Precision symptom phenotyping identifies early clinical and proteomic predictors of distinct COVID-19 sequelae.
J Infect Dis
; 2024 Jun 25.
Article
em En
| MEDLINE
| ID: mdl-38916431
ABSTRACT
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:
1,988 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative post-COVID symptom scores were included in this analysis. Among participants who reported moderate-to-severe symptoms on surveys collected 6-months post-SARS-CoV-2 infection, principal component analysis (PCA) 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 compared to 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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
J Infect Dis
Ano de publicação:
2024
Tipo de documento:
Article