Modeling Chronic Obstructive Pulmonary Disease Progression Using Continuous-Time Hidden Markov Models.
Understanding the progression of chronic diseases, such as chronic obstructive pulmonary disease (COPD), is important to inform early diagnosis, personalized care, and health system management. Data from clinical and administrative systems have the potential to advance this understanding, but traditional methods for modelling disease progression are not well-suited to analyzing data collected at irregular intervals, such as when a patient interacts with a healthcare system. We applied a continuous-time hidden Markov model to irregularly-spaced healthcare utilization events and patient-level characteristics in order to analyze the progression through discrete states of 76,888 patients with COPD. A 4-state model allowed classification of patients into interpretable states of disease progression and generated insights about the role of comorbidities, such as cardiovascular diseases, in accelerating severe trajectories. These results can improve the understanding of the evolution of COPD and point to new hypotheses about chronic disease management and comorbidity.
['Powell|Guido Antonio|GA|', 'Verma|Aman|A|', 'Luo|Yu|Y|', 'Stephens|David|D|', 'Buckeridge|David|D|']
D002318:Cardiovascular Diseases D002908:Chronic Disease D015897:Comorbidity D018450:Disease Progression D006801:Humans D029424:Pulmonary Disease, Chronic Obstructive
chronic obstructive pulmonary disease disease progression Latent multi-state models