A Real-Time COPD Exacerbation Detection Algorithm Using multi-morbid symptom diaries: Insights from a Multi-Site Study

Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide and is often accompanied by comorbidities. Patients, due to their health condition, may experience a rapid worsening of symptoms, defined as an exacerbation, which could lead to undesirable hospitalizations or emergency care. To lower this burden, early detection of symptoms as well as health monitoring are crucial. Data from routine clinical visits and follow-ups with the use of questionnaires and self-reported symptoms collected via a dedicated mobile app provide valuable insights that might enable a prompt identification of worsening health conditions. To this end, in this paper we propose a real-time exacerbation detection algorithm based on a paper version of a multimorbid symptom diary (the COPE-III study) developed in the scope of the RE-SAMPLE project. Results on its implementation and use in three hospitals across different countries in Europe are reported along with a discussion on its potential and challenges. Finally, we demonstrate that it detects exacerbation events that are associated with 46% of the emergency accesses and 32% of hospitalizations reported at GEM pilot site.