Medical College of Wisconsin Leverages the TriNetX Platform to Win Grant to Study Outcomes and Risk Factors for Sickle Cell Disease Patients with COVID-19

Dr. Ashima Singh at the Medical College of Wisconsin needed a large sickle-cell-disease patient dataset to investigate whether COVID-19 elevated thrombosis risk in this population, which already faces elevated clotting risk. Using TriNetX's real-world data platform, she quickly obtained patient demographics and feasibility data that helped her team win a grant from the Children's Research Institute at MCW. The research resulted in a manuscript published in Blood Advances (August 2022) comparing venous thromboembolism rates among hospitalized sickle cell disease patients with and without COVID-19, with plans to expand the pilot into larger future studies.

TriNetX is a federated real-world-data network where data stays behind each health system's firewall and is queried in place, used for cohort discovery and trial-site feasibility.

This sits in the Data Providers stage of the AI use-case lifecycle: Named examples from the real-world-data, claims and competitive-intelligence providers pharma AI systems are trained on, benchmarked against, or run inside of.