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

Sudre, C. H.; Lee, K.; Ni Lochlainn, M.; Varsavsky, T.; Murray, B.; Graham, M. S.; Menni, C.; Modat, M.; Bowyer, R. C. E.; Nguyen, L. H.; Drew, D. A.; Joshi, A. D.; Ma, W.; Guo, C. G.; Lo, C. H.; Ganesh, S.; Buwe, A.; Capdevila Pujol, J.; Lavigne du Cadet, J.; Visconti, A.; Freydin, M.; El Sayed Moustafa, J. S.; Falchi, M.; Davies, R.; Gomez, M. F.; Fall, T.; Cardoso, M. J.; Wolf, J.; Franks, P. W.; Chan, A. T.; Spector, T. D.; Steves, C. J.; Ourselin, S.

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.