# Developing High-Quality Tools to Characterize Allograft Quality, Predict Transplant Outcomes and Expand Access to Kidney and Liver Transplantation

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $525,678

## Abstract

Project Summary/Abstract
In the US, nearly 95,000 patients are waitlisted for a kidney transplant, yet in 2018, only 14,700 received a
deceased donor kidney transplant, while nearly 8,500 died or became too sick. The organ shortage is equally
intense for liver transplant; in 2018, among more than 14,000 waitlisted patients, only 7,700 received a
deceased donor liver transplant while 2,500 died or became too sick. Unfortunately, more than 5,000 kidneys
and 2,000 livers from deceased donors were offered for transplant in 2018, but never transplanted. Although a
subset of these organs was unsuitable for transplant, data clearly demonstrate that the inability to accurately
assess graft quality directly led to many discards and/or undermined our ability to guide organs to appropriate
patients. Prior to their organs being offered for transplant, deceased donors are hospitalized for days, often
with numerous longitudinal data points (e.g., laboratory values) available to assess organ function. Yet,
existing models of graft quality have these major flaws: 1) a reliance on cross-sectional clinical and laboratory
data directly prior to procurement; 2) neglect of biologically-relevant, longitudinal data from the donor terminal
hospitalization such serial hemodynamics (kidney and liver) and urine output (kidney); and 3) failure to
integrate interactions between donor and recipient characteristics. As a result, existing kidney and liver donor
risk models have inadequate prediction accuracy (C-statistics of only 0.6-0.65). Our group proposes to
advance the field by developing state-of-the art models that make use of extensive, longitudinal donor data
during the donor's terminal hospitalization—laboratory biomarkers of organ injury, and measures of organ
function and perfusion. Second, we will develop highly robust allograft risk models using the joint modeling
approach, which can account for longitudinal donor exposure data and time-to-event outcomes such as graft
failure, instead of standard techniques (e.g., Cox regression). Third, we will highlight the real-world impact of
the results in terms of population health. We have these specific aims: 1) Develop kidney graft failure models
using joint modeling to predict graft failure with higher discrimination and calibration relative to the current
kidney donor risk index; 2) Develop liver graft failure risk models using joint modeling to predict graft failure
with high discrimination and calibration; 3a) Simulate the change in allograft life years from better pairing
organs to recipients based on alignment of projected organ and patient survival; and 3b) Simulate the change
in the number of transplants and allograft life years for the transplant population by implementing improved
organ quality metrics in organ allocation to decrease discards. The models will be constructed using
comprehensive US transplant data and externally validated with data from two Canadian provinces. The grant
will also include important...

## Key facts

- **NIH application ID:** 10051933
- **Project number:** 1R01DK123041-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** David Seth Goldberg
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $525,678
- **Award type:** 1
- **Project period:** 2020-07-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10051933

## Citation

> US National Institutes of Health, RePORTER application 10051933, Developing High-Quality Tools to Characterize Allograft Quality, Predict Transplant Outcomes and Expand Access to Kidney and Liver Transplantation (1R01DK123041-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10051933. Licensed CC0.

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