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Diagnostics
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Advances in Pulmonary and Critical Care Medicine: Diagnosis and Management
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Advances in Pulmonary and Critical Care Medicine: Diagnosis and Management

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 9077

Special Issue Editor

Dr. Te-Chun Shen

E-Mail Website
Guest Editor
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung 404, Taiwan
Interests: pulmonary; critical care medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Pulmonary and critical care medicine stands at the forefront of medical research due to its significant impact on patient outcomes and healthcare systems. The COVID-19 pandemic has further underscored the critical role of this field in addressing respiratory infections, mechanical ventilation, and the management of critically ill patients. Advances in this area not only promise to improve patient care but also aim to reduce the burden on healthcare resources.

This Special Issue aims to explore the latest advances in pulmonary and critical care medicine, focusing on innovative diagnostic techniques and management strategies. With the increasing prevalence of respiratory diseases globally, there is an urgent need for more effective diagnostic tools and therapeutic approaches. The challenges of managing complex pulmonary conditions necessitate multidisciplinary efforts and innovations in medical technology and patient care.

The goal of this Special Issue is to bring together leading researchers, clinicians, and healthcare professionals to share their findings, insights, and experiences in pulmonary and critical care medicine. In this Special Issue, original clinical and basic research articles and reviews are welcome. Research areas may include (but not limited to) the following: COPD, asthma, interstitial lung disease, lung cancer, respiratory infections, obstructive sleep apnea, respiratory failure, and acute respiratory distress syndrome. I look forward to receiving your contributions.

Dr. Te-Chun Shen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • COPD
  • asthma
  • interstitial lung disease
  • lung cancer
  • pneumonia (respiratory infection, pulmonary infection)
  • obstructive sleep apnea
  • respiratory failure
  • acute respiratory distress syndrome

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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14 pages, 1343 KiB  
Article
Detection of Respiratory Disease Based on Surface-Enhanced Raman Scattering and Multivariate Analysis of Human Serum
by Yulia Khristoforova, Lyudmila Bratchenko, Vitalii Kupaev, Dmitry Senyushkin, Maria Skuratova, Shuang Wang, Petr Lebedev and Ivan Bratchenko
Diagnostics 2025, 15(6), 660; https://doi.org/10.3390/diagnostics15060660 - 8 Mar 2025
Viewed by 731
Abstract
Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a significant public health concern, affecting millions of people worldwide. This study aims to use Surface-Enhanced Raman Scattering (SERS) technology to detect the presence of respiratory conditions, with a focus on COPD. Methods: The [...] Read more.
Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a significant public health concern, affecting millions of people worldwide. This study aims to use Surface-Enhanced Raman Scattering (SERS) technology to detect the presence of respiratory conditions, with a focus on COPD. Methods: The samples of human serum from 41 patients with respiratory diseases (11 patients with COPD, 20 with bronchial asthma (BA), and 10 with asthma–COPD overlap syndrome) and 103 patients with ischemic heart disease, complicated by chronic heart failure (CHF), were analyzed using SERS. A multivariate analysis of the SERS characteristics of human serum was performed using Partial Least Squares Discriminant Analysis (PLS-DA) to classify the following groups: (1) all respiratory disease patients versus the pathological referent group, which included CHF patients, and (2) patients with COPD versus those with BA. Results: We found that a combination of SERS characteristics at 638 and 1051 cm−1 could help to identify respiratory diseases. The PLS-DA model achieved a mean predictive accuracy of 0.92 for classifying respiratory diseases and the pathological referent group (0.85 sensitivity, 0.97 specificity). However, in the case of differentiating between COPD and BA, the mean predictive accuracy was only 0.61. Conclusions: Therefore, the metabolic and proteomic composition of human serum shows significant differences in respiratory disease patients compared to the pathological referent group, but the differences between patients with COPD and BA are less significant, suggesting a similarity in the serum and general pathogenetic mechanisms of these two conditions. Full article
(This article belongs to the Special Issue Advances in Pulmonary and Critical Care Medicine: Diagnosis and Management)
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27 pages, 2729 KiB  
Article
Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms
by Walaa H. Elashmawi, Adel Djellal, Alaa Sheta, Salim Surani and Sultan Aljahdali
Diagnostics 2024, 14(24), 2822; https://doi.org/10.3390/diagnostics14242822 - 14 Dec 2024
Cited by 1 | Viewed by 1231
Abstract
Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such [...] Read more.
Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing. Full article
(This article belongs to the Special Issue Advances in Pulmonary and Critical Care Medicine: Diagnosis and Management)
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11 pages, 636 KiB  
Article
Clinical Efficacy and Safety of an Automatic Closed-Suction System in Mechanically Ventilated Patients with Pneumonia: A Multicenter, Prospective, Randomized, Non-Inferiority, Investigator-Initiated Trial
by Dong-Hyun Joo, Hyo Chan Park, Joon Han Kim, Seo Hee Yang, Tae Hun Kim, Hyung-Jun Kim, Myung Jin Song, Sung Yoon Lim, Sung A Kim, Hee Won Bae, Yoon Hae Ahn, Si Mong Yoon, Jimyung Park, Hong Yeul Lee, Jinwoo Lee, Sang-Min Lee, Jung Chan Lee and Young-Jae Cho
Diagnostics 2024, 14(11), 1068; https://doi.org/10.3390/diagnostics14111068 - 21 May 2024
Viewed by 1743
Abstract
Endotracheal suctioning is an essential but labor-intensive procedure, with the risk of serious complications. A brand new automatic closed-suction device was developed to alleviate the workload of healthcare providers and minimize those complications. We evaluated the clinical efficacy and safety of the automatic [...] Read more.
Endotracheal suctioning is an essential but labor-intensive procedure, with the risk of serious complications. A brand new automatic closed-suction device was developed to alleviate the workload of healthcare providers and minimize those complications. We evaluated the clinical efficacy and safety of the automatic suction system in mechanically ventilated patients with pneumonia. In this multicenter, randomized, non-inferiority, investigator-initiated trial, mechanically ventilated patients with pneumonia were randomized to the automatic device (intervention) or conventional manual suctioning (control). The primary efficacy outcome was the change in the modified clinical pulmonary infection score (CPIS) in 3 days. Secondary outcomes were the frequency of additional suctioning and the amount of secretion. Safety outcomes included adverse events or complications. A total of 54 participants, less than the pre-determined number of 102, were enrolled. There was no significant difference in the change in the CPIS over 72 h (−0.13 ± 1.58 in the intervention group, −0.58 ± 1.18 in the control group, p = 0.866), but the non-inferiority margin was not satisfied. There were no significant differences in the secondary outcomes and safety outcomes, with a tendency for more patients with improved tracheal mucosal injury in the intervention group. The novel automatic closed-suction system showed comparable efficacy and safety compared with conventional manual suctioning in mechanically ventilated patients with pneumonia. Full article
(This article belongs to the Special Issue Advances in Pulmonary and Critical Care Medicine: Diagnosis and Management)
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11 pages, 602 KiB  
Article
The Clinical Impact of the Pulmonary Embolism Severity Index on the Length of Hospital Stay of Patients with Pulmonary Embolism: A Randomized Controlled Trial
by Marco Paolo Donadini, Nicola Mumoli, Patrizia Fenu, Fulvio Pomero, Roberta Re, Gerardo Palmiero, Laura Spadafora, Valeria Mazzi, Alessandra Grittini, Lorenza Bertù, Drahomir Aujesky, Francesco Dentali, Walter Ageno and Alessandro Squizzato
Diagnostics 2024, 14(7), 776; https://doi.org/10.3390/diagnostics14070776 - 7 Apr 2024
Viewed by 2085
Abstract
Background: The Pulmonary Embolism Severity Index (PESI) is an extensively validated prognostic score, but impact analyses of the PESI on management strategies, outcomes and health care costs are lacking. Our aim was to assess whether the adoption of the PESI for patients admitted [...] Read more.
Background: The Pulmonary Embolism Severity Index (PESI) is an extensively validated prognostic score, but impact analyses of the PESI on management strategies, outcomes and health care costs are lacking. Our aim was to assess whether the adoption of the PESI for patients admitted to an internal medicine ward has the potential to safely reduce the length of hospital stay (LOS). Methods: We carried out a multicenter randomized controlled trial, enrolling consecutive adult outpatients diagnosed with acute PE and admitted to an internal medicine ward. Within 48 h after diagnosis, the treating physicians were randomized, for every patient, to calculate and report the PESI in the clinical record form on top of the standard of care (experimental arm) or to continue routine clinical practice (standard of care). The ClinicalTrials.gov identifier is NCT03002467. Results: This study was prematurely stopped due to slow recruitment. A total of 118 patients were enrolled at six internal medicine units from 2016 to 2019. The treating physicians were randomized to the use of the PESI for 59 patients or to the standard of care for 59 patients. No difference in the median LOS was found between the experimental arm (8, IQR 6–12) and the standard-of-care arm (8, IQR 6–12) (p = 0.63). A pre-specified secondary analysis showed that the LOS was significantly shorter among the patients who were treated with DOACs (median of 8 days, IQR 5–11) compared to VKAs or heparin (median of 9 days, IQR 7–12) (p = 0.04). Conclusions: The formal calculation of the PESI in the patients already admitted to internal medicine units did not impact the length of hospital stay. Full article
(This article belongs to the Special Issue Advances in Pulmonary and Critical Care Medicine: Diagnosis and Management)
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Review

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19 pages, 1238 KiB  
Review
Particles in Exhaled Air (PExA): Clinical Uses and Future Implications
by Thomas Roe, Siona Silveira, Zixing Luo, Eleanor L. Osborne, Ganapathy Senthil Murugan, Michael P. W. Grocott, Anthony D. Postle and Ahilanandan Dushianthan
Diagnostics 2024, 14(10), 972; https://doi.org/10.3390/diagnostics14100972 - 7 May 2024
Cited by 1 | Viewed by 2290
Abstract
Access to distal airway samples to assess respiratory diseases is not straightforward and requires invasive procedures such as bronchoscopy and bronchoalveolar lavage. The particles in exhaled air (PExA) device provides a non-invasive means of assessing small airways; it captures distal airway particles (PEx) [...] Read more.
Access to distal airway samples to assess respiratory diseases is not straightforward and requires invasive procedures such as bronchoscopy and bronchoalveolar lavage. The particles in exhaled air (PExA) device provides a non-invasive means of assessing small airways; it captures distal airway particles (PEx) sized around 0.5–7 μm and contains particles of respiratory tract lining fluid (RTLF) that originate during airway closure and opening. The PExA device can count particles and measure particle mass according to their size. The PEx particles can be analysed for metabolites on various analytical platforms to quantitatively measure targeted and untargeted lung specific markers of inflammation. As such, the measurement of distal airway components may help to evaluate acute and chronic inflammatory conditions such as asthma, chronic obstructive pulmonary disease, acute respiratory distress syndrome, and more recently, acute viral infections such as COVID-19. PExA may provide an alternative to traditional methods of airway sampling, such as induced sputum, tracheal aspirate, or bronchoalveolar lavage. The measurement of specific biomarkers of airway inflammation obtained directly from the RTLF by PExA enables a more accurate and comprehensive understanding of pathophysiological changes at the molecular level in patients with acute and chronic lung diseases. Full article
(This article belongs to the Special Issue Advances in Pulmonary and Critical Care Medicine: Diagnosis and Management)
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