Abstract
The 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.
Highlights
The natural history of chronic obstructive pulmonary disease (COPD) is still not well understood
We hypothesize that the natural history of COPD is the end-result of a complex multi-stage process, and that each of these stages exhibits large individual variability that result in different natural history trajectories
EASI is envisaged as a theoretical, conceptual computational model that begins to explore the relationships between E, A, S and I in different clinical scenarios to facilitate the design of appropriate field studies that can confirm or dispute the predictions of the model [13]
Summary
The natural history of chronic obstructive pulmonary disease (COPD) is still not well understood. Computational models can help to understand complex biological problems by offering a theoretical framework where to explore the relationships amongst different variables [12]. They have, the potential to generate novel hypotheses that can be later tested experimentally [12, 13]. As a first attempt to explore this hypothesis, we developed an individualized, multi-stage computational model of COPD (named EASI) that explores, integrates and displays graphically the dynamic relationships in a given individual between Exposure (smoking), Activity (Inflammation), Severity (as assessed by the expired volume of gas in the first second of a forced spirometry maneuver—FEV1) and Impact of the disease (dyspnea). EASI is envisaged as a theoretical, conceptual computational model that begins to explore the relationships between E, A, S and I in different clinical scenarios to facilitate the design of appropriate field studies that can confirm or dispute the predictions of the model [13]
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