# Harnessing Dynamic Cardiac Parameters to Optimize Donor Heart Utilization

> **NIH NIH F32** · STANFORD UNIVERSITY · 2022 · $62,427

## Abstract

PROJECT SUMMARY
Heart failure is an extremely prevalent and morbid condition projected to affect over 8 million people in the United
States by 2030. Heart transplantation is considered the final life-saving treatment option for patients with
advanced heart failure. However, there is a large discrepancy between the number of patients on the heart
transplant waiting list and the number of transplants performed each year. This discrepancy is largely due to the
high proportion of donor hearts that are turned down by transplant centers due to lack of standardized donor
selection criteria. Our limited understanding of which donor cardiac parameters are associated with poor recipient
outcomes hampers our ability to expand the donor pool. Currently we rely on traditional donor selection criteria
such as normal echocardiograms, normal electrocardiograms, and low doses of vasoactive and inotropic
medications. We know that many donor factors influence transplant recipient outcomes, yet there is limited
understanding of the degree of risk that each donor factor represents. Thus, there is a critical need to better
understand the landscape of cardiac dysfunction present in donor hearts and their relationship with long term
outcomes in order to expand access to heart transplantation and promote transplant longevity. The central
hypothesis is that traditional donor selection criteria will be inversely correlated with transplant recipient mortality
and acute rejection and that machine learning applied to recipient data will better predict outcomes such as acute
rejection. This study will (1) investigate donor vasoactive/inotropic medication use, left ventricular
hypertrophy/remodeling, left ventricular ejection fraction, and left ventricular wall motion abnormalities and
determine their relationship with donor use and transplant recipient outcomes; (2) determine whether donor
electrocardiogram abnormalities correlate with donor echocardiogram abnormalities; and (3) apply a previously
validated machine learning algorithm to transplant recipient electrocardiograms for assessment of acute graft
rejection. The results of this project will challenge traditional donor selection criteria, allow for expansion of donor
heart use, and will contribute to improving heart transplant longevity via novel use of machine learning algorithms.
This will ultimately expand access to life-saving heart transplantation and improve transplant longevity. This
project will be conducted at Stanford University, an internationally renowned research institution and a leading
site for cutting edge heart transplant research. Stanford was the site of the first heart transplant in the United
States and is the 4th largest heart transplant program in the country. The candidate will receive expert mentorship
from esteemed faculty in the field of Advanced Heart Failure and Transplant Cardiology. To complement this
mentoring, an individually tailored training plan will enable the candidate to develop c...

## Key facts

- **NIH application ID:** 10536540
- **Project number:** 1F32HL165817-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** NATALIE TAPASKAR
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $62,427
- **Award type:** 1
- **Project period:** 2022-08-25 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10536540

## Citation

> US National Institutes of Health, RePORTER application 10536540, Harnessing Dynamic Cardiac Parameters to Optimize Donor Heart Utilization (1F32HL165817-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10536540. Licensed CC0.

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