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

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 ViscontiMaxim 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

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)

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 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.

Original languageEnglish
Article numbereabd4177
JournalScience Advances
Volume7
Issue number12
DOIs
Publication statusPublished - 19 Mar 2021

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