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Smartphone-enabled detection of COugh in COvid-19 (COCO) – preliminary analysis of an exploratory, observational cohort study

Frank Rassouli, Maximilian Boesch, Florent Baty, Peter Tinschert, Filipe Barata, Iris Shih, David Cleres, Elgar Fleisch, Martin H. Brutsche
European Respiratory Journal 2021 58: PA3865; DOI: 10.1183/13993003.congress-2021.PA3865
Frank Rassouli
1Lung center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
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  • For correspondence: frank.rassouli@kssg.ch
Maximilian Boesch
1Lung center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
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Florent Baty
1Lung center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
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Peter Tinschert
2Resmonics AG, Zurich, Switzerland
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Filipe Barata
3Department of Management, Technology, and Economy, ETH Zurich, Zurich, Switzerland
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Iris Shih
2Resmonics AG, Zurich, Switzerland
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David Cleres
3Department of Management, Technology, and Economy, ETH Zurich, Zurich, Switzerland
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Elgar Fleisch
3Department of Management, Technology, and Economy, ETH Zurich, Zurich, Switzerland
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Martin H. Brutsche
1Lung center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
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Abstract

Introduction: COVID-19 mainly manifests as a respiratory disease, and cough is a major symptom. Age and certain comorbidities are recognized risk factors for severe disease and hospitalization. Mobile technology could help to more precisely predict the course of disease.

Aims and objectives: To detect cough frequencies in hospitalized patients with COVID-19 and non-COVID-19 pneumonia and correlate these data to a variety of clinical parameters.

Methods: Smartphone-enabled detection of coughs technically based on a convolutional neural network-based model was used in 33 patients with COVID-19 and 12 patients with non-COVID-19 pneumonia in a non-ICU setting. Clinical data were extracted from medical records and correlated to cough frequencies.

Results: The technology reliably detected coughing events in all COVID-19 and non-COVID-19 patients over extended periods of time. In contrast to non-COVID-19, significant positive correlations between hourly cough counts and blood ferritin levels, FiO2, and breathing rate were found in COVID-19 pneumonia (Figure 1), and hourly cough counts decreased significantly with hospitalization length.

Conclusions: Automated, smartphone-based quantification of cough is feasible in an in-patient setting. Cough counts correlated with surrogate markers of COVID-19 disease activity and decreased towards hospital discharge. Although a low sample size limits the generalizability of our study, results are encouraging and warrant further investigation of cough as a COVID-19 digital biomarker.

  • Covid-19
  • Cough
  • Monitoring

Footnotes

Cite this article as: European Respiratory Journal 2021; 58: Suppl. 65, PA3865.

This abstract was presented at the 2021 ERS International Congress, in session “Prediction of exacerbations in patients with COPD”.

This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).

  • Copyright ©the authors 2021
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Smartphone-enabled detection of COugh in COvid-19 (COCO) – preliminary analysis of an exploratory, observational cohort study
Frank Rassouli, Maximilian Boesch, Florent Baty, Peter Tinschert, Filipe Barata, Iris Shih, David Cleres, Elgar Fleisch, Martin H. Brutsche
European Respiratory Journal Sep 2021, 58 (suppl 65) PA3865; DOI: 10.1183/13993003.congress-2021.PA3865

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Smartphone-enabled detection of COugh in COvid-19 (COCO) – preliminary analysis of an exploratory, observational cohort study
Frank Rassouli, Maximilian Boesch, Florent Baty, Peter Tinschert, Filipe Barata, Iris Shih, David Cleres, Elgar Fleisch, Martin H. Brutsche
European Respiratory Journal Sep 2021, 58 (suppl 65) PA3865; DOI: 10.1183/13993003.congress-2021.PA3865
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