Development and future deployment of a 5 years allograft survival model for kidney transplantation. Issue 8 (30th April 2019)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Nephrology. Volume 24:Issue 8(2019)
• Page Start: 855
• Page End: 862
• Publication Date: 2019-04-30
• Subjects: data analysis -- decision support technique -- kidney -- graft survival -- transplant; Nephrology -- Periodicals; Kidneys -- Diseases -- Periodicals; Nephrologists -- Periodicals; 616.61; 616.61
• DOI: 10.1111/nep.13488
• Languages: English
• ISSNs: 1320-5358

Abstract

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. SUMMARY AT A GLANCE: The study focusses on the development of a predictive model for 5 years overall allograft survival using big data approach, using registry data as well as granular and dynamic data from electronic health care records. This model has over an 80% predictive capacity in determining 5 years allograft survival and may be a useful adjunct to standard post‐transplant care. Validation of this predictive model in other cohorts will be essential.