ATC Abstracts

American Transplant Congress abstracts

  • Home
  • Meetings Archive
    • 2022 American Transplant Congress
    • 2021 American Transplant Congress
    • 2020 American Transplant Congress
    • 2019 American Transplant Congress
    • 2018 American Transplant Congress
    • 2017 American Transplant Congress
    • 2016 American Transplant Congress
    • 2015 American Transplant Congress
    • 2013 American Transplant Congress
  • Keyword Index
  • Resources
    • 2021 Resources
    • 2016 Resources
      • 2016 Welcome Letter
      • ATC 2016 Program Planning Committees
      • ASTS Council 2015-2016
      • AST Board of Directors 2015-2016
    • 2015 Resources
      • 2015 Welcome Letter
      • ATC 2015 Program Planning Committees
      • ASTS Council 2014-2015
      • AST Board of Directors 2014-2015
      • 2015 Conference Schedule
  • Search

A Machine Learning Approach to Estimating Anticipated Waiting Time and Survival Benefit in Older Kidney Transplant Candidates

D. M. Vock1, E. S. Helgeson2, A. J. Matas2

1University of Minnesota, Minneapolis, MN, 2U MN, Mpls, MN

Meeting: 2022 American Transplant Congress

Abstract number: 539

Keywords: Allocation, Elderly patients, Kidney transplantation, Waiting lists

Topic: Clinical Science » Organ Inclusive » 72 - Machine Learning, Artificial Intelligence and Social Media in Transplantation

Session Information

Session Name: Machine Learning, Artificial Intelligence and Social Media in Transplantation

Session Type: Rapid Fire Oral Abstract

Date: Tuesday, June 7, 2022

Session Time: 5:30pm-7:00pm

 Presentation Time: 6:10pm-6:20pm

Location: Hynes Room 210

*Purpose: Older kidney transplant (ktx) recipients face worse post-transplant survival due to increasing age and more comorbidities. Simultaneously, older ktx candidates are more likely to wait longer (increasing time on dialysis) or receive a higher KDPI kidney which further erodes post-transplant survival. There may be certain older recipients who based on their predicted wait times cannot expect a substantial benefit from a deceased donor (DD) transplant. We sought to estimate how long an older candidate can wait for a DD ktx and still expect a survival benefit given their characteristics.

*Methods: Using SRTR data, we identified candidates listed for first ktx alone after 2000 who were 60 years of age or older (n = 87,416 candidates, n = 27,594 DD recipients). We fit random survival forest models to estimate waitlist and post-DD ktx survival adjusting for candidate/recipient characteristics (e.g., demographics, comorbidities, functional status, time on dialysis) and donor/surgical characteristics (KDPI, ischemia time). Using these machine learning models, we estimated the expected survival under different waiting times for different quality of DD organs. We defined DD ktx as beneficial if the restricted mean 10-year survival (RMS-10yr) was increased by 0.5 yrs compared to remaining on the waiting list and calculated the maximum waiting time for a candidate to expect a benefit (referred as TIPPING POINT).

*Results: Among this cohort (median age 65.3 yrs, 63% male), candidates had significant comorbidities including 58% DM, 77% HTN, 12% angina, 5% CeVD, 9% PVD, and 10% prior malignancy. Overall, the benefit of DD ktx declined as waiting time increased (Fig 1). Averaging over other characteristics, the tipping point was approximately 5.4 years if the candidate received a higher-quality kidney (KDPI = 35) and just under 5 years for a lower-quality kidney (KDPI = 85, Fig 2). In general the tipping point was lower for candidates with comorbidities but did not very substantially by age at listing overall or within comorbid conditions (Fig 2).

*Conclusions: Potential ktx recipients who based on the organ availability in their region cannot expect to receive a DD transplant before the tipping point should be counseled to pursue living donor transplant.

  • Tweet
  • Click to email a link to a friend (Opens in new window) Email
  • Click to print (Opens in new window) Print

To cite this abstract in AMA style:

Vock DM, Helgeson ES, Matas AJ. A Machine Learning Approach to Estimating Anticipated Waiting Time and Survival Benefit in Older Kidney Transplant Candidates [abstract]. Am J Transplant. 2022; 22 (suppl 3). https://atcmeetingabstracts.com/abstract/a-machine-learning-approach-to-estimating-anticipated-waiting-time-and-survival-benefit-in-older-kidney-transplant-candidates/. Accessed May 17, 2025.

« Back to 2022 American Transplant Congress

Visit Our Partner Sites

American Transplant Congress (ATC)

Visit the official site for the American Transplant Congress »

American Journal of Transplantation

The official publication for the American Society of Transplantation (AST) and the American Society of Transplant Surgeons (ASTS) »

American Society of Transplantation (AST)

An organization of more than 3000 professionals dedicated to advancing the field of transplantation. »

American Society of Transplant Surgeons (ASTS)

The society represents approximately 1,800 professionals dedicated to excellence in transplantation surgery. »

Copyright © 2013-2025 by American Society of Transplantation and the American Society of Transplant Surgeons. All rights reserved.

Privacy Policy | Terms of Use | Cookie Preferences