Interdisciplinary Faculty Team Awarded Grant to Examine Outcomes in Pediatric Organ Transplants
Two Florida State University faculty members have been awarded a grant to study health outcomes after pediatric organ transplants.
Michael Killian, an assistant professor with the FSU College of Social Work, and Zhe He, an assistant professor with the FSU College of Communication and Information, were awarded the grant by the University of Florida Clinical and Translational Science Institute (CTSI) Precision Health Initiative Pilot Project.
“We’re grateful for the piloting funding from the joint UF and FSU Clinical and Translational Science Institute to study health outcomes in these children,” Killian said. “Our goal is to help support decision-making in their healthcare and to enhance the health and quality of life for these children. Long term, we want to make Florida and our health care systems statewide the national leaders in pediatric organ transplant care and health outcomes.”
The grant is a partnership with the OneFlorida Clinical Research Consortium to examine posttransplant health outcomes in pediatric patients nationally and across Florida, as well as to advance the use of predictive modeling using data and statistics to predict health outcomes.
The OneFlorida Clinical Research Consortium is a statewide research group that seeks to improve patient health, healthcare and health policy. Included in the consortium is the OneFlorida Data Trust, which is a valuable data repository containing healthcare data. This consortium is the result of a collaboration among the University of Florida, Florida State University and the University of Miami along with other affiliated health systems state-wide.
Killian, He and their research team, including pediatric cardiologist Dipankar Gupta of Shands Children’s Hospital at the University of Florida and FSU School of Information Doctoral Candidate Seyedeh Neelufar Payrovnaziri, will set out to analyze a national organ transplant database and pediatric transplant patient data from the OneFlorida Data Consortium using machine learning and deep learning approaches.
“Machine learning refers to the study and use of algorithms and statistical models that scientists can use to apply to learn from data and make predictions in specific tasks,” He said. “Deep learning approaches are the most promising kind of machine learning methods that attempt to simulate the human brain for learning and prediction.”
These approaches will be used to understand the risk factors for outcomes for children after an organ transplant and predict these outcomes with data. The risk factors include demographics, familial, medication adherence, health and posttransplant outcomes like organ rejection and related hospitalizations.
The long-term goal of the project is to improve knowledge and the prediction of posttransplant outcomes in pediatric patients to improve medical care and quality of life.
Researchers said their results will inform a decision-making tool for transplant physicians and teams, which will allow for more timely and efficient identification of children and families at risk for poor outcomes after an organ transplant and interventions that can prevent them.
The findings of the study also will directly inform a subsequent project that will examine results among a larger multisite study of pediatric transplant centers within the OneFlorida Clinical Research Consortium and Data Trust.