Personalized medicine for patients with COPD: where are we?

The interplay between hyaluronic acid and stem cell secretome boosts pulmonary differentiation in 3D biomimetic microenvironments

Mesenchymal stem cells (MCSs) secretome provide MSC-like therapeutic effects in preclinical models of lung injury, circumventing safety concerns with the use of live cells. Secretome consists of Extracellular Vesicles (EVs), including populations of nano- to micro-sized particles (exosomes and microvesicles) delimited by a phospholipidic bilayer. However, its poor stability and bioavailability severely limit its application. The role of Hyaluronic acid (HA) as potential carrier in biomedical applications has been widely demonstrated. Here, we investigated the interplay between HA and MSCs- secretome blends and their ability to exert a bioactive effect on pulmonary differentiation in a 3D microenvironment mimicking lung niche. To this aim, the physical-chemical properties of HA/Secre blends have been characterized at low, medium and high HA Molecular Weights (MWs), by means of SEM/TEM, DLS, confocal microscopy and FTIR. Collectively physical-chemical properties highlight the interplay between the HA and the EVs. In 3D matrices, HA/Secre blends showed to promote differentiation in pulmonary lineage, improved as the MW of the HA in the blends decreased. Finally, HA/Secre blends' ability to cross an artificial mucus has been demonstrated. Overall, this work provides new insights for the development of future devices for the therapy of respiratory diseases that are still unmet.

Artificial intelligence in COPD CT images: identification, staging, and quantitation

Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.

Advances in COPD imaging using CT and MRI: linkage with lung physiology and clinical outcomes

Recent years have witnessed major advances in lung imaging in patients with COPD. These include significant refinements in images obtained by computed tomography (CT) scans together with the introduction of new techniques and software that aim for obtaining the best image whilst using the lowest possible radiation dose. Magnetic resonance imaging (MRI) has also emerged as a useful radiation-free tool in assessing structural and more importantly functional derangements in patients with well-established COPD and smokers without COPD, even before the existence of overt changes in resting physiological lung function tests. Together, CT and MRI now allow objective quantification and assessment of structural changes within the airways, lung parenchyma and pulmonary vessels. Furthermore, CT and MRI can now provide objective assessments of regional lung ventilation and perfusion, and multinuclear MRI provides further insight into gas exchange; this can help in structured decisions regarding treatment plans. These advances in chest imaging techniques have brought new insights into our understanding of disease pathophysiology and characterising different disease phenotypes. The present review discusses, in detail, the advances in lung imaging in patients with COPD and how structural and functional imaging are linked with common resting physiological tests and important clinical outcomes.

Association of Fetal Lung Development Disorders with Adult Diseases: A Comprehensive Review

Fetal lung development is a crucial and complex process that lays the groundwork for postnatal respiratory health. However, disruptions in this delicate developmental journey can lead to fetal lung development disorders, impacting neonatal outcomes and potentially influencing health outcomes well into adulthood. Recent research has shed light on the intriguing association between fetal lung development disorders and the development of adult diseases. Understanding these links can provide valuable insights into the developmental origins of health and disease, paving the way for targeted preventive measures and clinical interventions. This review article aims to comprehensively explore the association of fetal lung development disorders with adult diseases. We delve into the stages of fetal lung development, examining key factors influencing fetal lung maturation. Subsequently, we investigate specific fetal lung development disorders, such as respiratory distress syndrome (RDS), bronchopulmonary dysplasia (BPD), congenital diaphragmatic hernia (CDH), and other abnormalities. Furthermore, we explore the potential mechanisms underlying these associations, considering the role of epigenetic modifications, transgenerational effects, and intrauterine environmental factors. Additionally, we examine the epidemiological evidence and clinical findings linking fetal lung development disorders to adult respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), and other respiratory ailments. This review provides valuable insights for healthcare professionals and researchers, guiding future investigations and shaping strategies for preventive interventions and long-term care.

Predicting parameters of airway dynamics generated from inspiratory and expiratory plethysmographic airway loops, differentiating subtypes of chronic obstructive diseases

BACKGROUND

The plethysmographic shift volume-flow loop (sRaw-loop) measured during tidal breathing allows the determination of several lung function parameters such as the effective specific airway resistance (sReff), calculated from the ratio of the integral of the resistive aerodynamic specific work of breathing (sWOB) and the integral of the corresponding flow-volume loop. However, computing the inspiratory and expiratory areas of the sRaw-loop separately permits the determination of further parameters of airway dynamics. Therefore, we aimed to define the discriminating diagnostic power of the inspiratory and expiratory sWOB (sWOBin, sWOBex), as well as of the inspiratory and expiratory sReff (sReff IN and sReff EX), for discriminating different functional phenotypes of chronic obstructive lung diseases.

METHODS

Reference equations were obtained from measurement of different databases, incorporating 194 healthy subjects (35 children and 159 adults), and applied to a collective of 294 patients with chronic lung diseases (16 children with asthma, aged 6-16 years, and 278 adults, aged 17-92 years). For all measurements, the same type of plethysmograph was used (Jaeger Würzburg, Germany).

RESULTS

By multilinear modelling, reference equations of sWOBin, sWOBex, sReff IN and sReff EX were derived. Apart from anthropometric indices, additional parameters such as tidal volume (VT), the respiratory drive (P0.1), measured by means of a mouth occlusion pressure measurement 100 ms after inspiration and the mean inspiratory flow (VT/TI) were found to be informative. The statistical approach to define reference equations for parameters of airway dynamics reveals the interrelationship between covariants of the actual breathing pattern and the control of breathing.

CONCLUSIONS

We discovered that sWOBin, sWOBex, sReff IN and sReff EX are new discriminating target parameters, that differentiate much better between chronic obstructive diseases and their subtypes, especially between chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap (ACO), thus strengthening the concept of precision medicine.

Pulmonary Diseases in Older Patients: Understanding and Addressing the Challenges

As the global population ages, pulmonary diseases among older people have emerged as a significant and growing public health concern. The increasing incidence of these conditions has led to higher rates of morbidity and mortality among older adults. This perspective study offers a thorough overview of the prevalent pulmonary diseases affecting the elderly demographic. It delves into the challenges encountered during the diagnosis and management of these conditions in older individuals, considering factors such as comorbidities, functional limitations, and medication complexities. Furthermore, innovative strategies and personalized interventions such as precision medicine, advanced therapies, telemedicine solutions, and patient-centered support systems aimed at enhancing the care provided to older individuals grappling with pulmonary disorders are thoroughly explored. By addressing the unique needs and complexities of this vulnerable population, healthcare systems can strive towards improving outcomes and enhancing the quality of life for elderly individuals affected by pulmonary diseases.

Assessing the Effectiveness of Rehabilitation Interventions through the World Health Organization Disability Assessment Schedule 2.0 on Disability-A Systematic Review

(1) Background: The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a tool designed to measure disability in accordance with the International Classification of Functioning, Disability and Health. Measuring disability is becoming increasingly important due to its high prevalence, which continues to rise. Rehabilitation interventions can reduce disability and enhance functioning. (2) Objective: The present study aims to assess the impact of rehabilitation interventions on reducing disability, as measured by the WHODAS 2.0 questionnaire. It also seeks to identify which specific rehabilitation interventions are more effective and to explore other disability assessment questionnaires. (3) Methods: Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, we conducted a systematic review, with the protocol registered with the identifier CRD42023495309, focused on "WHODAS" and "rehabilitation" using PubMed and Web of Science electronic databases. (4) Results: We identified 18 articles from various regions encompassing patients with various health conditions, related to stroke, the cardiovascular system (cardiovascular disease, chronic heart failure), the pulmonary system (chronic obstructive pulmonary disease), the neurologic system (Parkinson's disease, cerebral palsy, neurodegenerative disease), the musculoskeletal system (orthopaedic surgery), cancer, and chronic pain, and among frail elderly. These patients have received a wide range of rehabilitation interventions: from conventional therapy to virtual reality, robot-assisted arm training, exergaming, and telerehabilitation. (5) Discussion and Conclusions: A wide range of rehabilitation techniques can effectively improve disability with various comorbidities, offering numerous benefits. The WHODAS 2.0 questionnaire proves to be an efficient and reliable tool for measuring disability, and scores have a tendency to decrease after rehabilitation.

Assessment of functional diversities in patients with Asthma, COPD, Asthma-COPD overlap, and Cystic Fibrosis (CF)

The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV1; FEV1/forced vital capacity: FEV1/FVC; forced expiratory flow between 25-75% FVC: FEF25-75), and plethysmography (effective, resistive airway resistance: sReff; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sReffIN, sReffEX, sWOBin, sWOBex) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRCpleth; residual volume: RV), the control of breathing (mouth occlusion pressure: P0.1; mean inspiratory flow: VT/TI; the inspiratory to total time ratio: TI/Ttot) and the inspiratory impedance (Zinpleth = P0.1/VT/TI) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P0.1, sRtot, and VT/TI differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOBex and Zinpleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Zinpleth may well serve as promising functional marker in the field of precision medicine.

The genomic landscape of chronic obstructive pulmonary disease: Insights from nutrigenomics

Chronic obstructivе pulmonary disеasе (COPD), a rеspiratory disеasе, is influenced by a combination of gеnеtic and еnvironmеntal factors. Thе fiеld of nutrigеnomics, which studiеs thе intеrplay bеtwееn diеt and gеnеs, provides valuable insights into thе gеnomic landscapе of COPD and its implications for production and managеmеnt. This rеviеw providеs a comprеhеnsivе ovеrviеw of thе gеnеtic aspеcts of COPD and thе rolе of nutrigеnomics in advancing our undеrstanding of thе undеrlying mеchanisms. Through studies of gеnomе-widе associations, researchers have identified gеnеtic factors that contribute to suscеptibility to COPD. Thеsе gеnеs arе associatеd with oxidativе strеss, inflammation, and antioxidant dеfеnsе mеchanisms. Nutrigеnomics rеsеarch is currеntly invеstigating how diеtary componеnts interact with gеnеtic variations to modulatе thе dеvеlopmеnt of COPD. Antioxidants, omеga-3 fatty acids and vitamin D havе dеmonstratеd potеntial bеnеfits in rеducing inflammation, improving lung function, and minimizing еxacеrbations in patients with COPD. Therefore, there are sеvеral challеngеs that must be added to the nutrigеnomic rеsеarch. The challenges include thе nееd for largеr clinical trials, adding hеtеrogеnеity and validating biomarkеrs. In the tеrms of futurе dirеctions, prеcision nutrition, gеnе-basеd thеrapiеs, biomarkеr dеvеlopmеnt, intеgration of multi-omics data, systеms biology analysis, longitudinal studiеs, and public hеalth implications arе important arеas to еxplorе. Pеrsonalizеd nutritional intеrvеntions based on an individual's gеnеtic profilе hold grеat promisе for optimizing COPD managеmеnt. In conclusion, nutrigеnomics provides valuable insights into the gеnomic landscapе of COPD and its intеraction with the disease. This knowlеdgе can guidе thе dеvеlopmеnt of pеrsonalizеd diеtary stratеgiеs and gеnе-basеd thеrapiеs for thе prеvеntion and managеmеnt of COPD. Howеvеr, morе rеsеarch is nееdеd to validatе thеsе findings, dеvеlop еffеctivе intеrvеntions, and implеmеnt thеm еffеctivеly in clinical practicе to improvе thе quality of lifе for pеoplе with COPD.

Systems Engineering Approach to Modeling and Analysis of Chronic Obstructive Pulmonary Disease Part II: Extension for Variable Metabolic Rates

Recently, we developed a systems engineering model of the human cardiorespiratory system [Kurian et al. ACS Omega2023, 8 (23), 20524-20535. DOI: 10.1021/acsomega.3c00854] based on existing models of physiological processes and adapted it for chronic obstructive pulmonary disease (COPD)-an inflammatory lung disease with multiple manifestations and one of the leading causes of death in the world. This control engineering-based model is extended here to allow for variable metabolic rates established at different levels of physical activity. This required several changes to the original model: the model of the controller was enhanced to include the feedforward loop that is responsible for cardiorespiratory control under varying metabolic rates (activity level, characterized as metabolic equivalent of the task-Rm-and normalized to one at rest). In addition, a few refinements were made to the cardiorespiratory mechanics, primarily to introduce physiological processes that were not modeled earlier but became important at high metabolic rates. The extended model is verified by analyzing the impact of exercise (Rm > 1) on the cardiorespiratory system of healthy individuals. We further formally justify our previously proposed adaptation of the model for COPD patients through sensitivity analysis and refine the parameter tuning through the use of a parallel tempering stochastic global optimization method. The extended model successfully replicates experimentally observed abnormalities in COPD-the drop in arterial oxygen tension and dynamic hyperinflation under high metabolic rates-without being explicitly trained on any related data. It also supports the prospects of remote patient monitoring in COPD.

Applications of deep learning models in precision prediction of survival rates for heart failure patients

BACKGROUND

Heart failure poses a significant challenge in the global health domain, and accurate prediction of mortality is crucial for devising effective treatment plans. In this study, we employed a Seq2Seq model from deep learning, integrating 12 patient features. By finely modeling continuous medical records, we successfully enhanced the accuracy of mortality prediction.

OBJECTIVE

The objective of this research was to leverage the Seq2Seq model in conjunction with patient features for precise mortality prediction in heart failure cases, surpassing the performance of traditional machine learning methods.

METHODS

The study utilized a Seq2Seq model in deep learning, incorporating 12 patient features, to intricately model continuous medical records. The experimental design aimed to compare the performance of Seq2Seq with traditional machine learning methods in predicting mortality rates.

RESULTS

The experimental results demonstrated that the Seq2Seq model outperformed conventional machine learning methods in terms of predictive accuracy. Feature importance analysis provided critical patient risk factors, offering robust support for formulating personalized treatment plans.

CONCLUSIONS

This research sheds light on the significant applications of deep learning, specifically the Seq2Seq model, in enhancing the precision of mortality prediction in heart failure cases. The findings present a valuable direction for the application of deep learning in the medical field and provide crucial insights for future research and clinical practices.

The Influence of Social Support in PROMs of Patients with COPD in Primary Care: A Scoping Review

Chronic obstructive pulmonary disease (COPD) is a prevalent and multidimensional disease with symptoms that greatly influence patients' health. Healthcare professionals utilize patient-reported outcome measures (PROMs) to classify and better manage the disease. Despite the value of PROMs, they inadequately represent some important dimensions of COPD, like social support and healthcare access/utilization. This is important, especially for social support, since it can positively influence PROMs results and the overall health of patients with COPD. Therefore, a scoping review was conducted to determine how social support affects PROMs of patients with COPD in primary care. The PRISMA-Scoping approach was adopted, and we sought articles published in MEDLINE and COHRANE. We screened 2038 articles for inclusion and finally included a total of 10 articles. Most of the articles were conducted in the U.S. and Norway. Social support had a strong positive impact on PROMs. Additionally, different types of social support were observed. Moreover, higher levels of social support were linked to better quality of life, mental health, self-care behaviors, self-management, functionality, and less severe COPD. Consequently, this scoping review highlights the value of social support in patients with COPD and its underrepresentation and misrepresentation in PROMs literature.

Overuse of long-acting β2-agonist/inhaled corticosteroids in patients with chronic obstructive pulmonary disease: time to rethink prescribing patterns

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality globally. In the major revision of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2023 report, the scientific committee concluded that the use of long-acting β2-agonist/inhaled corticosteroids (LABA/ICS) is not encouraged in patients with COPD. However, current prescribing patterns reveal significant use of LABA/ICS. In this paper, the evidence behind the current practice and the latest treatment recommendations is reviewed. We compare the efficacy and safety of combination therapy with long-acting muscarinic antagonist (LAMA) and LABA vs LABA/ICS and note that LAMA/LABA combinations have reduced the annual rate of moderate/severe exacerbations, delayed the time to first exacerbation, and increased post-dose FEV1 vs ICS-based regimens. The GOLD 2023 report recommends treatment with LABA and LAMA combination (preferably as a single inhaler) in patients with persistent dyspnea, with initiation of ICS in patients based on the symptoms (dyspnea and exercise intolerance as indicated by modified Medical Research Council [mMRC] score ≥ 2 and COPD Assessment Test [CAT™] > 20), blood eosinophil count (≥ 300 cells/µL), and exacerbation history (history of hospitalizations for exacerbations of COPD and ≥ 2 moderate exacerbations per year despite appropriate long-acting bronchodilator maintenance therapy). We describe practical recommendations for primary care physicians to optimize therapy for their patients and prevent overuse of ICS-based regimens. We advocate adherence to current recommendations and a greater focus on effective treatments to successfully control symptoms, minimize exacerbation risk, preserve lung function, maximize patient outcomes, and reduce the burden of drug-related adverse events.

Perspectives and Attitudes of General Practitioners Towards Pharmacological and Non-Pharmacological COPD Management in a Belgian Primary Care Setting: A Qualitative Study

Background

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous condition that requires multidisciplinary management. In Belgium, the treatment of COPD is mainly managed by general practitioners (GPs). Several clinical practice guidelines (CPGs) recommend the use of non-pharmacological treatments, such as pulmonary rehabilitation, and interdisciplinary care for COPD patients. Although considerable research has been devoted to addressing the multitude of reasons for the lack of adherence to these aspects of the CPGs, less attention has been paid to understanding the perspectives and attitudes of GPs that lead to this suboptimal implementation.

Purpose

This study aimed to 1) explore Belgian GPs' perceptions regarding COPD management in a primary care setting and 2) collect their views on the importance of pulmonary rehabilitation and interprofessional care in COPD management.

Methods

A descriptive study, conducted between August 2014 and May 2015, used interviews from a sample of 30 Flemish GPs. Data were analyzed following the principles of thematic analysis.

Results

COPD management was patient-centered, focusing on immediate symptom relief and reducing future risks through pharmacotherapy and proper patient education. Deviations from the CPGs were noted, with only a few GPs performing spirometry themselves. Conditions to prescribe respiratory physiotherapy were not well known. Some GPs remained unconvinced about the (cost-)effectiveness of respiratory physiotherapy despite the fast-expanding scientific evidence. Interprofessional care was limited to GP-respiratory physician (re)-referral and communication. GPs showed a reactive attitude towards interprofessional collaboration for non-pharmacological therapies, which is not in line with the proactive approach recommended in CPGs.

Conclusion

GPs managed COPD patients mainly by reducing symptoms with pharmacological therapy. Integrated care regarding non-pharmacological treatments was not well implemented due to the obstacles in interprofessional collaboration. Future care models incorporating personalized care planning could provide a solution to manage COPD's complex healthcare demands.

Animal models and mechanisms of tobacco smoke-induced chronic obstructive pulmonary disease (COPD)

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, and its global health burden is increasing. COPD is characterized by emphysema, mucus hypersecretion, and persistent lung inflammation, and clinically by chronic airflow obstruction and symptoms of dyspnea, cough, and fatigue in patients. A cluster of pathologies including chronic bronchitis, emphysema, asthma, and cardiovascular disease in the form of hypertension and atherosclerosis variably coexist in COPD patients. Underlying causes for COPD include primarily tobacco use but may also be driven by exposure to air pollutants, biomass burning, and workplace related fumes and chemicals. While no single animal model might mimic all features of human COPD, a wide variety of published models have collectively helped to improve our understanding of disease processes involved in the genesis and persistence of COPD. In this review, the pathogenesis and associated risk factors of COPD are examined in different mammalian models of the disease. Each animal model included in this review is exclusively created by tobacco smoke (TS) exposure. As animal models continue to aid in defining the pathobiological mechanisms of and possible novel therapeutic interventions for COPD, the advantages and disadvantages of each animal model are discussed.

Systems Engineering Approach to Modeling and Analysis of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by airflow limitation. This study develops a systems engineering framework for representing important mechanistic details of COPD in a model of the cardiorespiratory system. In this model, we present the cardiorespiratory system as an integrated biological control system responsible for regulating breathing. Four engineering control system components are considered: sensor, controller, actuator, and the process itself. Knowledge of human anatomy and physiology is used to develop appropriate mechanistic mathematical models for each component. Following a systematic analysis of the computational model, we identify three physiological parameters associated with reproducing clinical manifestations of COPD: changes in the forced expiratory volume, lung volumes, and pulmonary hypertension. We quantify the changes in these parameters (airway resistance, lung elastance, and pulmonary resistance) as the ones that result in a systemic response that is diagnostic of COPD. A multivariate analysis of the simulation results reveals that the changes in airway resistance have a broad impact on the human cardiorespiratory system and that the pulmonary circuit is stressed beyond normal under hypoxic environments in most COPD patients.

Cigarette smoke-induced pulmonary impairment is associated with social recognition memory impairments and alterations in microglial profiles within the suprachiasmatic nucleus of the hypothalamus

Chronic obstructive pulmonary disease (COPD) is a major, incurable respiratory condition that is primarily caused by cigarette smoking (CS). Neurocognitive disorders including cognitive dysfunction, anxiety and depression are highly prevalent in people with COPD. It is understood that increased lung inflammation and oxidative stress from CS exposure may 'spill over' into the systemic circulation to promote the onset of these extra-pulmonary comorbidities, and thus impacts the quality of life of people with COPD. The precise role of the 'spill-over' of inflammation and oxidative stress in the onset of COPD-related neurocognitive disorders are unclear. The present study investigated the impact of chronic CS exposure on anxiety-like behaviors and social recognition memory, with a particular focus on the role of the 'spill-over' of inflammation and oxidative stress from the lungs. Adult male BALB/c mice were exposed to either room air (sham) or CS (9 cigarettes per day, 5 days a week) for 24 weeks and were either daily co-administered with the NOX2 inhibitor, apocynin (5 mg/kg, in 0.01 % DMSO diluted in saline, i.p.) or vehicle (0.01 % DMSO in saline) one hour before the initial CS exposure of the day. After 23 weeks, mice underwent behavioral testing and physiological diurnal rhythms were assessed by monitoring diurnal regulation profiles. Lungs were collected and assessed for hallmark features of COPD. Consistent with its anti-inflammatory and oxidative stress properties, apocynin treatment partially lessened lung inflammation and lung function decline in CS mice. CS-exposed mice displayed marked anxiety-like behavior and impairments in social recognition memory compared to sham mice, which was prevented by apocynin treatment. Apocynin was unable to restore the decreased Bmal1-positive cells, key in cells in diurnal regulation, in the suprachiasmatic nucleus of the hypothalamus to that of sham levels. CS-exposed mice treated with apocynin was associated with a restoration of microglial area per cell and basal serum corticosterone. This data suggests that we were able to model the CS-induced social recognition memory impairments seen in humans with COPD. The preventative effects of apocynin on memory impairments may be via a microglial dependent mechanism.

Role of digital health in pulmonary rehabilitation and beyond: shaping the future

Pulmonary rehabilitation (PR) is a cost-effective intervention with well-known benefits to exercise capacity, symptoms and quality of life in patients with chronic respiratory diseases. Despite the compelling evidence of its benefits, PR implementation is still suboptimal, and maintenance of PR benefits is challenging. To overcome these pitfalls, there has been a growing interest in developing novel models for PR delivery. Digital health is a promising solution, as it has the potential to address some of the most reported barriers to PR uptake and adherence (such as accessibility issues), help maintain the positive results following a PR programme and promote patients' adherence to a more active lifestyle through physical activity (tele)coaching. Despite the accelerated use of digital health to deliver PR during the coronavirus disease 2019 pandemic, there are still several factors that contribute to the resistance to the adoption of digital health, such as the lack of evidence on its effectiveness, low acceptability by patients and healthcare professionals, concerns about implementation and maintenance costs, inequalities in access to the internet and technological devices, and data protection issues. Nevertheless, the trend towards reducing technology costs and the higher availability of digital devices, as well as the greater ease and simplicity of use of devices, enhance the opportunities for future development of digitally enabled PR interventions. This narrative review aims to examine the current evidence on the role of digital health in the context of PR, including strengths and weaknesses, and to determine possible threats and opportunities, as well as areas for future work.

Network Pharmacology and Experimental Validation to Reveal Effects and Mechanisms of Icariin Combined with Nobiletin against Chronic Obstructive Pulmonary Diseases

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a long-term respiratory disorder marked by restricted airflow and persistent respiratory symptoms. According to previous studies, icariin combined with nobiletin (I&N) significantly ameliorates COPD, but the therapeutic mechanisms remain unclear.

PURPOSE

The aim of the study is to investigate the therapeutic mechanisms of I&N against COPD using network pharmacology and experimental validation.

METHODS

The targets of I&N and related genes of COPD were screened and their intersection was selected. Next, the protein-protein interaction (PPI) networks, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Further, a COPD rat model was established to validate the effect and mechanisms of I&N.

RESULTS

445 potential targets I&N were obtained from SwissTargetPrediction, STITCH 5.0, and PharmMapper databases. 1831 related genes of COPD were obtained from GeneCards, DrugBank, and DisGeNet databases. 189 related genes were screened via matching COPD targets with I&N. 16 highest score targets among 189 targets were obtained according to PPI networks. GO and KEGG pathway enrichment analyses of 16 highest score targets suggested that these key genes of I&N were mostly enriched in the tumor necrosis factor (TNF) pathway, mitogen-activated protein kinase (MAPK) pathway, and phosphatidyl inositol 3-kinase (PI3K)-protein kinase B (AKT) pathway. Therefore, the treatments of I&N for COPD were connected with inflammation-related pathways. In in vivo experiments, the studies indicated that I&N improved the lung function and alleviated the damage of pulmonary histopathology. Moreover, I&N reduced levels of interleukin (IL)-6, IL-1β, and TNF-α in lung tissues of COPD rats and inhibited the activation of the MAPK pathway and PI3K-Akt pathway.

CONCLUSIONS

Icariin combined with nobiletin has therapeutic effects on COPD by inhibiting inflammation. The potential mechanisms of I&N may relate to the MAPK pathway and PI3K-Akt pathway.

Quantitative evaluation of diaphragmatic motion during forced breathing in chronic obstructive pulmonary disease patients using dynamic chest radiography

Objective

To quantitatively evaluate the bilateral diaphragmatic motion difference during forced breathing between chronic obstructive pulmonary disease (COPD) patients and healthy individuals using dynamic chest radiography technique.

Methods

This prospective study included the COPD patients (n: 96, f/m: 17/79, age: 66 ± 8 years old) and healthy individuals (n: 50, f/m: 42/8, age: 53 ± 5 years old) that underwent dynamic chest radiography with a flat panel X-ray detector system during forced breathing in a standing position. After analyzing the excursions, duration and velocity of diaphragmatic motion were automatically calculated using the postprocessing software. The parameters of diaphragmatic motion including excursion, duration, velocity, inhalation/exhalation times were assessed in all subjects for both diaphragms. The correlation between lung function parameters and diaphragmatic motion excursions were further evaluated.

Results

The excursions of diaphragmatic motion in COPD patients were significantly decreased in COPD patients compared with healthy individuals during forced breathing (P < 0.05). The excursion in COPD patients was 35.93 ± 13.07 mm vs. 41.49 ± 12.07 mm in healthy individuals in the left diaphragm, and 32.05 ± 12.29 mm in COPD patients vs. 36.88 ± 10.96 mm in healthy individuals in the right diaphragm. The duration of diaphragmatic motion significantly decreased in COPD patients, compared with the healthy individuals (P < 0.05). The inhalation time in COPD patients was 2.03 ± 1.19 s vs. 2.53 ± 0.83 s in healthy individuals in the left diaphragm and 1.94 ± 1.32 s in COPD patients vs. 2.23 ± 1.21 s in healthy individuals in the right diaphragm. The exhalation time was 4.77 ± 1.32 s in COPD patients vs. 6.40 ± 2.73 s in healthy individuals in the left diaphragm and 4.94 ± 3.30 s in COPD patients vs. 6.72 ± 2.58 s in healthy individuals in the right diaphragm. The peak velocity of diaphragmatic motion showed no significant difference between COPD and healthy groups. The excursions of bilateral diaphragmatic motion showed moderate correlation with FEV1/FVC (r = 0.44, P < 0.001). Multi-linear regression analysis showed that the excursions of bilateral diaphragm are significantly associated with COPD occurrence (P < 0.05).

Conclusion

The excursions and duration of diaphragmatic motion during forced breathing are significantly decreased in COPD patients, compared with healthy individuals. Our study showed that precise bilateral diaphragmatic motion activity can be evaluated by dynamic chest radiography.

A Study of Various Predictors of COPD-Related Fatigue (COPD-RF) and its Correlation with other Established Outcome Parameters in COPD

Background

Over the years approach to COPD has changed. The focus of evaluation is shifting from physiological outcome parameters to patient centered outcomes. COPD-RF is one such patient related outcome that is of considerable importance to the patients as fatigue is the second common distressing symptom in COPD & is a major concern for the patients. COPD-RF is not only a reflection of airflow limitation severity but also a result of psychological, behavioral, and physical factors. Therefore, identification of markers which determine COPD RF is essential to improve patient management. This study aims to identify the predictors of COPD Related fatigue (COPD-RF) and its correlation with other outcome tools like FEV1, 6-minute walk distance (6MWD), MMRC grade, BODE index & inflammatory markers.

Methodology

50 stable COPD patients were taken in the study. MMRC grade, FEV1, 6MWD, BODE index, CRP level and sputum neutrophil % obtained. COPD-RF was assessed using 13 item (FACIT-fatigue) questionnaire. The correlation analysis was done by spearman rank correlation and ANOVA. Predictors of COPD-RF were identified by multiple linear regression.

Result

Mean age of the population was 56.53 ± 9.29. All GOLD category of severity was equally represented in the study group. The fatigue score showed significant correlation with inflammatory markers (CRP-r=-0.675; neutrophil%- r=-0.485) & 6MWD (r=-.428). Categorical variables (MMRC, FEV1 GOLD stages & BODE quartiles) also showed significant difference of fatigue among categories. BODE index & serum CRP were identified as the statistically significant predictors of fatigue, suggesting COPD-RF could reflect severity of underlying pathophysiologic process i.e. systemic inflammation.

Conclusion

BODE index and CRP levels are the two important surrogate markers that predicts COPR-RF implying a role of systemic inflammation in the pathogenesis of fatigue. Also, COPD-RF could be an indicator of long-term prognosis of the disease & should be routinely evaluated during COPD assessment.

Patient-Centered Discussion on End-of-Life Care for Patients with Advanced COPD

Exacerbations of chronic obstructive pulmonary disease (COPD) may lead to a rapid decline in health and subsequent death, an unfortunate tyranny of having COPD-an irreversible health condition of 16 million individuals in the USA totaling 60 million in the world. While COPD is the third largest leading cause of death, causing 3.23 million deaths worldwide in 2019 (according to the WHO), most patients with COPD do not receive adequate treatment at the end stages of life. Although death is inevitable, the trajectory towards end-of-life is less predictable in severe COPD. Thus, clinician-patient discussion for end-of-life and palliative care could bring a meaningful life-prospective to patients with advanced COPD. Here, we summarized the current understanding and treatment of COPD. This review also highlights the importance of patient-centered discussion and summarizes current status of managing patients with advanced COPD.

Metabolomics of COPD Pulmonary Rehabilitation Outcomes via Exhaled Breath Condensate

Chronic obstructive pulmonary disease (COPD) is characterized by different phenotypes and clinical presentations. Therefore, a single strategy of pulmonary rehabilitation (PR) does not always yield the expected clinical outcomes as some individuals respond excellently, others discreetly, or do not respond at all. Fifty consecutive COPD patients were enrolled. Of them, 35 starting a 5-week PR program were sampled at admission (T₀), after 2 (T_2W) and 5 (T_5W) weeks, while 15 controls not yet on PR were tested at T₀ and T_5W. Nuclear magnetic resonance (NMR) profiling of exhaled breath condensate (EBC) and multivariate statistical analysis were applied to investigate the relationship between biomarkers and clinical parameters. The model including the three classes correctly located T_2W between T₀ and T_5W, but 38.71% of samples partially overlapped with T₀ and 32.26% with T_5W, suggesting that for some patients PR is already beneficial at T_2W (32.26% overlapping with T_5W), while for others (38.71% overlapping with T₀) more time is required. Rehabilitated patients presented several altered biomarkers. In particular, methanol from T₀ to T_5W decreased in parallel with dyspnea and fatigue, while the walk distance increased. Methanol could be ascribed to lung inflammation. We demonstrated that the metabolic COPD phenotype clearly evolves during PR, with a strict relationship between clinical and molecular parameters. Methanol, correlating with clinical parameters, represents a useful biomarker for monitoring personalized outcomes and establishing more targeted protocols.

Early Diagnosis and Real-Time Monitoring of Regional Lung Function Changes to Prevent Chronic Obstructive Pulmonary Disease Progression to Severe Emphysema

First- and second-hand exposure to smoke or air pollutants is the primary cause of chronic obstructive pulmonary disease (COPD) pathogenesis, where genetic and age-related factors predispose the subject to the initiation and progression of obstructive lung disease. Briefly, airway inflammation, specifically bronchitis, initiates the lung disease, leading to difficulty in breathing (dyspnea) and coughing as initial symptoms, followed by air trapping and inhibition of the flow of air into the lungs due to damage to the alveoli (emphysema). In addition, mucus obstruction and impaired lung clearance mechanisms lead to recurring acute exacerbations causing progressive decline in lung function, eventually requiring lung transplant and other lifesaving interventions to prevent mortality. It is noteworthy that COPD is much more common in the population than currently diagnosed, as only 16 million adult Americans were reported to be diagnosed with COPD as of 2018, although an additional 14 million American adults were estimated to be suffering from COPD but undiagnosed by the current standard of care (SOC) diagnostic, namely the spirometry-based pulmonary function test (PFT). Thus, the main issue driving the adverse disease outcome and significant mortality for COPD is lack of timely diagnosis in the early stages of the disease. The current treatment regime for COPD emphysema is most effective when implemented early, on COPD onset, where alleviating symptoms and exacerbations with timely intervention(s) can prevent steep lung function decline(s) and disease progression to severe emphysema. Therefore, the key to efficiently combatting COPD relies on early detection. Thus, it is important to detect early regional pulmonary function and structural changes to monitor modest disease progression for implementing timely interventions and effectively eliminating emphysema progression. Currently, COPD diagnosis involves using techniques such as COPD screening questionnaires, PFT, arterial blood gas analysis, and/or lung imaging, but these modalities are limited in their capability for early diagnosis and real-time disease monitoring of regional lung function changes. Hence, promising emerging techniques, such as X-ray phase contrast, photoacoustic tomography, ultrasound computed tomography, electrical impedance tomography, the forced oscillation technique, and the impulse oscillometry system powered by robust artificial intelligence and machine learning analysis capability are emerging as novel solutions for early detection and real time monitoring of COPD progression for timely intervention. We discuss here the scope, risks, and limitations of current SOC and emerging COPD diagnostics, with perspective on novel diagnostics providing real time regional lung function monitoring, and predicting exacerbation and/or disease onset for prognosis-based timely intervention(s) to limit COPD–emphysema progression.

Are in Person and Telephone Interviews Equivalent Modes of Administrating the CAT, the FACIT-FS and the SGRQ in People With COPD?

Background: The COVID-19 pandemic brought numerous challenges, namely in routine assessment of people with chronic obstructive pulmonary disease (COPD). The COPD Assessment Test (CAT), the Functional Assessment of Chronic Illness-Fatigue-Subscale (FACIT-FS) and the St. George's respiratory questionnaire (SGRQ) are important patient-reported outcome measures used to assess people with COPD, but its face-to-face application has been compromised. The telephone interview offers a simple and effective alternative, yet uncertainty regarding its equivalence remains. This study aimed to establish the reliability and validity of the CAT, the FACIT-FS and the SGRQ administered by telephone interview in people with COPD.

Methods: Data from an observational prospective study including people with COPD were analyzed. Participants answered to the CAT, FACIT-FS and SGRQ questionnaires in person and by telephone, with a maximum interval of 48-h. Participants were randomly selected to answer first to the in-person questionnaire followed by telephone or vice versa. Reliability measures included internal consistency with Cronbach's alpha, test-retest reliability with the intraclass correlation coefficient (ICC_2,1), test-retest measurement error with the standard error of measurement (SEM) and agreement with the Bland and Altman 95% limits of agreement. Validity was assessed with the Spearman correlation (rho).

Results: Fifty-five people with COPD (44 men; 68.1 ± 7.9 years; FEV₁: 59.1 ± 20.3% predicted) were included. Similar internal consistency was observed between in person vs. telephone interview for the CAT (0.82 vs. 0.84), the FACIT-FS (0.83 vs. 0.84) and the SGRQ (0.92 vs. 0.93). Test-retest reliability was excellent, with an ICC_2,1 of 0.77 (95% CI: 0.65; 0.86), 0.86 (95% CI: 0.77; 0.92) and 0.94 (95% CI: 0.90; 0.96) for the CAT, FACIT-FS and SGRQ total scores, respectively. The SEM showed a low level of associated measurement error and the Bland and Altman plots illustrated a good level of agreement between both modes of administration, with no evidence of systematic bias. Robust positive correlations (rho 0.87–0.94, p < 0.001) were found for the CAT, FACIT-FS and SGRQ total scores applied by both methods.

Conclusion: The telephonic administration of the CAT, the FACIT-FS and the SGRQ are a valid and reliable alternative approach to in person interviews for monitoring symptoms and health-related quality of life in people with COPD. The telephone might be an important add-on for personalized assessment and management of COPD thru remote monitoring.

A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective-Results of a Qualitative Study

Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.

Effects of Doxofylline Combined with Ceftazidime on Clinical Efficacy, Drug Safety, and Prognosis in Patients with Chronic Obstructive Pulmonary Disease Complicated with Infection

Background

The aim of this study was to investigate the effects of doxofylline combined with ceftazidime on clinical efficacy, drug safety, and prognosis in patients with COPD complicated with infection.

Material/Methods

A total of 450 patients admitted to the Inner Mongolia BaoGang Hospital for treatment of COPD from January 2017 to December 2019 were selected to participate. All patients were randomly divided into control and observation groups, with 225 patients in each group. In addition, patients with COPD in the remission stage were matched by sex and age for a blank control group. The control group was treated with doxofylline, and the observation group was treated with ceftazidime and doxofylline. No drug intervention was given to the blank control group. Short-term efficacy, pulmonary ventilation function, patient quality of life, peripheral blood TNF-α and PGDF-B levels, and adverse drug reactions were observed.

Results

The effective treatment rate in the observation group was 96.89%, which was significantly higher than that in the control group (84.00%). Measures of pulmonary ventilation function and patient quality of life in the observation group were significantly higher than those in the control group. Levels of TNF-α and PDGF-B in the observation group were significantly lower than those in the control group. There were no significant differences in the above indicators between the blank control group and the observation group.

Conclusions

Doxofylline combined with ceftazidime effectively treated patients with COPD complicated with infection. These results provide a reference for clinical treatment.

Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?

Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert’s pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual’s clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.

Renewed vision on pulmonary rehabilitation service delivery for chronic obstructive pulmonary disease management beyond COVID-19

Pulmonary rehabilitation (PR) is a cornerstone management for chronic obstructive pulmonary disease (COPD). International respiratory societies defined PR is more than "just an exercise program"; it is a comprehensive care delivered by a team of dedicated healthcare professionals with a strong emphasis on long-term health-enhancing Behaviors. However, "Uncertainty" exists with varied reasons for the political and geographical barriers of referral, uptake, attendance, and completion of PR in both primary and secondary care. Besides, COVID-19 pandemic has sparked many global controversies and challenges on pulmonary rehabilitation service delivery. Post-COVID-19 guidelines emphasize on integrated care rehabilitation for patients with COPD. Thus, this concise review intends to understand the gaps in United Kingdom healthcare policies, practices, and PR services resources. To date, there is no clear consensus on PR integrated care model pathway to address the unmet needs, measure the health and social care disparities; adds to the disease burden of COPD. Based on the culmination of evidence, this perspective offers a theoretical framework of PR integrated service model, a pathway to deliver high-value personalized care to patients with COPD.

The Relation Between Clinical Phenotypes, GOLD Groups/Stages and Mortality in COPD Patients - A Prospective Multicenter Study

Introduction

The concept of phenotyping emerged, reflecting specific clinical, pulmonary and extrapulmonary features of each particular chronic obstructive pulmonary disease (COPD) case. Our aim was to analyze prognostic utility of: “Czech“ COPD phenotypes and their most frequent combinations, ”Spanish” phenotypes and Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages + groups in relation to long-term mortality risk.

Methods

Data were extracted from the Czech Multicenter Research Database (CMRD) of COPD. Kaplan-Meier (KM) estimates (at 60 months from inclusion) were used for mortality assessment. Survival rates were calculated for the six elementary “Czech” phenotypes and their most frequent and relevant combinations, “Spanish” phenotypes, GOLD grades and groups. Statistically significant differences were tested by Log Rank test. An analysis of factors underlying mortality risk (the role of confounders) has been assessed with the use of classification and regression tree (CART) analysis. Basic factors showing significant differences between deceased and living patients were entered into the CART model. This showed six different risk groups, the differences in risk were tested by a Log Rank test.

Results

The cohort (n=720) was 73.1% men, with a mean age of 66.6 years and mean FEV₁ 44.4% pred. KM estimates showed bronchiectases/COPD overlap (HR 1.425, p=0.045), frequent exacerbator (HR 1.58, p<0.001), cachexia (HR 2.262, p<0.001) and emphysematous (HR 1.786, p=0.015) phenotypes associated with higher mortality risk. Co-presence of multiple phenotypes in a single patient had additive effect on risk; combination of emphysema, cachexia and frequent exacerbations translated into poorest prognosis (HR 3.075; p<0.001). Of the “Spanish” phenotypes, AE CB and AE non-CB were associated with greater risk of mortality (HR 1.787 and 2.001; both p=0.001). FEV₁% pred., cachexia and chronic heart failure in patient history were the major underlying factors determining mortality risk in our cohort.

Conclusion

Certain phenotypes (“Czech” or “Spanish”) of COPD are associated with higher risk of death. Co-presence of multiple phenotypes (emphysematous plus cachectic plus frequent exacerbator) in a single individual was associated with amplified risk of mortality.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Neurological and Psychiatric Comorbidities in Chronic Obstructive Pulmonary Disease

Background and Purpose

Chronic obstructive pulmonary disease (COPD) is often accompanied by different neurological and psychiatric comorbidities. The purpose of this study was to examine which of them are the most frequent and to explore whether their manifestation can be explained by underlying latent variables.

Methods

Data about patients with COPD and their neurological and psychiatric comorbidities were extracted from an electronic database of the National Health Insurance Fund of Lithuania for the period between January 1, 2012, and June 30, 2014. Exploratory factor analysis (EFA) was used to investigate comorbidity patterns.

Results

A study sample of 4834 patients with COPD was obtained from the database, 3338 (69.1%) of who were male. The most frequent neurological and psychiatric comorbidities were nerve, nerve root and plexus disorders (n=1439, 29.8%), sleep disorders (n=666, 13.8%), transient ischemic attack (n=545, 11.3%), depression (n=364, 7.5%) and ischemic stroke (n=349, 7.2%). The prevalence of ischemic stroke, transient ischemic attack, Parkinson’s disease, dementia and sleep disorders increased with age. One latent variable outlined during EFA grouped neurological disorders, namely ischemic stroke, transient ischemic attack, epilepsy, dementia and Parkinson’s disease. The second encompassed depression, anxiety, somatoform and sleep disorders. While similar patterns emerged in data from male patients, no clear comorbidity profiles among women with COPD were obtained.

Conclusion

Our study provides novel insights into the neurological and psychiatric comorbidities in COPD by outlining an association among cerebrovascular, neurodegenerative disorders and epilepsy, and psychiatric and sleep disorders. Future studies could substantiate the discrete pathological mechanism that underlie these comorbidity groups.

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

[Comparing group educational intervention to individual and self-learning in COPD]

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) is considered a public health issue which affects 10.2% of Spanish population between 40 and 80 years of age. Many patients do not perform well the inhalation technique. Error rates vary between 50-80% depending on the device under study. These values haven been proven to decrease with educational interventions.

OBJECTIVE

To ascertain whether a group educational intervention is superior to an individual intervention or to a conventional approach in these patients as regards quality of life measured by means of the total score of the COPD Assessment Test (CAT),of adherence to treatment, exacerbations and hospitalizations.

MATERIAL AND METHODS

A multicenter, multidisciplinary cluster-randomized controlled clinical trial with three branches (conventional intervention, individual intervention and group intervention) in a cohort of COPD-patients. Sociodemographic data and risk factors were collected and several questionnaires were completed (CAT, BODEx, Barthel, Lawton y Brody). A descriptive analysis of qualitative and quantitative variables and a multiple linear regression were conducted.

OUTCOMES

149 patients of average age 69.08 (SD 1.26). Significant differences were observed in CAT in the different intervention groups according to the level of severity on BODEx. The rate of patients performing well the inhalation technique was significantly lower at the beginning of the study and the number of exacerbations was lower after the intervention. Last year's exacerbations were linearly related to post-intervention suffering.

CONCLUSIONS

Better results are obtained using the traditional and individual interventions. There is a decrease in number of exacerbations, hospitalizations, CAT score and post-intervention inhalation technique.

Chronic obstructive pulmonary disease - diagnosis and management of stable disease; a personalized approach to care, using the treatable traits concept based on clinical phenotypes. Position paper of the Czech Pneumological and Phthisiological Society

This position paper has been drafted by experts from the Czech national board of diseases with bronchial obstruction, of the Czech Pneumological and Phthisiological Society. The statements and recommendations are based on both the results of randomized controlled trials and data from cross-sectional and prospective real-life studies to ensure they are as close as possible to the context of daily clinical practice and the current health care system of the Czech Republic. Chronic Obstructive Pulmonary Disease (COPD) is a preventable and treatable heterogeneous syndrome with a number of pulmonary and extrapulmonary clinical features and concomitant chronic diseases. The disease is associated with significant mortality, morbidity and reduced quality of life. The main characteristics include persistent respiratory symptoms and only partially reversible airflow obstruction developing due to an abnormal inflammatory response of the lungs to noxious particles and gases. Oxidative stress, protease-antiprotease imbalance and increased numbers of pro-inflammatory cells (mainly neutrophils) are the main drivers of primarily non-infectious inflammation in COPD. Besides smoking, household air pollution, occupational exposure, low birth weight, frequent respiratory infections during childhood and also genetic factors are important risk factors of COPD development. Progressive airflow limitation and airway remodelling leads to air trapping, static and dynamic hyperinflation, gas exchange abnormalities and decreased exercise capacity. Various features of the disease are expressed unequally in individual patients, resulting in various types of disease presentation, emerging as the "clinical phenotypes" (for specific clinical characteristics) and "treatable traits" (for treatable characteristics) concept. The estimated prevalence of COPD in Czechia is around 6.7% with 3,200-3,500 deaths reported annually. The elementary requirements for diagnosis of COPD are spirometric confirmation of post-bronchodilator airflow obstruction (post-BD FEV₁/VCmax <70%) and respiratory symptoms assessement (dyspnoea, exercise limitation, cough and/or sputum production. In order to establish definite COPD diagnosis, a five-step evaluation should be performed, including: 1/ inhalation risk assessment, 2/ symptoms evaluation, 3/ lung function tests, 4/ laboratory tests and 5/ imaging. At the same time, all alternative diagnoses should be excluded. For disease classification, this position paper uses both GOLD stages (1 to 4), GOLD groups (A to D) and evaluation of clinical phenotype(s). Prognosis assessment should be done in each patient. For this purpose, we recommend the use of the BODE or the CADOT index. Six elementary clinical phenotypes are recognized, including chronic bronchitis, frequent exacerbator, emphysematous, asthma/COPD overlap (ACO), bronchiectases with COPD overlap (BCO) and pulmonary cachexia. In our concept, all of these clinical phenotypes are also considered independent treatable traits. For each treatable trait, specific pharmacological and non-pharmacological therapies are defined in this document. The coincidence of two or more clinical phenotypes (i.e., treatable traits) may occur in a single individual, giving the opportunity of fully individualized, phenotype-specific treatment. Treatment of COPD should reflect the complexity and heterogeneity of the disease and be tailored to individual patients. Major goals of COPD treatment are symptom reduction and decreased exacerbation risk. Treatment strategy is divided into five strata: risk elimination, basic treatment, phenotype-specific treatment, treatment of respiratory failure and palliative care, and treatment of comorbidities. Risk elimination includes interventions against tobacco smoking and environmental/occupational exposures. Basic treatment is based on bronchodilator therapy, pulmonary rehabilitation, vaccination, care for appropriate nutrition, inhalation training, education and psychosocial support. Adequate phenotype-specific treatment varies phenotype by phenotype, including more than ten different pharmacological and non-pharmacological strategies. If more than one clinical phenotype is present, treatment strategy should follow the expression of each phenotypic label separately. In such patients, multicomponental therapeutic regimens are needed, resulting in fully individualized care. In the future, stronger measures against smoking, improvements in occupational and environmental health, early diagnosis strategies, as well as biomarker identification for patients responsive to specific treatments are warranted. New classes of treatment (inhaled PDE3/4 inhibitors, single molecule dual bronchodilators, anti-inflammatory drugs, gene editing molecules or new bronchoscopic procedures) are expected to enter the clinical practice in a very few years.

Systems medicine 2030: A Delphi study on implementation in the German healthcare system

INTRODUCTION

Systems medicine is based on approaches taken from systems biology, omics research, bio-informatics and network theory. It promises to facilitate a better understanding of the causes of diseases, detection at an earlier stage, and the use of tailor-made approaches to prevention and therapy. This study provides information on how systems medicine could be incorporated into the German healthcare system.

METHODS

In a Policy Delphi, consensus and divergence was elicited on whether experts believed systems medicine could be incorporated into the German healthcare system by 2030. Additionally, factors that could influence the implementation process were analysed. 11 theses on potential systems medicine developments and 193 arguments on influencing factors were evaluated. Experts from health and health-related fields were selected using "purposive sampling".

RESULTS

The experts interviewed expressed their trust in the provision of a legal-political framework, though they remained uncertain as to whether the necessary social discourse on the ethical and cultural questions surrounding systems medicine would occur. They do not (currently) expect systems medicine to be implemented by 2030.

CONCLUSION

Systems medicine is currently regarded as a visionary concept. As such, it would be premature to attempt to judge the success of the translation process at this stage. The results can help with the identification of the challenges involved in implementation, and the action required to achieve this aim.

Heavy shackles: The experience of symptom distress and coping behaviors of Chinese patients with chronic obstructive pulmonary disease

People with chronic obstructive pulmonary disease exhibit various symptoms, some of which can negatively affect their daily lives. Thus, they may adopt coping behaviors to improve their condition. This qualitative descriptive study investigated symptom distress and coping behaviors among 19 Chinese patients with chronic obstructive pulmonary disease using individual, semi-structured, face-to-face interviews. We identified the following three themes for the patients' symptom distress: distressing symptoms, inescapable imprisonment, and no choice other than being a burden to the family. The various coping behaviors of the patients were categorized into the following three themes: struggle during the medical treatment process, careful maintenance of daily life, and coping with negative emotions. Although all patients experienced physical and psychological distress, they displayed a strong desire to improve their lives and health. By recognizing the patients' symptom distress and coping behaviors, tailored interventions could be developed to improve the quality of their lives.

Induced sputum metabolomic profiles and oxidative stress are associated with chronic obstructive pulmonary disease (COPD) severity: potential use for predictive, preventive, and personalized medicine

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous disease, and metabolomics plays a hub role in predictive, preventive, and personalized medicine (PPPM) related to COPD. This study thus aimed to reveal the role of induced sputum metabolomics in predicting COPD severity. In this pilot study, a total of 20 COPD patients were included. The induced sputum metabolites were assayed using a liquid chromatography-mass spectrometry (LC-MS/MS) system. Five oxidative stress products (myeloperoxidase (MPO), superoxide dismutase (SOD), glutathione (GSH), neutrophil elastase (NE), and 8-iso-PGF2α) in induced sputum were measured by ELISA, and the metabolomic profiles were distinguished by principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA). The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway enrichment analysis, and a significant difference in induced sputum metabolomics was observed between moderate and severe COPD. The KEGG analysis revealed that the glycerophospholipid metabolism pathway was downregulated in severe COPD. Due to the critical role of glycerophospholipid metabolism in oxidative stress, significant negative correlations were discovered between glycerophospholipid metabolites and three oxidative stress products (SOD, MPO, and 8-iso-PGF2α). The diagnostic values of SOD, MPO, and 8-iso-PGF2α in induced sputum were found to exhibit high sensitivities and specificities in the prediction of COPD severity. Collectively, this study provides the first identification of the association between induced sputum metabolomic profiles and COPD severity, indicating the potential value of metabolomics in PPPM for COPD management. The study also reveals the correlation between glycerophospholipid metabolites and oxidative stress products and their value for predicting COPD severity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-020-00227-w.

Treatable traits qualifying for nonpharmacological interventions in COPD patients upon first referral to a pulmonologist: the COPD sTRAITosphere

Introduction

The present study assessed the prevalence of nine treatable traits (TTs) pinpointing nonpharmacological interventions in patients with COPD upon first referral to a pulmonologist, how these TTs co-occurred and whether and to what extent the TTs increased the odds having a severely impaired health status.

Methods

Data were collected from a sample of 402 COPD patients. A second sample of 381 patients with COPD was used for validation. Nine TTs were assessed: current smoking status, activity-related dyspnoea, frequent exacerbations <12 months, severe fatigue, depressed mood, poor physical capacity, low physical activity, poor nutritional status and a low level of self-management activation. For each TT the odds ratio (OR) of having a severe health status impairment was calculated. Furthermore, a graphic representation was created, the COPD sTRAITosphere, to visualise TTs prevalence and OR.

Results

On average 3.9±2.0 TTs per patient were observed. These TTs occurred relatively independently of each other and coexisted in 151 unique combinations. A significant positive correlation was found between the number of TTs and Clinical COPD Questionnaire total score (r=0.58; p<0.001). Patients with severe fatigue (OR: 8.8), severe activity-related dyspnoea (OR: 5.8) or depressed mood (OR: 4.2) had the highest likelihood of having a severely impaired health status. The validation sample corroborated these findings.

Conclusions

Upon first referral to a pulmonologist, COPD patients show multiple TTs indicating them to several nonpharmacological interventions. These TTs coexist in many different combinations, are relatively independent and increase the likelihood of having a severely impaired health status.

Therapeutic Targeting of MMP-12 for the Treatment of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a lung disorder characterized by progressive airflow obstruction associated with inflammation and emphysema, and it is currently one of the leading causes of death worldwide. Recent studies with genetically engineered mice reported that during pulmonary inflammation, basophil-derived interleukin-4 can act on lung-infiltrating monocytes causing aberrant expression of the matrix metalloproteinase-12 (MMP-12). MMP-12 activity in turn causes the destruction of alveolar walls leading to emphysema, making it potentially a valid target for pharmacological intervention. Using nuclear magnetic resonance (NMR)- and structure-based optimizations, the current study reports on the optimized novel, potent, and selective MMP-12 inhibitors with single-digit nanomolar affinity in vitro and in vivo efficacy. Using a murine model of elastase-induced emphysema we demonstrated that the most potent agents exhibited a significant decrease in emphysema-like pathology compared to vehicle-treated mice, thus suggesting that the reported agents may potentially be translated into novel therapeutics for the treatment of COPD.

Comorbid Conditions in Chronic Obstructive Pulmonary Disease: Potential Therapeutic Targets for Unmet Needs

The management of chronic obstructive pulmonary disease (COPD) has improved significantly due to advances in therapeutic agents, but it has also become apparent that there are issues that remain difficult to solve with the current treatment algorithm. COPD patients face a number of unmet needs concerning symptoms, exacerbations, and physical inactivity. There are various risk factors and triggers for these unmet needs, which can be roughly divided into two categories. One is the usual clinical characteristics for COPD patients, and the other is specific clinical characteristics in patients with comorbid conditions, such as asthma, cardiovascular disease, and bronchiectasis. These comorbidities, which are also associated with the diversity of COPD, can cause unmet needs resistance to usual care. However, treatable conditions that are not recognized as therapeutic targets may be latent in patients with COPD. We again realized that treatable traits should be assessed and treated as early as possible. In this article, we categorize potential therapeutic targets from the viewpoint of pulmonary and systemic comorbid conditions, and address recent data concerning the pathophysiological link with COPD and the impact of intervention on comorbid conditions in order to obtain evidence that could enable us to provide personalized COPD management.

Therapeutic effects of black seed oil supplementation on chronic obstructive pulmonary disease patients: A randomized controlled double blind clinical trial

This study sought to examine whether supplementation of Black Seed Oil (BSO) can improve pulmonary function tests (PFTs), inflammation, and oxidant-antioxidant markers in COPD patients. The study involved 100 patients of mild to moderate COPD divided randomly into 2 groups who were appointed to receive standard medication only (control group) or with additional Black Seed Oil (BSO group). They were assessed initially and after 3 months, 44 patients responded in control group and 47 patients in BSO group. BSO group evidenced a significant decreasing in oxidant and inflammatory markers; thiobarbituric acid reactive-substances (TBARS), protein carbonyl (PC) content, interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α), a significant increase in antioxidants; superoxide dismutase (SOD), catalase (CAT), reduced glutathione (GSH), glutathione peroxidase (GPx), vitamin C, and E, and a significant improvement in PFTs versus control group and baseline levels. Supplementation of Black Seed Oil may be an effective adjunct therapy to improve pulmonary functions, inflammation, and oxidant-antioxidant imbalance in COPD patients.

Artificial intelligence and machine learning in respiratory medicine

Introduction: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly relevant topic.Areas covered: We aimed to identify and describe the studies published on the use of AI and ML in the field of respiratory diseases. The string '(((pulmonary) OR respiratory)) AND ((artificial intelligence) OR machine learning)' was used in PubMed as a search strategy. The majority of studies identified corresponded to the area of chronic obstructive pulmonary disease (COPD), in particular to COPD and chest computed tomography scans, interpretation of pulmonary function tests, exacerbations and treatment. Another field of interest is the application of AI and ML to the diagnosis of interstitial lung disease, and a few other studies were identified on the fields of mechanical ventilation, interpretation of images on chest X-ray and diagnosis of bronchial asthma.Expert opinion: ML may help to make clinical decisions but will not replace the physician completely. Human errors in medicine are associated with large financial losses, and many of them could be prevented with the help of AI and ML. AI is particularly useful in the absence of conclusive evidence of decision-making.

Effectiveness of non-pharmacological COPD management on health-related quality of life - a systematic review

Introduction: Chronic obstructive pulmonary disease (COPD) is the third leading cause of mortality worldwide. The chronic progressive disease is accompanied by a high loss of health-related quality of life (HRQoL). The available drugs usually only have symptomatic effects; therefore, non-pharmacological therapies are essential too.Areas covered: This systematic review examines non-pharmacological interventions consisting of pulmonary rehabilitation, physical activity, and training versus usual care or no intervention in COPD using at least one of the following HRQoL measuring instruments: St. George's Respiratory Questionnaire, Clinical COPD Questionnaire, COPD Assessment Test, and EuroQol-5D. Of 1532 identified records from CENTRAL, MEDLINE, and EMBASE, 15 randomized controlled trials met the inclusion criteria. Pulmonary rehabilitation programs were investigated in nine studies, education and counseling-based training programs in three studies, and breathing exercises in three studies. Ten studies were found that investigated non-pharmacological treatment programs that led to a significant and clinically relevant improvement in HRQoL compared with usual care or no treatment.Expert opinion: Non-pharmacological interventions consisting of pulmonary rehabilitation, education and counseling-based training programs, and breathing exercises can improve the HRQoL of COPD patients.

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.

Gathering Structured Patient Insight to Drive the PRO Strategy in COPD: Patient-Centric Drug Development from Theory to Practice

We illustrate our experience of gathering patient insights on the most patient-relevant symptoms in chronic obstructive pulmonary disease (COPD) via a structured and systematic approach towards ‘patient-centric’ drug development, leveraging recent advances in digital technologies using online platforms. The four-step approach comprised the following: literature search, social media listening (SML) study, online bulletin board (OBB) exercise, and design of an online patient preference study (PPS). The initial online studies (SML and OBB) revealed that, besides dyspnoea and exacerbations, patients perceive cough and mucus production as equally important aspects of disease management for COPD. To further build and quantify patients’ understanding of the importance of these symptoms, an online patient preference survey is underway. Based on these findings, we have elected to include the Cough and Sputum Assessment Questionnaire or CASA-Q, a validated instrument to collect patient-reported outcomes (PRO), besides the use of the COPD assessment test or CAT to assess the severity and impact of COPD in drug development studies for COPD. Additionally, to capture movement and sleep disturbance, we consider the inclusion of actigraphy as a digital evidence-capture end point. Lastly, in a phase II trial, a survey questionnaire on incontinence will be administered to evaluate the importance of this issue among patients. We believe that integrating insights derived from “online” studies (SML, OBB, and PPS) into drug development offers an opportunity to truly listen to patients’ voices in early product design ensuring relevance of end points selected for the clinical trial program. This approach also has the potential to complement conventional qualitative and quantitative data collection requirements for PRO instrument development. While awaiting final guidance from the US Food and Drug Administration, or FDA, the recently released draft documents on collecting representative patients’ input reference social media as a tool to collect qualitative patient preference data and these developments suggest that patient preference data can influence future clinical trial design, end point selection, and regulatory reviews.

Funding: Novartis Pharma AG, Basel.