Rosemary G McKaig
$185,676
UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
South Carolina
National Institute of Allergy and Infectious Diseases (NIAID)
The COVID-19 pandemic has cast a heavy burden on individuals with HIV infection. Based on data of 15,522 hospitalized patients with the coinfection of HIV and SARS-CoV-2 from 24 countries, a recent World Health Organization (WHO) report for the first time confirmed that HIV to be an independent risk factor for severe COVID-19. Despite a generally high risk of severe COVID-19 clinical course in individuals with HIV, the interactions between SARS-CoV-2 and HIV infections remain unclear. For example, the severity of COVID-19 in individuals with HIV is correlated with certain comorbidities in which some of these comorbidities are more prevalent in patients with HIV than other populations. Yet, several contradictory findings suggested the predominant role of comorbidities in the severity of COVID-19 regardless of HIV infection. Individuals with low CD4+ T-cell count (e.g., <200~500 cells/ยตL) and unsuppressed viral load are associated with severe clinical course, yet the role of antiretroviral therapy (ART) exposure and adherence in the context of COVID-19 exposure needs to be examined. Risk factors for the severe clinical course of the coinfection are undetermined because individuals with the same or similar severity level of COVID-19 show different clinical characteristics. To fill address these knowledge gaps, this study will establish an EHR-based cohort for individuals with HIV/SARS- CoV-2 coinfection and develop large-scale EHR-based data mining to examine the interactions between HIV and SARS-CoV-2 infections and systematically identify and validate factors contributing to the severe clinical course of the coinfection. Ultimately, collected clinical evidence will be implemented and used to pilot test a Clinical Decision Support (CDS) prototype to assist providers in screening and referral of at-risk patients in real-world clinics.