Risk tools for predicting long-term sequelae based on symptom profiles after known and undetected SARS-CoV-2 infections in the population.

ABSTRACT: The aim was to determine the profile of long-term symptoms after known and undetected SARS-CoV-2 infections and to generate tools for risk and diagnostic assessment of Post-COVID syndrome (PCS). In the population-based Gutenberg COVID-19 Study (N = 10,250), sequential, systematic screening for SARS-CoV-2 was performed in 2020/2021. Individuals received a standardized interview on newly occurred or worsened symptoms since the infection or the pandemic. Robust Poisson regression models were fit to compare the frequency of symptoms between groups. Two scores were developed using machine learning techniques and prospectively validated in an independent cohort. Among n = 942 individuals, prevalence of long-term symptoms was 36.4% among individuals with known SARS-CoV-2 infection, 25.0% in those

SUBMITTER: Baumkotter R 

PROVIDER: S-EPMC12304058 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature