BACKGROUND: Community-acquired pneumonia (CAP) is an essential consideration in patients presenting to primary care with respiratory symptoms; however, accurate diagnosis is difficult when clinical and radiological examinations are not possible, such as during telehealth consultations .

AIM: To develop and test a smartphone-based algorithm for diagnosing CAP without need for clinical examination or radiological inputs .

DESIGN AND

SETTING: A prospective cohort study using data from participants aged> 12 years presenting with acute respiratory symptoms to a hospital in Western Australia.

METHOD: Five cough audio-segments were recorded and four patient-reported symptoms (fever, acute cough, productive cough, and age) were analysed by the smartphone-based algorithm to generate an immediate diagnostic output for CAP . Independent cohorts were recruited to train and test the accuracy of the algorithm . Diagnostic agreement was calculated against the confirmed discharge diagnosis of CAP by specialist physicians . Specialist radiologists reported medical imaging .

RESULTS: The smartphone-based algorithm had high percentage agreement (PA) with the clinical diagnosis of CAP in the total cohort (n = 322, positive PA [PPA] = 86.2%, negative PA [NPA] = 86.5%, area under the receiver operating characteristic curve [AUC] = 0.95); in participants 22- <65 years (n = 192, PPA = 85.7%, NPA = 87.0%, AUC = 0.94), and in participants aged ≥65 years (n = 86, PPA = 85.7%, NPA = 87.5%, AUC = 0.94). Agreement was preserved across CAP severity : 85.1% (n = 80/94) of participants with CRB-65 scores 1 or 2, and 87.7% (n = 57/65) with a score of 0, were correctly diagnosed by the algorithm .

CONCLUSION: The algorithm provides rapid and accurate diagnosis of CAP . It offers improved accuracy over current protocols when clinical evaluation is difficult . It provides increased capabilities for primary and acute care, including telehealth services, required during the COVID-19 pandemic.