Diagnosis, Treatment, and Management of COPD and Asthma: Advances in the 21st Century

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 4678

Special Issue Editor


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Guest Editor
Department of Respiratory Medicine, National Center for Geriatrics and Gerontology, Obu 474-8511, Japan
Interests: COPD; asthma; interstitial pneumonia; patient-reported outcomes; quality of life; health status; respiratory medicine; computed tomography

Special Issue Information

Dear Colleagues,

Chronic obstructive pulmonary disease (COPD) is a major cause of mortality and morbidity globally, and is the third leading cause of death in the USA. Since many patients with COPD complain of dyspnoea and exertional intolerance, the condition has been one of the model diseases for measuring patient-reported outcomes (PROs), such as health-related quality of life. For instance, the Chronic Respiratory Disease Questionnaire (CRQ) was the first published disease-specific tool for measuring the quality of life around the world. On the other hand, although so far there have been plenty of clinical practice guidelines across the world, asthma guidelines were the origin of all of the medical guidelines. An asthma management plan was first published in 1989 by Southampton’s group and the Thoracic Society of Australia and New Zealand, followed by the Guidelines for the Diagnosis and Management of Asthma, established by the U.S. Department of Health and Human Services. As just described, chest physicians have been playing a leading role in addressing critical development issues of medicine. The purpose of this Special Issue, entitled “Diagnosis, Treatment, and Management of COPD and Asthma: Advances in the 21st Century”, is to share up-to-date information and include recent knowledge regarding COPD and asthma. I believe this would be the best opportunity to publish your excellent work. Articles such as original articles or reviews are welcome, but we do not accept case/brief reports.  Topics include, but are not limited to, the following:

  • Early COPD or late COPD.
  • Blood eosinophil count as a biomarker.
  • Asthma and COPD overlap.
  • Single-inhaler or multiple-inhaler triple therapy.
  • Definition of COPD exacerbation.
  • Frequent exacerbators.
  • Patient-reported outcomes.
  • Health status or health-related quality of life.
  • Dyspnoea measurement.
  • Epidemiology of dyspnoea.
  • Integrated care as well as COPD care bundles.
  • Telemedicine and eHealth.

Dr. Koichi Nishimura
Guest Editor

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Published Papers (2 papers)

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Research

14 pages, 3070 KiB  
Article
The Role of Multidimensional Indices for Mortality Prediction in Chronic Obstructive Pulmonary Disease
by Stanislav Kotlyarov
Diagnostics 2023, 13(7), 1344; https://doi.org/10.3390/diagnostics13071344 - 04 Apr 2023
Cited by 2 | Viewed by 1194
Abstract
(1) Background: Chronic obstructive pulmonary disease (COPD) is one of the most important respiratory diseases. It is characterised by a progressive course with individual differences in clinical presentation and prognosis. The use of multidimensional indices such as the BODE, eBODE, BODEX, CODEX, ADO, [...] Read more.
(1) Background: Chronic obstructive pulmonary disease (COPD) is one of the most important respiratory diseases. It is characterised by a progressive course with individual differences in clinical presentation and prognosis. The use of multidimensional indices such as the BODE, eBODE, BODEX, CODEX, ADO, and Charlson Comorbidity Index has been proposed to predict the survival rate of COPD patients. However, there is limited research on the prognostic significance of these indices in predicting long-term survival rates in patients with COPD. The aim of this prospective cohort study was to investigate the prognostic value of the BODE, eBODE, BODEX, CODEX, ADO, COTE and Charlson Comorbidity Index in predicting 5- and 10-year survival in patients with COPD. (2) Methods: A total of 170 patients were included in the study and their clinical and functional characteristics of COPD progression, such as dyspnoea, body mass index and spirometry data, were evaluated. A Kaplan–Meier survival analysis was used to calculate 5- and 10-year survival rates. The predictive value of each index was assessed using Cox proportional hazards regression models. (3) Results: The 5-year survival rate was 62.35% and the 10-year survival rate was 34.70%. The BODE, eBODE, BODEX, CODEX, ADO, COTE and Charlson Comorbidity Index were all significantly associated with the 10-year survival rate of COPD patients (p < 0.05). The hazard ratios (HRs) for these indices were as follows: BODE (HR = 1.30, 95% confidence interval [CI] 1.21–1.39); eBODE (HR = 1.29, 95% CI 1.21–1.37); BODEX (HR = 1.48, 95% CI 1.35–1.63); CODEX (HR = 1.42, 95% CI 1.31–1.54); COTE (HR = 1.55, 95% CI 1.36–1.75); ADO (HR = 1.41, 95% CI 1.29–1.54); and Charlson Comorbidity Index (HR = 1.35, 95% CI 1.22–1.48). (4) Conclusions: The multidimensional indices are a useful clinical tool for assessing the course and prognosis of COPD. These indices can be used to identify patients at a high risk of mortality and guide the management of COPD patients. Full article
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14 pages, 582 KiB  
Article
An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features
by Hafeez-Ur-Rehman Siddiqui, Ali Raza, Adil Ali Saleem, Furqan Rustam, Isabel de la Torre Díez, Daniel Gavilanes Aray, Vivian Lipari, Imran Ashraf and Sandra Dudley
Diagnostics 2023, 13(6), 1096; https://doi.org/10.3390/diagnostics13061096 - 14 Mar 2023
Cited by 3 | Viewed by 2691
Abstract
Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the [...] Read more.
Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage. Full article
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