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Background: Despite potential benefits and widespread prescription of aspirin among chronic obstructive pulmonary disease (COPD) patients, limited research has investigated its adverse effects (AEs) in COPD population.
Methods: We conducted a retrospective analysis of adverse drug events (ADEs) reported in the US Food and Drug Administration Adverse Event Reporting System (FAERS) between Q1 2013 and Q2 2022. COPD patients were categorized into two groups based on aspirin use. ADEs related to aspirin use were identified using combined reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC) methods.
Results: A total of 56,660 ADEs reports associated with COPD patients were included in the study. Among these reports, 144 adverse events were linked to aspirin use in COPD patients, including fatigue (4.12%), diarrhea (3.13%), dyspnea exertional (2.03%), rhinorrhea (1.99%), weight increased (1.89%) and vomiting (1.84%), muscle spasms (1.79%), cardiac disorder (1.74%), heart rate increased (1.69%) and peripheral swelling (1.59%). Subgroup analysis indicates that age and gender might affect the AEs frequency in COPD patients using aspirin.
Conclusions: Our findings identify 10 most frequently reported ADEs associated with aspirin use in COPD patients, thus offer valuable insights into the AEs of aspirin for safer clinical utilization in COPD management.
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http://dx.doi.org/10.1080/17476348.2023.2294927 | DOI Listing |
Int J Chron Obstruct Pulmon Dis
September 2025
The First Clinical Medical College of Lanzhou University, Lanzhou, People's Republic of China.
Chronic Obstructive Pulmonary Disease (COPD) is a prevalent chronic respiratory disorder characterized by airway inflammation and irreversible airflow limitation. Its marked heterogeneity and complexity pose significant challenges to traditional clinical assessments in terms of prognostic prediction and personalized management. In recent years, the exploration of biomarkers has opened new avenues for the precise evaluation of COPD, particularly through multi-biomarker prediction models and integrative multimodal data strategies, which have substantially improved the accuracy and reliability of prognostic assessments.
View Article and Find Full Text PDFERJ Open Res
September 2025
Univ. Grenoble Alpes, Inserm, CHU Grenoble Alpes, HP2, Grenoble, France.
https://bit.ly/44RG0XW.
View Article and Find Full Text PDFERJ Open Res
September 2025
Respiratory Rehabilitation Unit, Istituti Clinici Scientifici Maugeri IRCCS, Institute of Lumezzane, Brescia, Italy.
Background: In patients with moderate COPD, response to pulmonary rehabilitation including exercise training varies according to the presence of peripheral muscle fatigue (pMF) of quadriceps. This study investigates the role of pMF in predicting pulmonary rehabilitation outcomes in more severe COPD patients who have already developed chronic respiratory failure (COPD-CRF).
Methods: A analysis of a prospective randomised controlled trial was performed at Istituti Clinici Scientifici Maugeri Lumezzane (Brescia, Italy), involving 30 COPD-CRF patients undergoing a pulmonary rehabilitation programme comprising 20 endurance training sessions.
ERJ Open Res
September 2025
Department of Bioanalysis, Pharmaceutical Care Unit, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.
Background: In Belgium, age-standardised hospital admission and mortality rates for asthma and COPD are higher than the European average. Understanding the factors that lead to a hospitalised exacerbation and/or mortality is needed to optimise patient management.
Methods: Patients ≥18 years old obtaining two claims for drugs for obstructive airway diseases (ATC code R03) in 1 year between 2017 and 2022 were identified in Belgian nationwide claims-based data.
Front Med (Lausanne)
August 2025
State Key Laboratory of Respiratory Diseases, Guangzhou Medical University, Guangzhou, China.
Background: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease. However, the biological role of mitochondrial metabolism (MM) in COPD remains poorly understood. This study aimed to explore the underlying mechanisms of MM in COPD using bioinformatics methods.
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