Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.
Sci Adv
; 7(12)2021 03.
Article
in English
| MEDLINE | ID: covidwho-1142980
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
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.
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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