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

2020; Cold Spring Harbor Laboratory; Linguagem: Inglês

10.1101/2020.06.12.20129056

Carole H. Sudre, Karla A. Lee, Mary Ní 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,

Clinical Reasoning and Diagnostic Skills

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 28 th , 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. One sentence summary Longitudinal clustering of symptoms can predict the need for respiratory support in severe COVID-19.