Purpose: Recent developments in the field of artificial intelligence and acoustics have made it possible to objectively monitor cough in clinical and ambulatory settings. We hypothesized that time patterns of objectively measured cough in COVID-19 patients could predict clinical prognosis and help rapidly identify patients at high risk of intubation or death.
Methods: One hundred and twenty-three patients hospitalized with COVID-19 were enrolled at University of Florida Health Shands and the Centre Hospitalier de l'Université de Montréal. Patients' cough was continuously monitored digitally along with clinical severity of disease until hospital discharge, intubation, or death. The natural history of cough in hospitalized COVID-19 disease was described and logistic models fitted on cough time patterns were used to predict clinical outcomes.
Results: In both cohorts, higher early coughing rates were associated with more favorable clinical outcomes. The transitional cough rate, or maximum cough per hour rate predicting unfavorable outcomes, was 3·40 and the AUC for cough frequency as a predictor of unfavorable outcomes was 0·761. The initial 6 h (0·792) and 24 h (0·719) post-enrolment observation periods confirmed this association and showed similar predictive value.
Interpretation: Digital cough monitoring could be used as a prognosis biomarker to predict unfavorable clinical outcomes in COVID-19 disease. With early sampling periods showing good predictive value, this digital biomarker could be combined with clinical and paraclinical evaluation and is well adapted for triaging patients in overwhelmed or resources-limited health programs.
Keywords: Artificial intelligence; Clinical decision making; Cough; Covid-19; Machine learning.
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