The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Maria Gomez

Maria Gomez

Professor

Maria Gomez

Symptom clusters in COVID-19 : A potential clinical prediction tool from the COVID Symptom Study app

Author

  • Carole H. Sudre
  • Karla A. Lee
  • Mary Ni Lochlainn
  • Thomas Varsavsky
  • Benjamin Murray
  • Mark S. Graham
  • Cristina Menni
  • Marc Modat
  • Ruth C.E. Bowyer
  • Long H. Nguyen
  • David A. Drew
  • Amit D. Joshi
  • Wenjie Ma
  • Chuan Guo Guo
  • Chun Han Lo
  • Sajaysurya Ganesh
  • Abubakar Buwe
  • Joan Capdevila Pujol
  • Julien Lavigne du Cadet
  • Alessia Visconti
  • Maxim B. Freidin
  • Julia S. El-Sayed Moustafa
  • Mario Falchi
  • Richard Davies
  • Maria F. Gomez
  • Tove Fall
  • M. Jorge Cardoso
  • Jonathan Wolf
  • Paul W. Franks
  • Andrew T. Chan
  • Tim D. Spector
  • Claire J. Steves
  • Sébastien Ourselin

Summary, in English

As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic - area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.

Department/s

  • Diabetic Complications
  • EXODIAB: Excellence of Diabetes Research in Sweden
  • Genomics, Diabetes and Endocrinology
  • Genetic and Molecular Epidemiology
  • EpiHealth: Epidemiology for Health

Publishing year

2021

Language

English

Publication/Series

Science Advances

Volume

7

Issue

12

Document type

Journal article

Publisher

American Association for the Advancement of Science (AAAS)

Topic

  • Infectious Medicine

Status

Published

Research group

  • Diabetic Complications
  • Genomics, Diabetes and Endocrinology
  • Genetic and Molecular Epidemiology

ISBN/ISSN/Other

  • ISSN: 2375-2548