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Saved Stories – None: Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app | medRxiv

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Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app

Carole H Sudre, Karla Lee, Mary Ni Lochlainn, Thomas Varsavsky, Benjamin Murray, Mark S. Graham, Cristina Menni, Marc Modat, Ruth C.E. Bowyer, Long H Nguyen, David Alden 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 Freydin, 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, Timothy D Spector, Claire J Steves, Sebastien Ourselin

Abstract

As no one symptom can predict disease severity or the need for dedicated medical support in COVID-19, we asked if 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 May 1- May 28th, 2020. Using the first 5 days of symptom logging, the ROC-AUC 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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