Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app

Sci Adv. 2021 Mar 19;7(12):eabd4177. doi: 10.1126/sciadv.abd4177. Print 2021 Mar.

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.

PMID:33741586 | DOI:10.1126/sciadv.abd4177

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