Development and future deployment of a 5 years allograft survival model for kidney transplantation
| Autor: | Vinayak Rohan, Nicole A. Pilch, David Northrup, Prabhakar K. Baliga, Zemin Su, Patrick D. Mauldin, Vinaya Rao, Thomas A. Morinelli, John Bian, Titte R. Srinivas, Derek Dubay, David J. Taber |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Graft Rejection Male medicine.medical_specialty Time Factors 030232 urology & nephrology Renal function 030204 cardiovascular system & hematology Risk Assessment Article Cohort Studies 03 medical and health sciences 0302 clinical medicine Internal medicine Heart rate medicine Humans Transplantation Homologous Social determinants of health Kidney transplantation Retrospective Studies Kidney Models Statistical business.industry Graft Survival Retrospective cohort study General Medicine Middle Aged medicine.disease Kidney Transplantation Tacrolimus medicine.anatomical_structure Data extraction Nephrology Female business Forecasting |
| Zdroj: | Nephrology (Carlton) |
| ISSN: | 1440-1797 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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