Post COVID-19 condition phenotypes: A prospective cohort study identifying four symptom clusters and their impact on...

Current evidence indicates that Post COVID-19 Condition (PCC) is multifaceted with distinct phenotypes. While previous studies have identified symptom clusters-commonly featuring fatigue, respiratory symptoms, and cognitive impairment-findings have been inconsistent, and no clear consensus exists. Moreover, how these symptom clusters evolve over time, particularly beyond the first year post-infection, remains poorly understood.

This multicentre prospective cohort study included 470 hospitalised and non-hospitalised adult individuals from the CoVUm study across four sites in Sweden between 2020 and 2021. Follow-ups were conducted up to 3 years after infection to assess persistent symptoms, health-related quality of life (HRQoL), and work capacity. Symptom clusters at 6 months were identified via hierarchical cluster analysis, and participants were tracked using a k-nearest neighbour algorithm.

The most common symptoms at 6 months were fatigue (33 %), dyspnoea (32 %), mental fatigue (30 %), and concentration difficulties (28 %), with a median EQ-5D-5L index of 0.98 (IQR 0.93-1). Four distinct symptom clusters were identified: (i) "Few Symptoms" (n = 265, 57 %), (ii) "Respiratory Symptoms" (n = 66, 14 %), (iii) "Neurocognitive Symptoms" (n = 75, 16 %), and (iv) "Multisystem Symptoms" (n = 52, 11 %). Participants in the latter three clusters were older, had more comorbidities, and were more often hospitalised during primary COVID-19 infection. These clusters also had significantly lower HRQoL compared to the "Few Symptoms" cluster. Over time, more than half of participants transitioned to a cluster with fewer or no symptoms, with significant perceived HRQoL improvement in the "Multisystem Symptoms" cluster.

While many patients with PCC improved over time, a subset had persistent symptoms at 3 years, especially if primary infection required hospitalisation. The identification of symptom clusters and their trajectories over time contributes to a better understanding of PCC heterogeneity, ultimately bringing the field closer to consensus on the classification and long-term impact of PCC.