Chronic obstructive pulmonary disease (COPD) is a leading cause of death, with exacerbations worsening functional decline, reducing quality of life, and increasing healthcare use. Current management remains reactive, with treatment often initiated only after symptoms worsen. Existing monitoring approaches struggle to distinguish between clinically significant deterioration and normal variability, leading to delayed intervention. This article proposes a digital twin framework combining patient-specific respiratory models with real-time wearable data to predict and manage COPD exacerbations proactively. The framework includes a mechanistic lung model, continuous data ingestion, a data assimilation module, an exacerbation prediction layer, and an alert system, enabling early detection of physiological deviations before severe symptoms arise. By supporting pre-emptive telehealth, medication adjustments, and patient self-management with clinician oversight, this approach could shift COPD care from reactive to personalized, proactive management, pending robust modeling, reliable sensing, and real-world validation.
Clinical Intelligence Research Press