CERSI Collaborator: Rohit Vashisht, PhD; Atul Butte, MD, PhD; Bridgit O. Crews, PhD; Omai Garner, PhD

FDA Collaborators: Kaveeta Vasisht, MD, Pharm. D; Susan Bersoff-Matcha, MD; Laurén Doamekpor, PhD, MPH; Osman Yogurtcu, PhD; Yong Ma, PhD; Andrew Giffin, PhD; Shuang Zhou, PhD

Project Start Date: October 13, 2021

Regulatory Science Challenge

The urgency of COVID-19 has set up a scenario where the public cannot wait for slow, deliberate generation and analysis of patient health and care data that are routinely collected as part of getting care or daily living (real-world data). Learning how to use available high quality real-world data sources to help with medical and policy decisions needs to be done quickly. Determining how and when available data can be trusted to quickly incorporate lessons learned into decision-making also needs to be understood. In addition to the real-world data challenges, research suggests that there are differences in how males and females are vulnerable to SARS-CoV-2 infection and how their immune systems respond to the infection. Males are more vulnerable to infection and death from SARS-CoV-2. Analyses of real-world data from electronic health records across the University of California Health (UC Health) system showed that males had a higher sensitivity of antibody tests as compared to females. This points to sex-specific differences in the immune response to SARS-CoV-2 infection. It is unclear how sex-specific patient-level clinical characteristics, such as pre-existing conditions, prior medication use, clinical visits, and medical procedures, might influence the susceptibility to SARS-CoV-2 infection and affect underlying immune responses. Also, there is a gap in our understanding of the sex-specific link between treatment choices and outcomes among patients who are hospitalized due to SARS-CoV-2 infection. Explaining clinical factors that might affect the susceptibility to SARS-CoV-2 infection, the underlying human response, and in-hospital treatment choices are of critical importance to improve medical and regulatory decision-making related to the COVID-19 pandemic.

Project Description & Goals

In this project, we aim to identify important clinical factors with a systematic analysis of the real-world clinical data of over half a million patients receiving COVID-19 related care across UC Health. We will then confirm our findings using the nationwide COVID-19 data from the NIH-N3C collaborative cohort of over 10 million individuals tested or treated for COVID-19. The UC Health system treats patients in the general population across 6 academic health centers and 12 hospitals, with approximately 150,000 inpatient and 4 million outpatient visits yearly. We will use the UC Health COVID Research Dataset (UC CORDS) as our training and testing data set and validate our findings using the nationwide COVID-19 dataset from the NIH-N3C collaborative. Both the UC CORDS and NIH-N3C collaborative structure their underlying clinical data using a standardized common data model. We, therefore, aim to develop, test, and validate our analysis based on both the datasets and make our computer program available to the public to enable further research.

The specific aims and their brief description are as follows:

1. Learning Potential Clinical Factors That Might Influence SARS-CoV-2 Susceptibility: We propose to build a fair and transparent analytic framework to learn potential clinical factors such as pre-existing conditions, prior medication use and medical procedures including socioeconomic factors that might influence sex-specific susceptibility to SARS-CoV-2 infection.

2. Characterizing Natural and Vaccine Mediated Immune Response: We aim to characterize sex-specific immune response to natural and vaccine mediated immunity against SARS-CoV-2 infection.

3. Characterizing Treatment Utilization Patterns and Outcome: We aim to characterize real-world treatment utilization pathways among 92 different treatments that are currently under evaluation based on treatment guidelines made available by the NIH (https://www.covid19treatmentguidelines.nih.gov/).