Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts.

ABSTRACT:

Background

SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors.

Methods

In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were anal

SUBMITTER: Zang C 

PROVIDER: S-EPMC11239808 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature