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

Characterization of an Integrative Prognostic Score for US Patients Taken from the DART Study

J. Bromberg1, R. Bloom2, P. Sood3

1University of Maryland, Baltimore, MD, 2Penn Medicine, Philadelphia, PA, 3University of Pittsburgh Medical Center, Pittsburgh, PA

Meeting: 2020 American Transplant Congress

Abstract number: B-316

Keywords: Glomerular filtration rate (GFR), Kidney transplantation, Outcome, Proteinuria

Session Information

Session Name: Poster Session B: Biomarkers, Immune Assessment and Clinical Outcomes

Session Type: Poster Session

Date: Saturday, May 30, 2020

Session Time: 3:15pm-4:00pm

 Presentation Time: 3:30pm-4:00pm

Location: Virtual

*Purpose: iBox is a validated cloud-based software as a service (SaaS) algorithm that provides a predictive analysis of the post-transplant patient, quantifying the risk of kidney loss based upon multiple pre-determined clinical factors in kidney transplant scenarios at any time point following surgery as long as the inputs necessary are available. iBox mandatory inputs include: 1. time from transplant to risk evaluation; 2. estimated GFR (mL/min/1,73m²); and 3. proteinuria (g/g of creatinine/total protein). Additional inputs that improve the accuracy include DSA MFI, scores for Banff indices (g,i,t, ptc,cg,ci,ct) or diagnoses based on pathology (ABMR, TCMR, CNI, Recurrence, BK, AKI). The output result includes allograft loss probabilities for 1, 3 and 5 years from evaluation time, and potential stratification based on renal function trajectory prediction. The objective of this study was to characterize the iBox algorithm using a smaller cohort of patients from 14 centers who were prospectively surveyed in the DART study over 12 months (ClinicalTrials.gov Identifier: NCT02424227). Due to the small sample size and short time window, the analysis assessed the ability of iBox iBox to predict a surrogate for graft loss, eGFR<20. Donor derived cell free DNA (dd-cfDNA), not currently included in the iBox algorithm, was independently considered as a predictor of outcome at 12 months.

*Methods: 185 patients from DART had sufficient data to complete the mandatory inputs required for iBox. Within 12 months of follow up, these patients had a total of 9 events, defined as eGFR<20 without return to higher eGFR. iBox scores were calculated at the 7 time points used in the surveillance protocol: months 1,2,3,4,6,9 and 12. This population was representative of patients followed through standard of care practice in US kidney transplant programs.

*Results: DART data did not have complete parameters for iBox at each time-point; for example biopsies were not done at each point; the best model possible for each sample was used. Within this set of samples 19.5% included both DSA and biopsy. Using the best fit model for each sample a C-statistic of 0.83 was obtained for iBox prediction of these events in the 185 DART patients (standard error 0.1). iBox 1-year prognostication scores measured for the samples were not correlated with dd-cfDNA measurement at the same sample (correlation=-0.1213, p = 0.083).

*Conclusions: As transplant moves toward increasingly relying on predictions made by computer models to inform clinical decisions, knowing the iBox algorithm performs as expected in smaller cohorts when considering diagnosis and 1-year prognosis, suggests clear translational value. As the data may be complementary, the inclusion of dd-cfDNA considering allograft injury may be a valuable addition to evolve this algorithm as we improve understanding as well as consider the impact of medical interventions and treatment response on graft failure risk.

  • Tweet
  • Email
  • Print

To cite this abstract in AMA style:

Bromberg J, Bloom R, Sood P. Characterization of an Integrative Prognostic Score for US Patients Taken from the DART Study [abstract]. Am J Transplant. 2020; 20 (suppl 3). https://atcmeetingabstracts.com/abstract/characterization-of-an-integrative-prognostic-score-for-us-patients-taken-from-the-dart-study/. Accessed May 10, 2025.

« Back to 2020 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