Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID symptom study app
Articolo
Data di Pubblicazione:
2021
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.
Tipologia CRIS:
03A-Articolo su Rivista
Elenco autori:
Sudre C.H.; Lee K.A.; Lochlainn M.N.; 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.; Pujol J.C.; Du Cadet J.L.; Visconti A.; Freidin M.B.; 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.
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