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

Sudre, Carole H. ; Lee, Karla A. ; Lochlainn, Mary Ni ; Varsavsky, Thomas ; Murray, Benjamin ; Graham, Mark S. ; Menni, Cristina ; Modat, Marc ; Bowyer, Ruth C.E. and Nguyen, Long H. , et al. (2021) In Science Advances 7(12).
Abstract

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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Science Advances
volume
7
issue
12
publisher
American Association for the Advancement of Science (AAAS)
external identifiers
  • pmid:33741586
  • scopus:85103231092
ISSN
2375-2548
DOI
10.1126/sciadv.abd4177
language
English
LU publication?
yes
id
6adce3d8-26e9-4e97-927e-0e58d97348df
date added to LUP
2021-04-08 09:33:57
date last changed
2025-01-27 10:06:41
@article{6adce3d8-26e9-4e97-927e-0e58d97348df,
  abstract     = {{<p>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.</p>}},
  author       = {{Sudre, Carole H. and Lee, Karla A. and Lochlainn, Mary Ni and Varsavsky, Thomas and Murray, Benjamin and Graham, Mark S. and Menni, Cristina and Modat, Marc and Bowyer, Ruth C.E. and Nguyen, Long H. and Drew, David A. and Joshi, Amit D. and Ma, Wenjie and Guo, Chuan Guo and Lo, Chun Han and Ganesh, Sajaysurya and Buwe, Abubakar and Pujol, Joan Capdevila and du Cadet, Julien Lavigne and Visconti, Alessia and Freidin, Maxim B. and El-Sayed Moustafa, Julia S. and Falchi, Mario and Davies, Richard and Gomez, Maria F. and Fall, Tove and Cardoso, M. Jorge and Wolf, Jonathan and Franks, Paul W. and Chan, Andrew T. and Spector, Tim D. and Steves, Claire J. and Ourselin, Sébastien}},
  issn         = {{2375-2548}},
  language     = {{eng}},
  number       = {{12}},
  publisher    = {{American Association for the Advancement of Science (AAAS)}},
  series       = {{Science Advances}},
  title        = {{Symptom clusters in COVID-19 : A potential clinical prediction tool from the COVID Symptom Study app}},
  url          = {{http://dx.doi.org/10.1126/sciadv.abd4177}},
  doi          = {{10.1126/sciadv.abd4177}},
  volume       = {{7}},
  year         = {{2021}},
}