@article{6f941fb482ba4c0e9d27a952e0032b3c,
title = "Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app",
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.",
author = "Sudre, {Carole H} and Lee, {Karla A} and Lochlainn, {Mary Ni} and Thomas Varsavsky and Benjamin Murray and Graham, {Mark S} and Cristina Menni and Marc Modat and Bowyer, {Ruth C E} and Nguyen, {Long H} and Drew, {David A} and Joshi, {Amit D} and Wenjie Ma and Chuan-Guo Guo and Chun-Han Lo and Sajaysurya Ganesh and Abubakar Buwe and Pujol, {Joan Capdevila} and {du Cadet}, {Julien Lavigne} and Alessia Visconti and Freidin, {Maxim B} and {El-Sayed Moustafa}, {Julia S} and Mario Falchi and Richard Davies and Gomez, {Maria F} and Tove Fall and Cardoso, {M Jorge} and Jonathan Wolf and Franks, {Paul W} and Chan, {Andrew T} and Spector, {Tim D} and Steves, {Claire J} and S{\'e}bastien Ourselin",
note = "Funding Information: Zoe provided support for all aspects of building, running, and supporting the app and service to all users worldwide. Support for this study was provided by the NIHR-funded Biomedical Research Centre based at GSTT NHS Foundation Trust. This work was supported by the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare. Investigators also received support from the Wellcome Trust, the MRC/BHF, Alzheimer's Society, EU, NIHR, CDRF, and the NIHR-funded BioResource, Clinical Research Facility and BRC based at GSTT NHS Foundation Trust in partnership with KCL. A.T.C. was supported in this work through a Stuart and Suzanne Steele MGH Research Scholar Award. C.M. is funded by the Chronic Disease Research Foundation and by the MRC AimHy project grant. L.H.N., D.A.D., A.D.J., A.T.C., C.G., and W.M. are supported by the Massachusetts Consortium on Pathogen Readiness (MassCPR) and Mark and Lisa Schwartz. The work performed on the Swedish study is supported by grants from the Swedish Research Council, Swedish Heart-Lung Foundation, and the Swedish Foundation for Strategic Research (LUDC-IRC 15-0067). Publisher Copyright: Copyright {\textcopyright} 2021 The Authors, some rights reserved.",
year = "2021",
month = mar,
day = "19",
doi = "10.1126/sciadv.abd4177",
language = "English",
volume = "7",
journal = "Science Advances",
issn = "2375-2548",
publisher = "American Association for the Advancement of Science",
number = "12",
}