Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app
Mary Ni Lochlainn,
Mark S Graham,
Ruth C.E. Bowyer,
David Alden Drew,
Amit D. Joshi,
Chuan Guo Guo,
Chun Han Lo,
Joan Capdevila Pujol,
Julien Lavigne du Cadet,
Julia S. El Sayed Moustafa,
Maria F Gomez,
M. Jorge Cardoso,
Paul W Franks,
Andrew T. Chan,
Timothy D. Spector,
Posted 16 Jun 2020
medRxiv DOI: 10.1101/2020.06.12.20129056
Posted 16 Jun 2020
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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