In the United States, approximately 14% of children with end-stage heart failure or inoperable congenital heart defects die while awaiting heart transplants, yet nearly 40% of donated hearts remain unutilized. When a donor heart becomes available, clinicians have minutes to sort through hundreds of donor and candidate variables to determine whether the organ is a good fit for their patient.
However, the nuanced impact of these variables on patient outcomes remains poorly understood, and no data-driven guidelines or evidence- based tools exist to support clinician decision-making. Consequentially, the prevailing uncertainty and associated risk aversion lead to the unnecessary rejection of many suitable hearts. We hypothesize that the integration of evidence-based tools can increase clinicians’ confidence in these critical decisions, thereby optimizing pediatric donor heart utilization and reducing waitlist mortality without negatively affecting post- transplant survival.
To answer this need, we are developing the first predictive models to assess, at the time of offer, the given candidate’s post-transplant survival if they received the offered heart; and if refused, projected time to next offer and the associated waitlist survival. To do this, we will use machine learning technology to analyze all 30,000 US-based pediatric heart offers made between 2010–2020, the most complete representation of national pediatric donor information ever compiled; we will validate the models using external data from 2020-2022.
We will collaborate with the United Network of Organ Sharing (UNOS), the national organ transplant system, to customize their established simulation platform. We will measure the success of our models and improved interface by sending simulated donor offers to a large team of pediatric transplant cardiologists from around the country.
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