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Asthma-COPD Overlap in Clinical Practice (ACO_CP 2023): Toward Precision Medicine

Asthma and COPD have characteristic symptoms, yet patients with both are prevalent. Despite this, there is currently no globally accepted definition for the overlap between asthma and COPD, commonly referred to as asthma–COPD overlap (ACO). Generally, ACO is not considered a distinct disease or symp...

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Detalles Bibliográficos
Autores principales: Alsayed, Ahmad R., Abu-Samak, Mahmoud S., Alkhatib, Mohammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146260/
https://www.ncbi.nlm.nih.gov/pubmed/37109063
http://dx.doi.org/10.3390/jpm13040677
Descripción
Sumario:Asthma and COPD have characteristic symptoms, yet patients with both are prevalent. Despite this, there is currently no globally accepted definition for the overlap between asthma and COPD, commonly referred to as asthma–COPD overlap (ACO). Generally, ACO is not considered a distinct disease or symptom from either clinical or mechanistic perspectives. However, identifying patients who present with both conditions is crucial for guiding clinical therapy. Similar to asthma and COPD, ACO patients are heterogeneous and presumably have multiple underlying disease processes. The variability of ACO patients led to the establishment of multiple definitions describing the condition’s essential clinical, physiological, and molecular characteristics. ACO comprises numerous phenotypes, which affects the optimal medication choice and can serve as a predictor of disease prognosis. Various phenotypes of ACO have been suggested based on host factors including but not limited to demographics, symptoms, spirometric findings, smoking history, and underlying airway inflammation. This review provides a comprehensive clinical guide for ACO patients to be used in clinical practice based on the available limited data. Future longitudinal studies must evaluate the stability of ACO phenotypes over time and explore their predictive powers to facilitate a more precise and effective management approach.