Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app - Carole H. Sudre; Karla A. Lee; Mary Ní Lochlainn; Thomas Varsavsky; Benjamin Murray; Mark S. Graham; Cristina Menni; Marc Modat; Ruth C. E. Bowyer; Long H. Nguyen; David A. Drew; Amit D. Joshi; et al. (2020)
Abstract As no one symptom can predict disease severity or the need for dedicated medical support in COVID-19, we asked if 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 May 1-May 28 th , 2020. Using the first 5 days of symptom logging, the ROC-AUC 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. One sentence summary Longitudinal clustering of symptoms can predict the need for respiratory support in severe COVID-19.