms. Conclusion: In China, the rate of medication treatment for COPD patients aged ≥40 years old, especially the rate of inhaled medication treatment was very low. Being aware of their own COPD status and the emergence of respiratory symptoms were important factors associated with COPD medication treatment. Early diagnosis of COPD should be strengthened and the level of standardized treatment for patients should be improved.