Identification of acute exacerbations of chronic obstructive pulmonary disease using simple patient-reported symptoms and cough feature analysis: A diagnostic agreement study

Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) are commonly encountered in the primary care setting, though accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9-89.9%) of subjects (n=86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4-96.3%) of individuals (n=78). Diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0-87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.