Identification of predictive models of COVID-19 severity in a multi-state setting for its use in risk stratification and care management (ID-CoV).

During the course of the COVID-19 outbreak, a wealth of data has been accumulated from the efforts of the health systems to overcome the pandemic. Months of patient encounters with primary to tertiary care systems are leaving an affluence of valuable information reflecting the real impact of the virus in people's health and lives. These real-world data [RWD] offer an unparalleled opportunity to understand COVID-19 but also an important analytical challenge due to the dissimilar and heterogeneous nature of this information. Using complementary data sources from primary health care and hospitals, this project aims to set up a methodological framework for data harmonization, linkage, and analytical development of a novel tool for multi-state risk prediction identifying the role of comorbidities, among other factors, in predicting COVID-19 progression into severity, and subsequent recovery or death. This research will afford a unique instrument for risk stratification and resource allocation in the face of current and future epidemics and will serve as a proof of concept of the usefulness of RWD and the feasibility of the adaptation of novel this methodological framework to other countries/settings based on local data.

Keywords:COVID-19

Disciplines:Epidemiology

Project type:Collaboration project