Abstract
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the 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, and 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). The 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.
Highlights
Chronic obstructive pulmonary disease (COPD) is a common respiratory condition worldwide and is increasing in prevalence[1]
The software algorithm demonstrated high diagnostic agreement with the clinical diagnosis (Table 2): positive percent agreement (PPA) was 82.6% and Negative percent agreement (NPA) was 91.0%
We evaluated the performance of the algorithm by the acute exacerbation of COPD (AECOPD) severity category assigned using the CRB-65 criteria
Summary
Chronic obstructive pulmonary disease (COPD) is a common respiratory condition worldwide and is increasing in prevalence[1]. It is characterized by persistent respiratory symptoms due to airflow and/or alveolar abnormalities usually caused by significant exposure to noxious particles or gases[2]. COPD represents a major cause of health care utilization and expense, and healthcare costs rise with each instance of AECOPD a patient experiences. A large study in the US, demonstrated a significant increase in all costs for patients with two or more exacerbations compared with the overall population of COPD patients, predominantly due to an increase in hospitalization[4]. The early identification and prevention of AECOPD such that patients no longer require hospitalization represent a critical juncture in developing a cost-effective disease management strategy
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