Unveiling Post-COVID-19 syndrome: incidence, biomarkers, and clinical phenotypes in a Thai population

Abstract

BackgroundPost-COVID- 19 syndrome (PCS) significantly impacts the quality of life of survivors. There is, however, a lack of a standardized approach to PCS diagnosis and management. Our bidirectional cohort study aimed to estimate PCS incidence, identify risk factors and biomarkers, and classify clinical phenotypes for enhanced management to improve patient outcomes.MethodsA bidirectional prospective cohort study was conducted at five medical sites in Hatyai district in Songkhla Province, Thailand. Participants were randomly selected from among the survivors of COVID-19 aged≥18 years between May 15, 2022, and January 31, 2023. The selected participants underwent a scheduled outpatient visit for symptom and health assessments 12 to 16 weeks after the acute onset of infection, during which PCS was diagnosed and blood samples were collected for hematological, inflammatory, and serological tests. PCS was defined according to the World Health Organization criteria. Univariate and multiple logistic regression analyses were used to identify biomarkers associated with PCS. Moreover, three clustering methods (agglomerative hierarchical, divisive hierarchical, and K-means clustering) were applied, and internal validation metrics were used to determine clustering and similarities in phenotypes.FindingsA total of 300 survivors were enrolled in the study, 47% of whom developed PCS according to the World Health Organization (WHO) definition. In the sampled cohort, 66.3% were females, and 79.4% of them developed PCS (as compared to 54.7% of males, p-value <0.001). Comorbidities were present in 19% (57/300) of all patients, with 11% (18/159) in the group without PCS and 27.7% (39/141) in the group with PCS. The incidence of PCS varied depending on the criteria used and reached 13% when a quality of life indicator was added to the WHO definition. Common PCS symptoms were hair loss (22%) and fatigue (21%), while mental health symptoms were less frequent (insomnia 3%, depression 3%, anxiety 2%). According to our univariate analysis, we found significantly lower hematocrit and IgG levels and greater ALP levels in PCS patients than in patients who did not develop PCS (p-value< 0.05). According to our multivariable analysis, adjusted ALP levels remained a significant predictor of PCS (OR 1.02, p-value= 0.005). Clustering analysis revealed four groups characterized by severe clinical symptoms and mental health concerns (Cluster 1, 4%), moderate physical symptoms with predominant mental health issues (Cluster 2, 9%), moderate mental health issues with predominant physical symptoms (Cluster 3, 14%), and mild to no PCS (Cluster 4, 77%). The quality of life and ALP levels varied across the clusters.InterpretationThis study challenges the prevailing diagnostic criteria for PCS, emphasizing the need for a holistic approach that considers quality of life. The identification of ALP as a biomarker associated with PCS suggests that its monitoring could be used for early detection of the onset of PCS. Cluster analysis revealed four distinct clinical phenotypes characterized by different clinical symptoms and mental health concerns that 'exhibited varying impacts on quality of life. This finding suggested that accounting for the reduced quality of life in the definition of PCS could enhance its diagnosis and management and that moving toward personalized interventions could both improve patient outcomes and help reduce medicalization and optimally target the available resources.FundingThe research publication received funding support from Medical Council of Thailand (Police General Dr. Jongjate Aojanepong Foundation), Hatyai Hospital Charity and Wellcome Trust.