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Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.
Sudre, Carole H; Lee, Karla A; Lochlainn, Mary Ni; Varsavsky, Thomas; Murray, Benjamin; Graham, Mark S; Menni, Cristina; Modat, Marc; Bowyer, Ruth C E; Nguyen, Long H; Drew, David A; Joshi, Amit D; Ma, Wenjie; Guo, Chuan-Guo; Lo, Chun-Han; Ganesh, Sajaysurya; Buwe, Abubakar; Pujol, Joan Capdevila; du Cadet, Julien Lavigne; Visconti, Alessia; Freidin, Maxim B; El-Sayed Moustafa, Julia S; Falchi, Mario; Davies, Richard; Gomez, Maria F; Fall, Tove; Cardoso, M Jorge; Wolf, Jonathan; Franks, Paul W; Chan, Andrew T; Spector, Tim D; Steves, Claire J; Ourselin, Sébastien.
  • Sudre CH; School of Biomedical Engineering & Imaging Sciences, King's College London, Westminster Bridge Road, London SE17EH, UK. carole.sudre@kcl.ac.uk sebastien.ourselin@kcl.ac.uk.
  • Lee KA; MRC Unit for Lifelong Health and Ageing at UCL, University College London, London WC1E 7BH, UK.
  • Lochlainn MN; Centre for Medical Image Computing, Department of Computer Science, University College London, London UK.
  • Varsavsky T; Department of Twin Research and Genetic Epidemiology King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Murray B; Department of Twin Research and Genetic Epidemiology King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Graham MS; School of Biomedical Engineering & Imaging Sciences, King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Menni C; School of Biomedical Engineering & Imaging Sciences, King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Modat M; School of Biomedical Engineering & Imaging Sciences, King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Bowyer RCE; Department of Twin Research and Genetic Epidemiology King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Nguyen LH; School of Biomedical Engineering & Imaging Sciences, King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Drew DA; Department of Twin Research and Genetic Epidemiology King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Joshi AD; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
  • Ma W; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
  • Guo CG; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
  • Lo CH; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
  • Ganesh S; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
  • Buwe A; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
  • Pujol JC; Zoe Global Limited, 164 Westminster Bridge Road, London SE1 7RW, UK.
  • du Cadet JL; Zoe Global Limited, 164 Westminster Bridge Road, London SE1 7RW, UK.
  • Visconti A; Zoe Global Limited, 164 Westminster Bridge Road, London SE1 7RW, UK.
  • Freidin MB; Zoe Global Limited, 164 Westminster Bridge Road, London SE1 7RW, UK.
  • El-Sayed Moustafa JS; Department of Twin Research and Genetic Epidemiology King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Falchi M; Department of Twin Research and Genetic Epidemiology King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Davies R; Department of Twin Research and Genetic Epidemiology King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Gomez MF; Department of Twin Research and Genetic Epidemiology King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Fall T; Zoe Global Limited, 164 Westminster Bridge Road, London SE1 7RW, UK.
  • Cardoso MJ; Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden.
  • Wolf J; Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden.
  • Franks PW; School of Biomedical Engineering & Imaging Sciences, King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Chan AT; Zoe Global Limited, 164 Westminster Bridge Road, London SE1 7RW, UK.
  • Spector TD; Department of Twin Research and Genetic Epidemiology King's College London, Westminster Bridge Road, London SE17EH, UK.
  • Steves CJ; Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden.
  • Ourselin S; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
Sci Adv ; 7(12)2021 03.
Article in English | MEDLINE | ID: covidwho-1142980
Preprint
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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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Diagnosis, Computer-Assisted / Mobile Applications / SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Adult / Female / Humans / Male / Middle aged Language: English Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Diagnosis, Computer-Assisted / Mobile Applications / SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Adult / Female / Humans / Male / Middle aged Language: English Year: 2021 Document Type: Article