The EASI model: A first integrative computational approximation to the natural history of COPD
Cosio, Borja G.
Anto, Josep Maria
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CitationAgustí, Alvar, Albert Compte, Rosa Faner, Judith Garcia-Aymerich, Guillaume Noell, Borja G. Cosio, Robert Rodriguez-Roisin, Bartolomé Celli, and Josep Maria Anto. 2017. “The EASI model: A first integrative computational approximation to the natural history of COPD.” PLoS ONE 12 (10): e0185502. doi:10.1371/journal.pone.0185502. http://dx.doi.org/10.1371/journal.pone.0185502.
AbstractThe natural history of chronic obstructive pulmonary disease (COPD) is still not well understood. Traditionally believed to be a self-inflicted disease by smoking, now we know that not all smokers develop COPD, that other inhaled pollutants different from cigarette smoke can also cause it, and that abnormal lung development can also lead to COPD in adulthood. Likewise, the inflammatory response that characterizes COPD varies significantly between patients, and not all of them perceive symptoms (mostly breathlessness) similarly. To investigate the variability and determinants of different “individual natural histories” of COPD, we developed a theoretical, multi-stage, computational model of COPD (EASI) that integrates dynamically and represents graphically the relationships between exposure (E) to inhaled particles and gases (smoking), the biological activity (inflammatory response) of the disease (A), the severity (S) of airflow limitation (FEV1) and the impact (I) of the disease (breathlessness) in different clinical scenarios. EASI shows that the relationships between E, A, S and I vary markedly within individuals (through life) and between individuals (at the same age). It also helps to delineate some potentially relevant, but often overlooked concepts, such as disease progression, susceptibility to COPD and issues related to symptom perception. In conclusion, EASI is an initial conceptual model to interpret the longitudinal and cross-sectional relationships between E, A, S and I in different clinical scenarios. Currently, it does not have any direct clinical application, thus it requires experimental validation and further mathematical development. However, it has the potential to open novel research and teaching alternatives.
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