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Development and future deployment of a 5 years allograft survival model for kidney transplantation
Author(s) -
DuBay Derek A,
Su Zemin,
Morinelli Thomas A,
Baliga Prabhakar,
Rohan Vinayak,
Bian John,
Northrup David,
Pilch Nicole,
Rao Vinaya,
Srinivas Titte R,
Mauldin Patrick D,
Taber David J
Publication year - 2019
Publication title -
nephrology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 61
eISSN - 1440-1797
pISSN - 1320-5358
DOI - 10.1111/nep.13488
Subject(s) - medicine , cohort , renal function , retrospective cohort study , kidney transplantation , psychological intervention , cohort study , transplantation , surgery , psychiatry
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.