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Published on: December 19, 2020
Precision Symptom Phenotyping Identifies Early Clinical and Proteomic Predictors of Distinct COVID-19 Sequelae
Nusrat J Epsi1,2, Josh G Chenoweth2, Paul W Blair2,3
1Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.
Background:
Post-COVID conditions (PCC) are difficult to characterize, diagnose, predict, and treat due to overlapping symptoms and poorly understood pathology. Identifying inflammatory profiles may improve clinical prognostication and trial endpoints.
Methods:
This analysis included 1988 SARS-CoV-2 positive U.S. Military Health System beneficiaries who had quantitative post-COVID symptom scores. Among participants who reported moderate-to-severe symptoms on surveys collected 6 months post-SARS-CoV-2 infection, principal component analysis followed by k-means clustering identified distinct clusters of symptoms.
Results:
Three symptom-based clusters were identified: a sensory cluster (loss of smell and/or taste), a fatigue/difficulty thinking cluster, and a difficulty breathing/exercise intolerance cluster. Individuals within the sensory cluster were all outpatients during their initial COVID-19 presentation. The difficulty breathing cluster had a higher likelihood of obesity and COVID-19 hospitalization than those with no/mild symptoms at 6 months post-infection. Multinomial regression linked early post-infection D-dimer and IL-1RA elevation to fatigue/difficulty thinking and elevated ICAM-1 concentrations to sensory symptoms.
Conclusions:
We identified three distinct symptom-based PCC phenotypes with specific clinical risk factors and early post-infection inflammatory predictors. With further validation and characterization, this framework may allow more precise classification of PCC cases and potentially improve the diagnosis, prognostication, and treatment of PCC.
