Development and future deployment of a 5 years allograft survival model for kidney transplantation

Derek A. DuBay*, Zemin Su, Thomas A. Morinelli, Prabhakar Baliga, Vinayak Rohan, John Bian, David Northrup, Nicole Pilch, Vinaya Rao, Titte R. Srinivas, Patrick D. Mauldin, David J. Taber

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Aim: Identifying kidney transplant patients at highest risk for graft loss prior to loss may allow for effective interventions to improve 5 years survival. Methods: We performed a 10 years retrospective cohort study of adult kidney transplant recipients (n = 1747). We acquired data from electronic health records, United Network of Organ Sharing, social determinants of health, natural language processing data extraction, and real-time capture of dynamically evolving clinical data obtained within 1 year of transplant; from which we developed a 5 years graft survival model. Results: Total of 1439 met eligibility; 265 (18.4%) of them experienced graft loss by 5 years. Graft loss patients were characterized by: older age, being African–American, diabetic, unemployed, smokers, having marginal donor kidneys and cardiovascular comorbidities. Predictive dynamic variables included: low mean blood pressure, higher pulse pressures, higher heart rate, anaemia, lower estimated glomerular filtration rate peak, increased tacrolimus variability, rejection and readmissions. This Big Data analysis generated a 5 years graft loss model with an 82% predictive capacity, versus 66% using baseline United Network of Organ Sharing data alone. Conclusion: Our analysis yielded a 5 years graft loss model demonstrating superior predictive capacity compared with United Network of Organ Sharing data alone, allowing post-transplant individualized risk-assessed care prior to transitioning back to community care.

Original languageEnglish (US)
Pages (from-to)855-862
Number of pages8
JournalNephrology
Volume24
Issue number8
DOIs
StatePublished - Aug 2019

Funding

Support for these studies was provided by National Institutes of Health; K23DK091514 (DD) and R03DK106432 (DD). Authors thank to Ms Rachel Mehard for editorial assistance.

Keywords

  • data analysis
  • decision support technique
  • graft survival
  • kidney
  • transplant

ASJC Scopus subject areas

  • Nephrology

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