Rapid, point of care detection of Chronic Obstructive Pulmonary Disease using a cough-centred algorithm in acute care settings.

Porter, P.; Brisbane, J.; Abeyratne, U.; Bear, N.; Wood, J.; Peltonen, V.; Della, P.; Purdie, F.; Smith, C.; Claxton, S.

Rapid and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD) is problematic in acute-care settings, particularly in the presence of infective comorbidities. The aim of this study was to develop a rapid, smartphone-based algorithm for the detection of COPD, in the presence or absence of acute respiratory infection, and then evaluate diagnostic accuracy on an independent validation set. Subjects aged 40-75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis or emphysema, were recruited into the study. The algorithm analysed five cough sounds and four patient-reported clinical symptoms providing a diagnosis in less than one minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. The algorithm demonstrated high percent agreement (PA) with reference clinical diagnosis for COPD in the total cohort (n=252, Positive PA=93.8%, Negative PA=77.0%, AUC=0.95); in subjects with pneumonia or infective exacerbations of COPD (n=117, PPA=86.7%, NPA=80.5%, AUC=0.93) and in subjects without an infective comorbidity (n=135, PPA=100.0%, NPA=74.0%, AUC=0.97.) In those who had their COPD confirmed by spirometry (n=229), PPA = 100.0% and NPA = 77.0%, AUC=0.97. The algorithm demonstrates high agreement with clinical diagnosis and rapidly detects COPD in subjects presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens and identify those at increased risk of mortality due to seasonal or other respiratory ailments.