Background

Six months after SARS-CoV-2  infection, 31-69% of COVID-19 patients still experience complaints. Long COVID is a heterogeneous disease and finding subtypes could aid in patient recovery.

Methods

Data was collected from the 95 patients of the P4O2 COVID-19 cohort at 3 to 6 months after infection. Patients were clustered using data obtained from patient characteristics, characteristics from acute SARS-CoV-2 infection, long COVID symptom data, lung function and questionnaires describing the impact and severity of long COVID. Patients were then clustered using hierarchal clustering.

Results

Four distinct clusters of patients with long COVID were revealed. These clusters differed in severity, patient characteristics and lung function. Cluster 1 (39%) represented the average patients with no significant differences compared to other clusters. Cluster 2 (28%) represented the more severe cluster, was predominantly female (82%) with a mild acute COVID-19. Cluster 3 (15%) was predominantly male (71%) with respiratory complaints and had a 20% lower lung function. Cluster 4 (18%) contained mild long COVID patients, was predominantly male (88%) and showed a more severe acute COVID-19.

Conclusions

Patients with long COVID can be clustered into four distinct phenotypes based on their clinical presentation and easily obtainable information. These clusters show distinction in patient characteristics, long COVID severity and acute COVID-19 characteristics. This clustering can help in selecting the most beneficial treatment, while follow-up research using omics techniques could reveal molecular mechanisms implicated in the different phenotypes.