Beyond Random Variation: Monte Carlo Analysis and Predictability of COPD Exacerbations

<title>Extract</title> Exacerbations of chronic obstructive pulmonary disease (COPD) are associated with high symptom burden and long-term consequences on disease progression [1, 2]. These events are currently classified as severe when they result in emergency room visits or hospitalizations, and as moderate when they are treated out of hospital. Several therapies exist, and many novel treatments are being investigated, for the prevention of these events. Decisions regarding treatment change are made in clinical practice based on the risk of exacerbations over the next 6–12 months [3]. The predictability of exacerbations is, therefore, important for making treatment decisions regarding initiating or escalating therapy. The strongest clinical risk factor for future exacerbations is a history of previous exacerbations [4, 5]. Exacerbation frequency in individual patients, however, fluctuates year-to-year [6, 7], and prior exacerbation history is associated with a modest performance for predicting future events with c-indices ranging from 0.59 to 0.65 [8]. In addition to external exposures, which may be variable, underlying disease severity may be associated with exacerbation frequency, especially for more severe events. Continued efforts are being made to more accurately predict exacerbations. There remains, however, a lack of data on whether moderate and severe events are both predictable. If one or both exacerbation types are due to random chance, then efforts to predict them will be unsuccessful. We aimed to evaluate whether exacerbations, moderate and severe, are truly predictable.