Symptom clusters in Long COVID in the UK: prospective community-based cohort study using unsupervised machine learning

Abstract

Long COVID is a condition usually defined by persisting symptoms following infection by the SARS-CoV-2 virus beyond the acute phase of infection. The condition has a significant impact on healthcare systems, the economy, and the individuals living with it. Due to the diverse and extensive symptomatology of long COVID, symptom co-occurrence tracking can be used to capture patient experiences and improve understanding, diagnosis, and management. Here, we leverage the UK Office for National Statistics (ONS) COVID-19 Infection Survey (CIS). The CIS was run between April 2020 and March 2023. On February 3, 2021, the ONS launched a new CIS question evaluating self-reported symptom persistence of 23 long COVID symptoms post self-reported COVID-19 infection. We use Jaccard symptom-by symptom distance matrices, derived from the binary survey responses of the presence of each symptom. Three methods are then used to visualise symptom co-occurrence: heatmaps, non-metric multidimensional scaling, and agglomerative hierarchical clustering with complete linkage. We split our analysis into two parts, first looking at all long COVID survey responses (n = 207,319) and then looking at responses stratified by time-since-onset of long COVID up to 24 months (n = 30,224 at zero months-since-onset). We find higher symptom co-occurrence in prolonged long COVID. We also find clusters of neurological/systemic symptoms, gastrointestinal symptoms, and respiratory symptoms, with separability in symptom co-occurrence by organ system becoming less pronounced as time-since-onset of long COVID increases. Shortness of breath, weakness/tiredness, and muscle ache present as core symptoms long COVID. We demonstrate that the symptom experience of long COVID evolves from early stages to late stages with higher symptom burden and multi-systemic presentation. We also find three possible phenotypes of early long COVID.