Sudre, CH;
Lee, KA;
Lochlainn, MN;
Varsavsky, T;
Murray, B;
Graham, MS;
Menni, C;
... Ourselin, S; + view all
(2021)
Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.
Science Advances
, 7
(12)
, Article eabd4177. 10.1126/sciadv.abd4177.
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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.
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