# Improving Pediatric Donor Heart Utilization with Predictive Analytics

> **NIH AHRQ R21** · UNIVERSITY OF VIRGINIA · 2024 · $138,009

## Abstract

PROJECT SUMMARY/ABSTRACT
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 (Aim 1). 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 (Aim 2) to actively run
and display Aim 1’s models as well as display the avalanche of data associated with a typical donor offer in
a user-friendly, individualizable interface produced from an iterative design process using feedback from a
team of experienced pediatric transplant cardiologists (Aim 3). 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. These realistic offers will use deidentified historic data variably augmented with
model outputs and/or the customizable visualizations, allowing us to assess the impact of these interventions
on donor acceptance practices. If successful, UNOS will integrate our models and dashboard into its online
platform for organ matching, where the technology will support clinical decision-making at the point of care.
These aims will support our long-term goal of producing a straightforward, real-time assessment tool for
heart transplant decisions. This technology could inform standardized and efficient guidelines for donor heart
acceptance, thereby saving lives and reducing the unnecessary refusal of donor hearts. The end result could
demonstrate a proof-of-concept for predictive modeling rese...

## Key facts

- **NIH application ID:** 11058224
- **Project number:** 1R21HS029548-01A1
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Michael A. McCulloch
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2024
- **Award amount:** $138,009
- **Award type:** 1
- **Project period:** 2024-09-30 → 2026-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11058224

## Citation

> US National Institutes of Health, RePORTER application 11058224, Improving Pediatric Donor Heart Utilization with Predictive Analytics (1R21HS029548-01A1). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/11058224. Licensed CC0.

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