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Introduction: The use of clinical characteristics to differentiate obstructive lung diseases remains unclear, with limited supporting studies. This study aims to assess the effectiveness of signs and symptoms from clinical questionnaires in diagnosing obstructive lung diseases., Methods: This prospective cohort study included patients diagnosed with chronic obstructive pulmonary disease (COPD), asthma, or asthma-COPD overlap (ACOS) based on spirometry. Clinical symptoms were gathered using questionnaires and incorporated into a multinomial logistic prediction model to evaluate their role in diagnosing obstructive lung diseases., Results: A total of 1443 patients were analyzed: 177 (12.3%) with COPD, 135 (9.4%) with asthma, 163 (11.3%) with ACOS, and 968 (67.1%) without obstruction. The average age was 64 years (SD 13.23), with COPD patients being older than those with asthma or ACOS. Forced expiratory volume in the first second of expiration/forced vital capacity ratios after bronchodilator use were 61.1 (SD 8.48) for COPD, 78.9 (SD 6.99) for asthma, 57.3 (SD 9.48) for ACOS, and 81.7 (SD 7.15) for non-obstructed patients. The area under the curve for diagnosing COPD was 0.75, for asthma 0.68, and for ACOS 0.78 (all P P  <  0.001)., Conclusions: Clinical variables can identify patients with obstructive lung diseases effectively, offering strong diagnostic performance and precision., (Copyright© Board of Regents of the University of Wisconsin System and The Medical College of Wisconsin, Inc.)

Background: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systemic effects, e.g., heart failure or voice distortion. However, the systemic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systemic effects could be helpful to detect the condition in its early stages., Objective: The proposed study aims to explore whether the voice features extracted from the vowel "a" utterance carry any information that can be predictive of COPD by employing Machine Learning (ML) on a newly collected voice dataset., Methods: Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel "a" utterance commenced following an information and consent meeting with each participant using the VoiceDiagnostic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature-importance measures., Results: The classifiers RF, SVM, and CB achieved a maximum accuracy of 77 %, 69 %, and 78 % on the test set and 93 %, 78 % and 97 % on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82 % and AP of 76 %. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order., Conclusion: This study concludes that the utterance of vowel "a" recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis., Clinical Trial Registration Number: NCT05897944., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alper Idrisoglu reports financial support provided by the Excellence Center at Linköping and Lund in Information Technology (ELLIIT). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Electronic health record databases can facilitate epidemiology research regarding diseases such as chronic obstructive pulmonary disease (COPD), a common medical condition worldwide. We aimed to assess the validity of International Classification of Diseases, 9th Revision (ICD-9) code algorithms for identifying COPD in Hong Kong's territory-wide electronic health record system, the Clinical Data Analysis and Reporting System (CDARS)., Methods: Adult patients diagnosed with COPD at all public hospitals in Hong Kong and specifically at Queen Mary Hospital from 2011 to 2020 were identified using the ICD-9 code 496 (Chronic airway obstruction, not elsewhere classified) within the CDARS. Two respiratory specialists reviewed clinical records and spirometry results to confirm the presence of COPD in a randomly selected group of cases., Results: During the study period, 93 971 and 2479 patients had the diagnostic code for COPD at all public hospitals in Hong Kong and specifically at Queen Mary Hospital, respectively. Two hundred cases were randomly selected from Queen Mary Hospital for validation using medical records and spirometry results. The overall positive predictive value was 81.5% (95% confidence interval=76.1%-86.9%). We also developed an algorithm to identify COPD cases in our cohort., Conclusion: This study represents the first validation of ICD-9 coding for COPD in the CDARS. Our findings demonstrated that the ICD-9 code 496 is a reliable indicator for identifying COPD cases, supporting the use of the CDARS database for further clinical research concerning COPD., Competing Interests: All authors have disclosed no conflicts of interest.

The current classification fails to fully meet the needs of physicians and COPD patients in clinical practice. The future classification should aim to prevent the development of disease in high-risk groups, enable early diagnosis before irreversible damage occurs, identify subgroups, and provide pathophysiological insight to inspire drug development. It would be reasonable to thoroughly examine and assess the contributing factors such as an increase in the annual decline of (FEV1), bronchial hyperresponsiveness, variable obstruction, FEV1 % predicted, blood eosinophil count, preserved ratio impaired spirometry (PRISm), and the subgroups of group B as potential additions to the new classification.

Characterization of lung sounds (LS) is indispensable for diagnosing respiratory pathology. Although conventional neural networks (NNs) have been widely employed for the automatic diagnosis of lung sounds, deep neural networks can potentially be more useful than conventional NNs by allowing accurate classification without requiring preprocessing and feature extraction. Utilizing the long short-term memory (LSTM) layers to reveal the sequence-based properties of the LS time series, a novel architecture consisting of a cascade of convolutional long short-term memory (ConvLSTM) and LSTM layers, namely ConvLSNet is developed, which permits highly accurate diagnosis of pulmonary disease states. By modeling the multichannel lung sounds through the ConvLSTM layer, the proposed ConvLSNet architecture can concurrently deal with the spatial and temporal properties of the six-channel LS recordings without heavy preprocessing or data transformation. Notably, the proposed model achieves a classification accuracy of 97.4 % based on LS data corresponding to three pulmonary conditions, namely asthma, COPD, and the healthy state. Compared with architectures consisting exclusively of CNN or LSTM layers, as well as those employing a cascade integration of 2DCNN and LSTM layers, the proposed ConvLSNet architecture exhibited the highest classification accuracy, while imposing the lowest computational cost as quantified by the number of parameters, training time, and learning rate., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The Lancet Commissions on COPD recommended a new classification based on five main risk factors. Patients with COPD were prospectively enrolled in a Korean COPD subgroup study cohort between April 2012 and June 2022. Patients were classified according to the etiologies (Type 1: Genetically determined (COPD-G), Type 2: Abnormal lung development (COPD-D), Type 3: Infections (COPD-I), Type 4: Cigarette smoking (COPD-C), Type 5: Biomass and pollution (COPD-P)). The database enrolled 3476 patients. Among 3392 patients, 52 (2 %), 1339 (39 %), 2930 (86 %), and 2221 (65 %) were compatible with type 2 (COPD-D), 3 (COPD-I), 4 (COPD-C), and 5 (COPD-P), respectively. Most patients (71 %, 2405) had multiple risk factors contributing to their COPD. However, 93, 712, and 182 patients had only type 3 (COPD-I), 4 (COPD-C), and 5 (COPD-P), respectively. Type 3 (COPD-I) only patients were significantly younger, more often female, and had lower lung function. Both the rate and frequency of severe exacerbations were significantly higher in type 3 (COPD-I) only patients (p = 0.038 and p = 0.048, respectively). Compared with type 5 (COPD-P) only, type 3 (COPD-I) only was significantly associated with the risk of severe exacerbation (Odds ratio, 5.7 [95 % CI, 1.0-32.4]; P = 0.049, incident rate ratio, 8.7 [95 % CI, 1.7-44.0]; P = 0.009). Many patients were affected by multiple factors. Therefore, it is important to consider not only smoking history, but also other potential risk factors when evaluating patients with COPD. Further research is needed to explore the implications of this new COPD classification system for clinical practice and treatment strategies., Competing Interests: Declaration of competing interest The authors have no potential conflict of interest relevant to this article., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Recent guidelines for spirometry interpretation recommend both race-neutral reference equations and use of z score thresholds to define severity of airflow obstruction., Research Question: How does the transition from race-specific to race-neutral equations impact severity classifications for patients with COPD when using % predicted vs z score thresholds, and do changes in severity correspond to clinical risk?, Study Design and Methods: This retrospective cohort study included Black and White patients with COPD and available spirometry from the Johns Hopkins Health System. Global Lung Function Initiative (GLI) 2012 (race-specific) equations and GLI Global (race-neutral) equations were used to determine FEV 1 % predicted and z score values. Patients were classified as having mild, moderate, or severe disease according to % predicted or z score thresholds. Associations between a change in severity classification from race-specific to race-neutral with COPD exacerbations and all-cause hospitalizations were evaluated using logistic regression., Results: This cohort included 13,324 patients, of whom 9,232 patients (69.3%) were White (mean age, 65.7 years) and 4,092 patients (30.7%) were Black (mean age, 61.1 years). More Black than White patients showed a change in severity classification between approaches when using % predicted thresholds (20.2% vs 6.1%; P < .001), but not with z score thresholds (12.6% vs 12.3%; P = .68). An increased severity classification with a race-neutral approach was associated with increased risk of exacerbation when using z score thresholds (OR, 2.34; 95% CI, 1.51-3.63), but not when using % predicted thresholds (OR, 1.08; 95% CI, 0.61-1.93). A decreased severity classification with a race-neutral approach was associated with lower risk of exacerbation with both % predicted (OR, 0.49; 95% CI, 0.28-0.87) and z score (OR 0.67; 95% CI, 0.50-0.90) thresholds., Interpretation: The proportions of Black and White individuals reclassified were similar with z score thresholds, and changes in severity corresponded to clinical risk with z scores. These results support recent recommendations for use of race-neutral equations and z score thresholds for spirometry interpretation., Competing Interests: Financial/Nonfinancial Disclosures None declared., (Copyright © 2024 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning., Methods: The retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier., Results: 104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87., Conclusions: The proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole., (© 2024. The Author(s).)

In 2017, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) substantially changed its ABCD group categorization. Although several studies had been conducted to assess the impact of the new GOLD category, there was no research on the change in the GOLD classification in Vietnam. This retrospective analysis was conducted at the Asthma and Chronic Obstructive Pulmonary Disease (COPD) Clinic at the University Medical Center in Ho Chi Minh City, Vietnam. Our study population comprised patients visiting the medical center from January 2018 to January 2020. We categorized patients' demographics, clinical characteristics, and pharmacotherapy based on GOLD 2011 and 2017 guidelines. A comparison between the two versions was also determined. A total of 457 patients were included in this study. The percentage of groups A, B, C, and D according to GOLD 2011 was 5%, 20.8%, 13.1%, and 61.1%, respectively, and according to GOLD 2017, it was 6.1%, 34.1%, 12%, and 47.8%, respectively. In terms of gender, male patients constituted nearly 95% of the study's population (433/457 patients). Regarding pharmacotherapy, approximately 20% of the low-risk group (groups A and B) was overtreated with inhaled corticosteroid (ICS) components: long-acting β-agonists (LABA) + ICS (15.8%) and long-acting muscarinic antagonist + LABA + ICS (3.8%). There were 13.3% and 1.1% of patients transferred from D to B and from C to A, respectively. All of them had a lower predicted percentage of forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), and FEV1/FVC than the patients who remained in groups B or A (p<0.005). This is the first research in Vietnam to show the distribution of COPD patients using both the GOLD 2011 and GOLD 2017 criteria. 14% of patients were reclassified from high-risk to low-risk groups when changing from the 2011 version to the 2017 one, and there was discordance of medications between guidelines and real-life practice. Therefore, clinicians should use their clinical competence to consider patients' conditions before deciding on the appropriate therapeutic approach. Consequently, further studies were required to evaluate the effect of the change in the GOLD classification.

To estimate the severity of the disease in outpatients with chronic obstructive pulmonary disease (COPD) in Hunan Province, China and use the subgroup analysis to evaluate the reliability of the new comprehensive evaluation of Global Initiative for Chronic Obstructive Lung Disease (GOLD). COPD outpatients from 12 medical centers in Hunan Province, China were stratified into groups A-D, and group D patients were further stratified into subgroups D 1 -D 3 according to the GOLD 2016 and 2019 comprehensive assessment. Demography, clinical characteristics and medications were compared among groups. In 1017 COPD outpatients, the distribution from group A to D and subgroup D 1 to D 3 was 41 (4.0%), 249 (24.5%), 17 (1.7%), 710 (69.8%) and 214 (30.2%), 204 (28.7%), 292 (41.1%), according to GOLD 2016. In terms of demographic and clinical characteristics related to A-D groups, there was a significant difference in COPD assessment test (CAT), modified Medical British Research Council (mMRC), the clinical COPD questionnaire(CCQ), age, BMI, education level, smoking history, comorbidities, the course of chronic bronchitis/emphysema, number of exacerbations/hospitalisations in the previous year, treatment protocols, forced expiratory volume in one second (FEV1) % predicted, and FEV1/forced vital capacity (FVC) (p < 0.01). Furthermore, some patients in groups C-D regrouped to groups A-B were all C 1 and D 1 subgroups according to GOLD 2019. Comparing subgroup D 1 with group B, subgroup D 2 and subgroup D 3 , it was found that the demography, clinical characteristics and medications of subgroup D 1 were the closest to group B, according to GOLD 2016 (p < 0.01). The disease severity of outpatients with COPD in Hunan Province was more pronounced in group B and D and patients in groups A-D had different demography, clinical characteristics and medications. Subgroup analysis can explain to a certain extent that GOLD2019's new comprehensive assessment is more reliable than GOLD 2016., (© 2022. The Author(s).)

Importance: Most of the global morbidity and mortality in chronic obstructive pulmonary disease (COPD) occurs in low- and middle-income countries (LMICs), with significant economic effects., Objective: To assess the discriminative accuracy of 3 instruments using questionnaires and peak expiratory flow (PEF) to screen for COPD in 3 LMIC settings., Design, Setting, and Participants: A cross-sectional analysis of discriminative accuracy, conducted between January 2018 and March 2020 in semiurban Bhaktapur, Nepal; urban Lima, Peru; and rural Nakaseke, Uganda, using a random age- and sex-stratified sample of the population 40 years or older., Exposures: Three screening tools, the COPD Assessment in Primary Care to Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE; range, 0-6; high risk indicated by a score of 5 or more or score 2-5 with low PEF [<250 L/min for females and <350 L/min for males]), the COPD in LMICs Assessment questionnaire (COLA-6; range, 0-5; high risk indicated by a score of 4 or more), and the Lung Function Questionnaire (LFQ; range, 0-25; high risk indicated by a score of 18 or less) were assessed against a reference standard diagnosis of COPD using quality-assured postbronchodilator spirometry. CAPTURE and COLA-6 include a measure of PEF., Main Outcomes and Measures: The primary outcome was discriminative accuracy of the tools in identifying COPD as measured by area under receiver operating characteristic curves (AUCs) with 95% CIs. Secondary outcomes included sensitivity, specificity, positive predictive value, and negative predictive value., Results: Among 10 709 adults who consented to participate in the study (mean age, 56.3 years (SD, 11.7); 50% female), 35% had ever smoked, and 30% were currently exposed to biomass smoke. The unweighted prevalence of COPD at the 3 sites was 18.2% (642/3534 participants) in Nepal, 2.7% (97/3550) in Peru, and 7.4% (264/3580) in Uganda. Among 1000 COPD cases, 49.3% had clinically important disease (Global Initiative for Chronic Obstructive Lung Disease classification B-D), 16.4% had severe or very severe airflow obstruction (forced expiratory volume in 1 second <50% predicted), and 95.3% of cases were previously undiagnosed. The AUC for the screening instruments ranged from 0.717 (95% CI, 0.677-0.774) for LFQ in Peru to 0.791 (95% CI, 0.770-0.809) for COLA-6 in Nepal. The sensitivity ranged from 34.8% (95% CI, 25.3%-45.2%) for COLA-6 in Nepal to 64.2% (95% CI, 60.3%-67.9%) for CAPTURE in Nepal. The mean time to administer the instruments was 7.6 minutes (SD 1.11), and data completeness was 99.5%., Conclusions and Relevance: This study demonstrated that screening instruments for COPD were feasible to administer in 3 low- and middle-income settings. Further research is needed to assess instrument performance in other low- and middle-income settings and to determine whether implementation is associated with improved clinical outcomes.

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity., Competing Interests: The authors have declared that no competing interests exist.

Aim: The mortality risk attributable to the classifications of chronic obstructive pulmonary disease (COPD) remains unclear. We investigated the associations of mortality with COPD classifications and reduced lung function in a large longitudinal cohort in Taiwan., Methods: A total of 388,401 adults (≥25 years of age) were recruited between 1996 and 2016 underwent 834,491 medical examinations including spirometry. We used the Global Initiative for Chronic Obstructive Lung Disease (GOLD) to establish the COPD classifications. A time-dependent Cox regression model was used to investigate the associations between the morality risk and COPD classifications. We also examined the associations between mortality and lung function., Results: The mean age of the participants was 42.1 years, and the median follow-up duration was 16.2 years. We identified 28,283 natural-cause deaths, and the mortality rate was 4.7 per 1,000 person-years. The hazard ratios (HRs) [95%confidence interval (95%CI)] of mortality in the participants with restrictive spirometry pattern and COPD GOLD Ⅰ-Ⅳ were 1.31 (1.27-1.35), 1.18 (1.00-1.39), 1.43 (1.35-1.51), 1.78 (1.66-1.90), and 2.13 (1.94-2.34), respectively, with reference to the participants with normal lung function. The natural-cause mortality risk increased by 33% [HR(95%CI): 1.33 (1.28-1.39)] for participants with COPD. Reduced lung function was also associated with a higher mortality risk., Conclusions: A more advanced classification of COPD was associated with a greater increase in the mortality risk. Our study suggests that early detection of COPD and slowing the disease progress in patients with COPD are crucial for mortality prevention., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Chronic obstructive pulmonary disease (COPD) is a multifactorial clinical condition, characterized by chronic progressive (or worsening) respiratory symptoms, structural pulmonary abnormalities, and impaired lung function, and is often accompanied by multiple, clinically significant comorbid disorders. In 2017, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) issued a new report on COPD prevention, diagnosis and management, aiming at personalizing the maintenance therapeutic approach of the stable disease, based on the patients' symptoms and history of exacerbations (ABCD assessment approach). Our objective was to evaluate the implementation of GOLD suggestions in everyday clinical practice in Greece., Methods: This was a cross-sectional observational study. Sixty-five different variables (demographics, vital sign measurements, COPD-related medical history parameters, comorbidities, vaccination data, COPD severity based on spirometry measurements, COPD stage based on the ABCD assessment approach, COPD treatments) were collected from 3615 nation-wide COPD patients (Greece)., Results: The mean age at the time of initial COPD diagnosis was 63.8 (± 10.2). Almost 60% of the subjects were classified into group B, while the remaining patients were falling into groups A (18%) and D (21%), and only a small minority of patients belonged to Group C, according to the ABCD assessment approach. The compliance of respiratory physicians to the GOLD 2017 therapeutic suggestions is problematic, especially when it comes to COPD patients belonging to Group A., Conclusion: Our data provide valuable information regarding the demographic and medical profile of COPD patients in Greece, the domains which the revised ABCD assessment approach may show some clinical significance on, and the necessity for medical practitioners dealing with COPD patients to adhere closer to international recommendations for the proper management of the disease., (© 2021. The Author(s).)

While there is no cure for chronic obstructive pulmonary disease (COPD), its progressive nature and the formidable challenge to manage its symptoms warrant a more extensive study of the pathogenesis and related mechanisms. A new emphasis on COPD study is the change of energy metabolism. For the first time, this study investigated the anaerobic and aerobic energy metabolic pathways in COPD using the metabolomic approach. Metabolomic analysis was used to investigate energy metabolites in 140 COPD patients. The significance of energy metabolism in COPD was comprehensively explored by the Global Initiative for Chronic Obstructive Lung Disease-GOLD grading, acute exacerbation vs. stable phase (either clinical stability or four-week stable phase), age group, smoking index, lung function, and COPD Assessment Test (CAT) score. Through comprehensive evaluation, we found that COPD patients have a significant imbalance in the aerobic and anaerobic energy metabolisms in resting state, and a high tendency of anaerobic energy supply mechanism that correlates positively with disease progression. This study highlighted the significance of anaerobic and low-efficiency energy supply pathways in lung injury and linked it to the energy-inflammation-lung ventilatory function and the motion limitation mechanism in COPD patients, which implies a novel therapeutic direction for this devastating disease.

Introduction: Since GOLD 2017 separates spirometry results from 'ABCD' groups, there have been some changes to the stages of COPD patients. Our aim was to investigate the shifts in COPD groups after GOLD 2017., Methods: COPD patients from outpatient clinics of 3 hospitals in Turkey were stratified into old and new ABCD groups according to exacerbation history, mMRC evaluation and spirometry results for both GOLD 2011 and 2017 assessments. Treatment protocols were also evaluated if they were suitable for both classifications., Results: There were 578 patients (334 men, 244 women) with a mean age of 65.21±10.42. The distribution of patients from group A to D was 28%, 15%, 15%, 42% (GOLD 2011) and 36%, 22%, 7% and 35% (GOLD 2017) respectively. There were shifts from group C to A (53.4%) and D to B (18.4%). The treatment suitability was 66.3% in GOLD 2011 and 60.9% in GOLD 2017. The most common inappropriate treatment protocol was triple therapy. Presence of exacerbations in last year, mMRC score, FEV1 level (p < 0.01 for three parameters) and proportion of males (p = 0.029) were statistically significantly higher in groups C and D of GOLD 2017 compared with new patients in group A and B of GOLD 2017 (shifted from GOLD 2011's C-D groups)., Conclusion: There were shifts from group C to A and D to B with GOLD 2017, which means some high-risk COPD patients were reclassified into low-risk groups. Despite guideline updates, there are still many COPD patients with overtreatment. To our knowledge, this is the first study examining transitions between COPD groups after GOLD 2017 in Turkey., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The purpose of the Asthma and Respiratory Foundation of New Zealand's COPD Guidelines: Quick Reference Guide is to provide simple, practical, evidence-based recommendations for the diagnosis, assessment, and management of chronic obstructive pulmonary disease (COPD) in clinical practice. The intended users are health professionals responsible for delivering acute and chronic COPD care in community and hospital settings, and those responsible for the training of such health professionals., Competing Interests: Dr Young reports: I receive honorarium from GSK for giving educational talks on COPD management or attending advisory meetings. However, this did not impact on my contri-bution to this guideline. Dr Baggott reports personal fees from Astra Zeneca, personal fees from Novartis, outside the submitted work. Nicola Corna reports other from Boehring-er Ingelheim, other from Astra Zeneca, other from Astra Zeneca, grants from Adherium, outside the submitted work. Dr Fingleton reports grants, personal fees and non-financial support from AstraZeneca, grants from Genentech, grants, personal fees and non-financial support from GlaxoSmithKline, personal fees and non-financial support from Boheringer lngleheim, outside the submitted work. Dr Hardy reports non-financial support from As-trazeneca, outside the submitted work. Dr Hancox reports grants from Astra Zeneca, grants from GlaxoSmithKline, personal fees from Menarini, other from Boehringer Ingelheim, outside the submitted work.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.

Introduction: Chronic obstructive pulmonary disease (COPD) is a major global health problem causing high morbidity and mortality. Easy to apply, cost-effective, and available COPD severity measures are required in rural areas. Objective: To assess the validity and reliability of the COPD Severity Score (COPDSS) in Colombian patients attended in primary care. Materials and methods: A cross-sectional study was conducted in a sample of 100 patients diagnosed with COPD according to GOLD guidelines. Convergent validity was assessed by correlating the COPDSS results with other important variables such as those assessed through the Chronic Respiratory Disease Questionnaire (CRQ) and the forced expiratory volume in one second (FEV1). Results: The COPDSS showed a significant correlation with the FEV1 (r=-0.33), with the CRQ (r=-0.57) and its dimensions: Dyspnea (r=0.51), fatigue (r=0.53), emotional function (r=0.43), and disease control (r=0.50). The factor analysis identified a single factor with a cumulative variance of 59.1%. The internal consistency analysis showed an adequate Cronbach’s alpha of 0.76. Conclusions: The use of the COPDSS as an assessment test for COPD patients in Colombia showed adequate validity, reliability, and easy application in primary care.

Patients who suffer from chronic obstructive pulmonary disease (COPD) often experience deterioration of baseline respiratory symptoms, acute exacerbations of COPD (AECOPD), that become more frequent with disease progression. Based on symptom severity, approximately 20% of these patients will require hospitalization. The most common indicators for intensive care unit (ICU) admission have been found to be worsening or impending respiratory failure and hemodynamic instability. Bacterial and viral bronchial infections are the causative triggers in the majority of COPD exacerbations in the ICU, with a comprehensive assessment revealing them in 72% of cases. The distribution of bacterial pathogens involved in AECOPD requiring ICU admission show an increased incidence of gram-negative respiratory isolates, including Pseudomonas and Enterobacteriaceae spp., when compared with outpatient exacerbations. Evaluation of these patients requires careful attention to comorbid conditions. An effort to rapidly obtain lower respiratory samples for microbiological samples prior to initiation of antibiotics should be made as adequate samples can guide subsequent modifications of antibiotic treatment if the clinical response to empiric treatment is poor. Empiric antibiotic treatment should be promptly initiated in all patients with a major consideration for the choice being the presence of risk factors for Pseudomonas infection. Evaluation of clinical response at 48 to 72 hours is crucial, and total duration of antibiotics of 5 to 7 days should be adequate., Competing Interests: None declared., (Thieme. All rights reserved.)

International Classification of Disease 10 (ICD-10) codes record hospital admissions. We aimed to measure the accuracy of COPD exacerbation (ECOPD) codes and examine coding practices for COPD exacerbation.Prospective screening and ICD-10 codes were used to identify potential ECOPD within the DECAF internal validation cohort. Two coding searches were performed. The first search identified patients with an ECOPD discharge code, and a second, broad search was developed to identify all clinically confirmed ECOPD.717 of 1,122 (64%) patients with an ECOPD code had confirmed ECOPD. Common reasons for misclassification in the 405 patients who did not have an ECOPD included: lack of obstructive spirometry to diagnose COPD; and hospital admission due to progressive malignancy, asthma or cardiovascular disease. The broad search identified an additional 297 patients with ECOPD missed by the ECOPD codes. The vast majority of this group had pneumonia complicating ECOPD.ECOPD codes are insufficiently reliable to identify patients with clinically confirmed ECOPD for the purposes of audit or research. Search strategies should include pneumonia codes, specialist review of medical notes and spirometry confirmation of COPD.

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) ABCD classification tool has been used to assess the symptom burden and exacerbation risk of patients with chronic obstructive pulmonary disease (COPD). An area requiring further exploration is the relationship between the GOLD classification's basic components and the measurements acquired by Sit-to-Stand tests (STST). We aimed to study the relationship between STST and the component of the GOLD classification tool. This study was conducted on a sample of 42 COPD subjects with patient history, COPD assessment test (CAT) and spirometry. 5STST performance time and the number 30s-STST repetitions showed differences of statistical significance in COPD subjects considered to be more symptomatic and in subjects with high risk of future exacerbations. Both STSTs correlated significantly with forced expiratory volume in one second % predicted (FEV1%), CAT, number of acute exacerbations in the past year and number of hospitalized exacerbations in the past year. STST performance correlates significantly with items of the CAT questionnaire that assess breathlessness, limitation of activities, confidence and lack of energy. Using multivariate analysis, age, FEV1% and CAT score manifested the strongest negative association with STST performance. 5STST performance time and the number 30s-STST repetitions in COPD patients correlates with the level of symptoms and the risk of future exacerbations that define groups A-D based on GOLD 2018 classification tool (at the time of data acquisition). The correlation of STST performance with CAT score involves specific items of the questionnaire that assess breathlessness, limitation of activities, confidence and lack of energy.

Chronic obstructive pulmonary disease (COPD) is a progressive chronic inflammatory disease and the third cause of death worldwide in 2016. COPD epidemiology is well documented in high-income countries where the disease is well managed. However, the disease is neglected in low-income countries and there is lack of data. Our study aims to identify COPD patients' characteristics and hospital admission causes, and to determine disease etiologies and associated factors. A retrospective study was conducted in COPD Algerian patients using medical record data collected from January 2007 to May 2017 at the pulmonology department of the Belloua Hospital of Tizi-Ouzou city. Out of 133 hospital admissions for COPD during the study period, only 120 records were found and analyzed. Most of the admitted patients were men (96%) and the mean age was 74.29±9.56 years. Among them, 78.7% were in the GOLD stage III or IV and 9 deaths (7.5%) were recorded during the study period. Interestingly, disease severity is associated with increasing age of the patients and mortality (p=0.01 and p=0.02, respectively). Risk factors include cigarette smoking (93%), history of medical conditions (36.66%) with the most prevalent conditions being emphysema (38.63%) and asthma (27.27%), the cold season (47%), and occupational exposures (58%). Most of the admissions (64.16%) were due to acute dyspnea and 21.66 % to respiratory infections, however, 34.16 % of patients were readmitted at least one time. Comorbidities were observed in 57.5% of the patients, including cardiovascular diseases (63.76%) and diabetes (18.84%). These results show that COPD severity is associated with age and mortality. Better understanding of the COPD etiologies and the causes of hospital admission will lead to more effective management of the disease.

Pulmonary hypertension and pulmonary vascular remodeling (PVR) are common in many lung diseases leading to right ventricular dysfunction and death. Differences in PVR result in significant prognostic divergences in both the pulmonary arterial and venous compartments, as in pulmonary arterial hypertension (PAH) and pulmonary veno-occlusive disease (PVOD), respectively. Our goal was to identify compartment-specific molecular hallmarks of PVR, considering the risk of life-threatening pulmonary edema in PVOD, if treated by conventional pulmonary hypertension therapy. Formalin-fixed and paraffin-embedded tissues from fresh explanted human lungs of patients with PVOD (n = 19), PAH (n = 20), idiopathic pulmonary fibrosis (n = 13), and chronic obstructive pulmonary disease (n = 15), were analyzed for inflammation and kinome-related gene regulation. The generated neuronal network differentiated PVOD from PAH samples with a sensitivity of 100% and a specificity of 92% in a randomly chosen validation set, a level far superior to established diagnostic algorithms. Further, various alterations were identified regarding the gene expression of explanted lungs with PVR, compared with controls. Specifically, the dysregulation of microtubule-associated serine/threonine kinase 2 and protein-o-mannose kinase SGK196 in all disease groups suggests a key role in pulmonary vasculopathy for the first time. Our findings promise to help develop novel target-specific interventions and innovative approaches to facilitate clinical diagnostics in an elusive group of diseases., (Copyright © 2020 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease, with oxidative stress and inflammation implicated in its development. Uric acid (UA) could exert anti-oxidative, pro-oxidative or pro-inflammatory effects, depending on the specific context. It was recently shown that soluble UA, and not just its crystals, could activate the nucleotide-binding oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome, leading to interleukin (IL)-1β secretion. We aimed to assess the differences in blood levels of UA and its ratio with creatinine (UCR) between COPD patients and healthy subjects, as well as their association with disease severity, smoking status, common COPD comorbidities and therapy regimes. The diagnostic characteristics of UA and UCR were also explored. This study included 109 stable COPD patients and 95 controls and measured white blood cells (WBC), C-reactive protein (CRP), fibrinogen (Fbg), IL-1β, creatinine (CREAT) and UA. All of the parameters were increased in COPD patients, except for CREAT. UA and UCR were positively associated with WBC, CRP and IL-1β. COPD smokers had lower UA and UCR values. Common COPD therapy did not affect UA or UCR, while patients with cardiovascular diseases (CVD) had higher UA, but not UCR, levels. Patients with higher UCR values showed worse disease-related outcomes (lung function, symptoms, quality of life, history of exacerbations, BODCAT and BODEx). Also, UCR differentiated patients with different severity of airflow limitation as well as symptoms and exacerbations. The great individual predictive potential of UCR and IL-1β was observed with their odds ratios (OR) being 2.09 and 5.53, respectively. Multiparameter models of UA and UCR that included IL-1β were able to correctly classify 86% and 90% of cases, respectively. We suggest that UA might be a useful biomarker when combined with IL-1β, while UCR might be even more informative and useful in overall COPD assessments., Competing Interests: The authors have declared that no competing interests exist.

This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. A total of 10 300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a ten-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We, thus, demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes., (Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Objectives: Triple therapy is indicated for patients with very severe chronic obstructive pulmonary disease (COPD). Use of this treatment in the appropriate patient population is important to ensure optimal outcomes. This study quantified the use of triple therapy and assessed concordance with 2013-2016 Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommendations within a national health plan., Study Design: Retrospective cohort study using data from a large national health plan., Methods: To estimate the prevalence of triple therapy using claims data, patients in the first of 2 cohorts were indexed on their first diagnosis of COPD between January 1, 2012, and December 31, 2014, and required to have 24 months postindex continuous enrollment. To assess concordance with GOLD recommendations, a second cohort was created and indexed on the date of triple therapy initiation between January 1, 2013, and November 30, 2016, and required to have 12 months preindex and 1 month postindex continuous enrollment. For both cohorts, patients were aged 40 years or older, with no International Classification of Diseases code for asthma, cystic fibrosis, or lung cancer during the study period., Results: In the first cohort of 92,248 patients with COPD receiving any COPD maintenance medication, 17% were prescribed triple therapy. In the second cohort (n = 19,645), the majority (60%) of patients on triple therapy were classified as GOLD group A or B (ie, no evidence of any exacerbation or only 1 exacerbation not resulting in hospitalization at baseline)., Conclusions: Results showed that triple therapy was often prescribed among patients classified as GOLD group A or B. Additional research is required, however, to further assess whether these patients may have had an exacerbation that was not evident in claims data. Treatment of COPD should be individualized to optimize outcomes and reduce adverse events.

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches - called "switch genes" - for the disease. Genes contributing to common biological processes or defining given cell types are usually co-regulated and co-expressed, forming expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.

Rationale: The decades-long progression of chronic obstructive pulmonary disease (COPD) renders identifying different trajectories of disease progression challenging. Objectives: To identify subtypes of patients with COPD with distinct longitudinal progression patterns using a novel machine-learning tool called "Subtype and Stage Inference" (SuStaIn) and to evaluate the utility of SuStaIn for patient stratification in COPD. Methods: We applied SuStaIn to cross-sectional computed tomography imaging markers in 3,698 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-4 patients and 3,479 controls from the COPDGene (COPD Genetic Epidemiology) study to identify subtypes of patients with COPD. We confirmed the identified subtypes and progression patterns using ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) data. We assessed the utility of SuStaIn for patient stratification by comparing SuStaIn subtypes and stages at baseline with longitudinal follow-up data. Measurements and Main Results: We identified two trajectories of disease progression in COPD: a "Tissue→Airway" subtype ( n  = 2,354, 70.4%), in which small airway dysfunction and emphysema precede large airway wall abnormalities, and an "Airway→Tissue" subtype ( n  = 988, 29.6%), in which large airway wall abnormalities precede emphysema and small airway dysfunction. Subtypes were reproducible in ECLIPSE. Baseline stage in both subtypes correlated with future FEV 1 /FVC decline ( r  = -0.16 [ P  < 0.001] in the Tissue→Airway group; r  = -0.14 [ P  = 0.011] in the Airway→Tissue group). SuStaIn placed 30% of smokers with normal lung function at elevated stages, suggesting imaging changes consistent with early COPD. Individuals with early changes were 2.5 times more likely to meet COPD diagnostic criteria at follow-up. Conclusions: We demonstrate two distinct patterns of disease progression in COPD using SuStaIn, likely representing different endotypes. One third of healthy smokers have detectable imaging changes, suggesting a new biomarker of "early COPD."

Background: Multiple studies have identified COPD subtypes by using visual or quantitative evaluation of CT images. However, there has been no systematic assessment of a combined visual and quantitative CT imaging classification. We integrated visually defined patterns of emphysema with quantitative imaging features and spirometry data to produce a set of 10 nonoverlapping CT imaging subtypes, and we assessed differences between subtypes in demographic features, physiological characteristics, longitudinal disease progression, and mortality., Methods: We evaluated 9,080 current and former smokers in the COPDGene study who had available volumetric inspiratory and expiratory CT images obtained using a standardized imaging protocol. We defined 10 discrete, nonoverlapping CT imaging subtypes: no CT imaging abnormality, paraseptal emphysema (PSE), bronchial disease, small airway disease, mild emphysema, upper lobe predominant centrilobular emphysema (CLE), lower lobe predominant CLE, diffuse CLE, visual without quantitative emphysema, and quantitative without visual emphysema. Baseline and 5-year longitudinal characteristics and mortality were compared across these CT imaging subtypes., Results: The overall mortality differed significantly between groups (P < .01) and was highest in the 3 moderate to severe CLE groups. Subjects having quantitative but not visual emphysema and subjects with visual but not quantitative emphysema were unique groups with mild COPD, at risk for progression, and with likely different underlying mechanisms. Subjects with PSE and/or moderate to severe CLE had substantial progression of emphysema over 5 years compared with findings in subjects with no CT imaging abnormality (P < .01)., Conclusions: The combination of visual and quantitative CT imaging features reflects different underlying pathological processes in the heterogeneous COPD syndrome and provides a useful approach to reclassify types of COPD., Trial Registry: ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov., (Copyright © 2019 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Purpose: Previous studies have used various definitions to classify chronic obstructive pulmonary disease (COPD) patients into chronic bronchitis (CB) and non-CB patients. This study was performed to identify differences among three definitions of CB based on the classical method, St. George's Respiratory Questionnaire (SGRQ), and the CAT (COPD Assessment Test) score., Patients and Methods: We extracted data from the multicenter Korea COPD Subgroup Study (KOCOSS) cohort, for which patients recruited from among 47 medical centers in South Korea beginning in April 2012. Patients were classified according to three different definitions of CB: 1) classical definition; 2) SGRQ (using questions regarding cough and sputum); and 3) CAT score (comprising cough [CAT1] and sputum [CAT2] subscale scores)., Results: A total of 2694 patients were enrolled in this study. The proportions of CB were 10.8%, 35.8%, and 24.0% according to the classical, SGRQ, and CAT definitions, respectively. The three definitions yielded consistently significant differences between CB and non-CB patients in modified Medical Research Council dyspnea scale CAT score, SGRQ score, number of moderate-to-severe exacerbations per year and forced expiratory volume in 1 second. By three definitions, CB consistently predicted future risk of exacerbation. The kappa coefficient of agreement between the classical definition and SGRQ definition was 0.29, that of the classical definition and CAT definition was 0.32, and that of the SGRQ definition and CAT definition was 0.44., Conclusion: Patients with CB according to the new definitions based on SGRQ or CAT score showed similar clinical characteristics to those defined according to the classical definition. The new CB definitions may be used as alternatives to the classical definition., Competing Interests: The authors have no conflicts of interest., (© 2019 Choi et al.)

The alarming rate of mortality and disability due to Chronic Obstructive Pulmonary Disease (COPD) has become a serious health concern worldwide. The progressive nature of this disease makes it inevitable to detect this disease in its early stages, leads to a greater demand for developing non-obstructive and reliable technology for COPD detection. The use of highly patient-effort dependent, time-consuming, and expensive methods are some major inherent limitations of previous techniques. Lack of knowledge about the disease and inadequacy of proper diagnostic tool for early detection of COPD is another reason behind the 3rd leading cause of death worldwide. For this reason, this study aims to explore the utility of ECG Derived Respiration (EDR) for classification between COPD patients and normal healthy subjects as EDR can be easily extracted from ECG. ECG and respiration signals collected from 30 normal and 30 COPD subjects were analysed. Error calculation and statistical analysis were performed to observe the similarity between original respiration and EDR signal. The morphological pattern changes of respiration and EDR signals were analysed and three different features were extracted from those. Classification was performed by different classifiers employing Decision Tree, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Apart from obtaining comparable classification performance it was seen that EDR has better potential than the original respiration signal for classification of COPD from normal population.

Severe obstructive lung disease, which encompasses asthma, chronic obstructive pulmonary disease (COPD) or features of both, remains a considerable global health problem and burden on healthcare resources. However, the clinical definitions of severe asthma and COPD do not reflect the heterogeneity within these diagnoses or the potential for overlap between them, which may lead to inappropriate treatment decisions. Furthermore, most studies exclude patients with diagnoses of both asthma and COPD. Clinical definitions can influence clinical trial design and are both influenced by, and influence, regulatory indications and treatment recommendations. Therefore, to ensure its relevance in the era of targeted biologic therapies, the definition of severe obstructive lung disease must be updated so that it includes all patients who could benefit from novel treatments and for whom associated costs are justified. Here, we review evolving clinical definitions of severe obstructive lung disease and evaluate how these have influenced trial design by summarising eligibility criteria and primary outcomes of phase III randomised controlled trials of biologic therapies. Based on our findings, we discuss the advantages of a phenotype- and endotype-based approach to select appropriate populations for future trials that may influence regulatory approvals and clinical practice, allowing targeted biologic therapies to benefit a greater proportion and range of patients. This calls for co-ordinated efforts between investigators, pharmaceutical developers and regulators to ensure biologic therapies reach their full potential in the management of severe obstructive lung disease., Competing Interests: Conflict of interest: R.J. Martin reports grants from NHLBI, MedImmune and Chiesi Farmaceutici SpA, personal fees for steering committee work from AstraZeneca, personal fees for consultancy from PMD Healthcare, personal fees (honorarium) from Regeneron, and personal fees for advisory board work from Boehringer Ingelheim, outside the submitted work; he is a member of the NOVELTY Study Scientific Committee. Conflict of interest: E.H. Bel reports that the study and medical writing support was funded by AstraZeneca, during the conduct of the study; grants and personal fees from AstraZeneca, GSK, Novartis and Teva, and personal fees from Boehringer Ingelheim, Sanofi/Regeneron and Vectura, outside the submitted work; she is a member of the NOVELTY Study Scientific Committee. Conflict of interest: I.D. Pavord reports speaker's honoraria, travel expenses and honoraria for attending advisory boards from AstraZeneca, GSK, Boehringer Ingelheim and Teva, grants and speaker's honoraria, travel expenses and honoraria for attending advisory boards from Chiesi, personal fees for advisory board work from Sanofi/Regeneron, Merck, Novartis, Knopp and Roche/Genentech, personal fees for lectures from Circassia and Mundipharma, and grants and personal fees for advisory board work from Afferent, outside the submitted work; he is a member of the NOVELTY Study Scientific Committee. Conflict of interest: D. Price reports that the study was funded by AstraZeneca; grants and personal fees for advisory board membership and travel/accommodation/meeting expenses from Aerocrine, grants from AKL Research and Development Ltd, British Lung Foundation, Respiratory Effectiveness Group and UK National Health Service, personal fees for consultancy and lectures from Almirall and GlaxoSmithKline, personal fees for advisory board membership and consultancy from Amgen, grants and personal fees for advisory board membership, consultancy, lectures and travel/accommodation/meeting expenses from AstraZeneca, Boehringer Ingelheim and Chiesi, personal fees for lectures from Cipla, Kyorin, Merck and Skyepharma, grants and personal fees for advisory board membership, consultancy and lectures from Mylan, grants and personal fees for advisory board membership, consultancy, lectures, manuscript preparation, educational activities and travel/accommodation/meeting expenses from Mundipharma, grants and personal fees for advisory board membership, consultancy and travel/accommodation/meeting expenses from Napp, grants and personal fees for advisory board membership, consultancy, lectures, patient enrolment or completion of research, development of educational materials and travel/accommodation/meeting expenses from Novartis, grants and personal fees for consultancy and lectures from Pfizer, grants and personal fees for advisory board membership and lectures from Regeneron Pharmaceuticals and Sanofi Genzyme, grants and personal fees for advisory board membership, consultancy, lectures, manuscript preparation, patient enrolment or completion of research and travel/accommodation/meeting expenses from Teva, grants and personal fees for consultancy from Theravance, and grants and personal fees for patient enrolment or completion of research from Zentiva (Sanofi Generics), and has participated in peer review for grant committees for Efficacy and Mechanism Evaluation programme and Health Technology Assessment, outside the submitted work; has stock/stock options from AKL Research and Development Ltd which produces phytopharmaceuticals; and owns 74% of the social enterprise Optimum Patient Care Ltd (Australia and UK) and 74% of Observational and Pragmatic Research Institute Pte Ltd (Singapore); he is a member of the NOVELTY Study Scientific Committee. Conflict of interest: H.K. Reddel reports grants, personal fees and non-financial support from AstraZeneca and GlaxoSmithKline, and personal fees from Boehringer Ingelheim, Merck, Novartis, Teva and Mundipharma, outside the submitted work; she is a member of the NOVELTY Study Scientific Committee., (Copyright ©ERS 2019.)

Objectives: This study aims to investigate the impact of respiratory symptoms in current and former smokers with and without obstructive lung disease (OLD) on all-cause mortality., Design: Secondary analysis in a prospective cohort (the Health, Aging and Body Composition study)., Setting: Memphis, Tennessee, and Pittsburgh, Pennsylvania., Participants: Black and white men and women with a history of current and former smoking (N = 596; 63% male and 37% female) aged 70-79 years followed for 13 years. Participants were categorized into 4 mutually exclusive groups based on symptom profile and forced expiratory volume in the 1st second to forced vital capacity ratio. The groups were Less Dyspnea-No OLD (N = 196), More Dyspnea-No OLD (N = 104), Less Dyspnea-With OLD (N = 162), and More Dyspnea-With OLD (N = 134)., Measurements: All-cause mortality., Results: Overall, 53% in Less Dyspnea-No OLD, 63% in More Dyspnea-No OLD, 67% in Less Dyspnea-With OLD, and 84% in More Dyspnea-With OLD died within the 13- year follow up period (log-rank χ 2  = 44.4, P < .0001). The hazard ratio was highest for participants with OLD, both with (HR =1.91, 95% CI 1.44 - 2.54; P < .0001) and without dyspnea (HR = 1.52, 95% CI 1.15 - 2.02; p = .004). Participants without OLD but with dyspnea had a similar risk of death to subjects who had OLD but fewer symptoms., Conclusions: OLD is associated with high risk of death with different risk profiles based on symptom group. Patients with symptoms of shortness of breath without OLD should be considered an at-risk group given their similar mortality to those with OLD with minimal symptoms. J Am Geriatr Soc 67:2116-2122, 2019., (© 2019 The American Geriatrics Society.)

Background: This paper is aimed to (i) develop an innovative classification of COPD, multi-dimensional phenotype, based on a multidimensional assessment; (ii) describe the identified multi-dimensional phenotypes., Methods: An exploratory factor analysis to identify the main classificatory variables and, then, a cluster analysis based on these variables were run to classify the COPD-diagnosed 514 patients enrolled in the STORICO (trial registration number: NCT03105999) study into multi-dimensional phenotypes., Results: The circadian rhythm of symptoms and health-related quality of life, but neither comorbidity nor respiratory function, qualified as primary classificatory variables. Five multidimensional phenotypes were identified: the MILD COPD characterized by no night-time symptoms and the best health status in terms of quality of life, quality of sleep, level of depression and anxiety, the MILD EMPHYSEMATOUS with prevalent dyspnea in the early-morning and day-time, the SEVERE BRONCHITIC with nocturnal and diurnal cough and phlegm, the SEVERE EMPHYSEMATOUS with nocturnal and diurnal dyspnea and the SEVERE MIXED COPD distinguished by higher frequency of symptoms during 24h and worst quality of life, of sleep and highest levels of depression and anxiety., Conclusions: Our results showed that properly collected respiratory symptoms play a primary classificatory role of COPD patients. The longitudinal observation will disclose the discriminative and prognostic potential of the proposed multidimensional phenotype., Trial Registration: Trial registration number: NCT03105999, date of registration: 10th April 2017., Competing Interests: B.F. reports grants and personal fees from ASTRAZENECA (https://www.astrazeneca.com/), grants and personal fees from BAYER (https://www.bayer.com/), personal fees from CHIESI (https://www.chiesi.com/), personal fees from GSK (https://it.gsk.com/), personal fees from GUIDOTTI (https://www.labguidotti.it/), grants and personal fees from GRIFOLS (https://www.grifols.com/en/home), grants from INSMED (https://www.insmed.com/), 408 personal fees from MENARINI (https://www.menarini.it), personal fees from NOVARTIS (https://www.novartis.com/), grants and personal fees from PFIZER (https://www.pfizer.com/), personal fees from TEVA (https://www.tevapharm.com/), personal fees from ZAMBON (https://www.zambon.com/it), outside the submitted work. R.S. and S.L. are employees of Medineos Observational Research (http://www.medineos.com/) and received salary from this company. This does not alter authors adherence to PLOS ONE policies on sharing data and materials. The other authors declare that they have no competing interests.

Chronic obstructive pulmonary disease (COPD) is an umbrella definition encompassing multiple disease processes. COPD heterogeneity has been described as distinct subgroups of individuals (subtypes) or as continuous measures of COPD variability (disease axes). There is little consensus on whether subtypes or disease axes are preferred, and the relative value of disease axes and subtypes for predicting COPD progression is unknown. Using a propensity score approach to learn disease axes from pairs of subtypes, we demonstrate that these disease axes predict prospective forced expiratory volume in 1 s decline and emphysema progression more accurately than the subtype pairs from which they were derived., Competing Interests: Competing interests: JC reports consulting fees and grant support from GSK and Novartis outside the submitted work. MC has received grant support from GSK. In the past 3 years, EKS received honoraria from Novartis for Continuing Medical Education Seminars and grant and travel support from GlaxoSmithKline., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: The association between GOLD categorizations and future exacerbations has not been fully investigated. This study elucidates whether the GOLD 2017 classification is associated with different future exacerbation risk in patients with chronic obstructive pulmonary disease (COPD) compared with the previous GOLD categorization. Another objective was to investigate the impacts of the symptoms and FEV1 on the predicted future exacerbation independently of previous exacerbation history., Methods: We analyzed patients from three prospective COPD cohorts (SNUH, KOCOSS, and KOLD) and evaluated the risk of moderate to severe exacerbation among different models, including GOLD grade (FEV1), GOLD 2011, and GOLD 2017., Results: In total, 611 COPD patients were included (36 from SNUH, 257 from KOCOSS, and 318 from KOLD). GOLD 2017 classification, excluding FEV1% for categorization criteria, showed no differences in future exacerbation risk compared with GOLD grade and GOLD 2011 based on c-statistics. Among those with no frequent exacerbation history and FEV1 ≥50%, the group with more symptoms was significantly associated with future exacerbations than the group with less symptoms. A lower FEV1 (FEV1 <50%) was not associated with a higher future exacerbation risk than a higher FEV1 (FEV1 ≥50%), regardless of prior exacerbation history and symptom group., Conclusion: The GOLD 2017 classification was not different from GOLD grade and GOLD 2011 regarding the association with future exacerbation risk, and there were no significant differences in exacerbation risk according to FEV1%. This suggests that FEV1 might not be an important factor in future exacerbation risk. These results partly support the GOLD 2017 assessment tool., (Copyright © 2019 SEPAR. Publicado por Elsevier España, S.L.U. All rights reserved.)

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease. The severity grading systems proposed by the Global initiative for Chronic Obstructive Lung Disease (GOLD) have changed over time. The aim of the study was to evaluate if the different GOLD classifications can capture the complexity of the disease by investigating the distribution of lung function and clinical parameters across the GOLD classification systems. This was an observational, retrospective, multicentre study. COPD patients were stratified according to the GOLD severity grading proposed in the 2007, and to the ABCD assessment tool present in the 2011, and 2017 versions of the initiative. Data from body plethysmography, DLCO, comorbidities, exacerbation history, pharmacological therapy and eosinophil counts were collected. A total of 1360 patients (73.4% males) were included in the analysis. Overall, 37% of the patients were severe-very severe according to GOLD 2007. Compared with GOLD 2011, applying the GOLD 2017 criteria, the proportion of the at risk categories (C and D) was reduced by ∼23%. Impairment in inspiratory capacity, DLCO and the prevalence of emphysema paralleled the GOLD 2007 classification only. The proportion of patients with ≥ 200 eosinophils/µL was higher in GOLD 2007 stages 3-4 compared with stages 1-2 ( P  = 0.008). Eosinophil levels were similar across risk classes in GOLD 2011 and 2017. Overall, 41.8% and 52.4% of the patients in the low risk groups according to GOLD 2011 and 2017 were exposed to inhaled corticosteroids. The GOLD 2011 and 2017 classifications, despite exploring symptoms and exacerbations, might miss other relevant patients' clinical characteristics such as lung function and phenotypes, which have a significant impact on outcomes and disease severity.

Rationale: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017 is based on an ABCD assessment tool of symptoms and exacerbation history and grade 1-4 of airflow limitation severity, facilitating classification either into 4 groups (ABCD) or 16 groups (1A-4D). We aimed to compare the GOLD 2011, GOLD 2017 ABCD, and GOLD 2017 1A-4D classifications in terms of their distribution and prediction of mortality and hospitalizations., Methods: In the GenKOLS study, 912 COPD patients with FEV1 less than 80% of the predicted answered questionnaires and performed lung function testing in 2003-2005. The patients were recruited from a hospital patient registry (n=662) and from the general population (n=250), followed up until 2011 with respect to all-cause and respiratory mortality, and all-cause and respiratory hospitalizations. We performed logistic regression and receiver operating curve (ROC) analyses for the different classifications with estimations of area under the curve (AUC) for comparisons., Results: Mean age at baseline was 60 years (SD 11), 55% were male. Mean duration of follow-up was 91 months. By GOLD 2011, 21% were classified as group A, 29% group B, 6% group C, and 43% as group D, corresponding percentages for GOLD 2017 were: 25%, 52%, 3%, and 20%. The GOLD 2011 classification had higher AUC values than the GOLD 2017 group ABCD classification for respiratory mortality and hospitalization, but after inclusion of airflow limitation severity in GOLD 2017 groups 2A-4D, AUC values were significantly higher with GOLD 2017., Conclusion: In a clinically relevant sample of COPD patients, the GOLD 2017 classification doubles the prevalence of group B and halves the prevalence of groups C and D as compared to the GOLD 2011 classification. The prediction of respiratory mortality and respiratory hospitalization was better for GOLD 2017 2A-4D taking airflow limitation severity into account, as compared to GOLD 2017 ABCD and GOLD 2011., Competing Interests: Professor Jon Hardie reports personal fees from AstraZeneca, outside the submitted work. Dr Odd Erik Johansen is an employee of Boehringer Ingelheim. The authors report no other conflicts of interest in this work.

Recent research showed group B patients express higher mortality compared to group C patients when GOLD A-D grouping is used. We aimed to compare the prognostic accuracy of three GOLD classification systems, I-IV ("pre-2011"), A-D ("2011-2016") and A-D ("2017-present") in relation to mortality, exacerbation risk, quality of life (QoL) assessment and specific treatments use in a real-life COPD cohort. We used the data of 720 patients from the Czech Multicenter Research Database of COPD. Four-year mortality and time-to-exacerbation using the GOLD "pre-2011", "2011-2016" and "2017-present" classification schemes were assessed. Moreover, distribution of specific treatments use and QoL measures were analyzed. The GOLD I-IV classification system showed gradual increase in 4-year mortality across the stages (GOLD II 18.8%, III 28.5%, IV 38.7%) (p = 0.001). Using the A-D "2011-2016" classification scheme, group C patients had lower mortality (16.7%) than group B (18.7%) (p = 0.009). The A-D "2017-present" classification showed higher mortality in group B (25.5%) compared to group C (20%) (p = 0.05). For additional outcomes, the GOLD I-IV scheme showed highest match between the calculated 4-year exacerbation risk and QoL measures and GOLD stage/grouping. In terms of specific treatment distributions, various patterns for each GOLD classification system were observed with best match of GOLD "2017-present" system to the layout of GOLD groups and categories. We conclude the GOLD I-IV classification system had the highest accuracy related to mortality, QoL measures and exacerbation risk prediction, while the A-D "2017-present" scheme was most accurate within severity of symptoms prediction reflected also by more frequent specific treatments use.