| Title: |
Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app |
| Authors: |
Sudre, CH; Lee, KA; Lochlainn, MN; Varsavsky, T; Murray, B; Graham, MS; Menni, C; Modat, M; Bowyer, RCE; Nguyen, LH; Drew, DA; Joshi, AD; Ma, W; Guo, C-G; Lo, C-H; Ganesh, S; Buwe, A; Pujol, JC; du Cadet, JL; Visconti, A; Freidin, MB; El-Sayed Moustafa, JS; Falchi, M; Davies, R; Gomez, MF; Fall, T; Cardoso, MJ; Wolf, J; Franks, PW; Chan, AT; Spector, TD; Steves, CJ; Ourselin, S |
| Source: |
Science Advances , 7 (12) , Article eabd4177. (2021) |
| Publication Year: |
2021 |
| Collection: |
University College London: UCL Discovery |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://discovery.ucl.ac.uk/id/eprint/10125064/1/eabd4177.full.pdf; https://discovery.ucl.ac.uk/id/eprint/10125064/ |
| Availability: |
https://discovery.ucl.ac.uk/id/eprint/10125064/1/eabd4177.full.pdf; https://discovery.ucl.ac.uk/id/eprint/10125064/ |
| Rights: |
open |
| Accession Number: |
edsbas.89A8A4C3 |
| Database: |
BASE |