Prognosis of patients with post-Covid-19 condition: Prospective cohort cluster analysis at one year

Objective: We aimed to identify clinically relevant clusters among patients with post-Covid-19 condition (PCC) and assess prognosis overall and within clusters. Methods: Prospective cohort study of patients with PCC attending a rehabilitation clinic. We monitored patient reported outcome measures (PROMs): EuroHIS quality of life and symptoms. Unsupervised hierarchical cluster analyses were performed to identify clusters of patients with different quantity of symptoms, and symptoms presenting together. Preliminary findings on symptom prevalence and quality of life at 12 months are reported. Results: Among 409 patients, 70.4% were women, with an average baseline of 20.3 (SD 6.8) symptoms. Three clusters emerged based on symptom quantity, labelled by the average number of symptoms at baseline: Cluster11 (17% of all patients), Cluster-17 (35%), and Cluster-25 (48%). Multinomial logistic regression showed female sex, multiple comorbidities predicting more symptoms. Four symptom-based clusters were defined: fatigue and cognitive complaints; pain, trouble sleeping, palpitations and other symptoms; gastrointestinal symptoms; and emotion-related symptoms. Linear regression models showed that female sex, multiple comorbidities, anxiety, use of antidepressants, BMI and smoking were among the determinants of symptom clusters. In 12-month followup, symptom count decreased, and quality of life improved across all clusters, with 9% having good quality of life at baseline and 33% at 12 months. Conclusion: Four patient clusters based on symptoms were identified in the PCC cohort. Prognosis was favorable across all clusters, with symptom reduction and improved quality of life observed. Female sex, comorbidities, BMI, and mental-health related variables predicted higher symptom burden, suggesting multifactorial origins of PCC.