BACKGROUND: Surprisingly few tools have been developed to predict outcomes after kidney transplant. STUDY DESIGN: Retrospective observational cohort study. SETTING & PARTICIPANTS: Adult patients from US Renal Data System (USRDS) data who underwent deceased donor kidney transplant in 2000-2006. PREDICTOR: Full and abbreviated prediction tools for graft loss using candidate predictor variables available in the USRDS registry, including data from the Organ Procurement and Transplantation Network and the Centers for Medicare & Medicaid Services End-Stage Renal Disease Program. OUTCOMES: Graft loss within 5 years, defined as return to maintenance dialysis therapy, preemptive retransplant, or death with a functioning graft. MEASUREMENTS: We used Cox proportional hazards analyses to develop separate tools for assessment (1) pretransplant, (2) at 7 days posttransplant, and (3) at 1 year posttransplant to predict subsequent risk of graft loss within 5 years of transplant. We used measures of discrimination and explained variation to determine the number of variables needed to predict outcomes at each assessment time in the full and abbreviated equations, creating simple user-friendly prediction tools. RESULTS: Although we could identify 32, 29, and 18 variables that predicted graft loss assessed pretransplant and at 7 days and 1 year posttransplant ("full" models), 98% of the discriminatory ability and >80% of the variability explained by the full models could be achieved using only 11, 8, and 6 variables, respectively. LIMITATIONS: Comorbidity data were from the Centers for Medicare & Medicaid Medical Evidence Report, which may significantly underreport comorbid conditions; C statistic values may indicate only modest ability to discriminate risk for an individual patient. CONCLUSIONS: This method produced risk-prediction tools that can be used easily by patients and clinicians to aid in understanding the absolute and relative risk of graft loss within 5 years of transplant.
BACKGROUND: Surprisingly few tools have been developed to predict outcomes after kidney transplant. STUDY DESIGN: Retrospective observational cohort study. SETTING & PARTICIPANTS: Adult patients from US Renal Data System (USRDS) data who underwent deceased donor kidney transplant in 2000-2006. PREDICTOR: Full and abbreviated prediction tools for graft loss using candidate predictor variables available in the USRDS registry, including data from the Organ Procurement and Transplantation Network and the Centers for Medicare & Medicaid Services End-Stage Renal Disease Program. OUTCOMES: Graft loss within 5 years, defined as return to maintenance dialysis therapy, preemptive retransplant, or death with a functioning graft. MEASUREMENTS: We used Cox proportional hazards analyses to develop separate tools for assessment (1) pretransplant, (2) at 7 days posttransplant, and (3) at 1 year posttransplant to predict subsequent risk of graft loss within 5 years of transplant. We used measures of discrimination and explained variation to determine the number of variables needed to predict outcomes at each assessment time in the full and abbreviated equations, creating simple user-friendly prediction tools. RESULTS: Although we could identify 32, 29, and 18 variables that predicted graft loss assessed pretransplant and at 7 days and 1 year posttransplant ("full" models), 98% of the discriminatory ability and >80% of the variability explained by the full models could be achieved using only 11, 8, and 6 variables, respectively. LIMITATIONS: Comorbidity data were from the Centers for Medicare & Medicaid Medical Evidence Report, which may significantly underreport comorbid conditions; C statistic values may indicate only modest ability to discriminate risk for an individual patient. CONCLUSIONS: This method produced risk-prediction tools that can be used easily by patients and clinicians to aid in understanding the absolute and relative risk of graft loss within 5 years of transplant.
Authors: Isaac E Hall; Mona D Doshi; Peter P Reese; Richard J Marcus; Heather Thiessen-Philbrook; Chirag R Parikh Journal: Clin J Am Soc Nephrol Date: 2012-06-21 Impact factor: 8.237
Authors: Ariane T Michelson; Demetra S Tsapepas; S Ali Husain; Corey Brennan; Mariana C Chiles; Brian Runge; Jennifer Lione; Byum H Kil; David J Cohen; Lloyd E Ratner; Sumit Mohan Journal: Clin Transplant Date: 2018-10-28 Impact factor: 2.863
Authors: Isaac E Hall; Peter P Reese; Mona D Doshi; Francis L Weng; Bernd Schröppel; William S Asch; Joseph Ficek; Heather Thiessen-Philbrook; Chirag R Parikh Journal: Transplantation Date: 2017-08 Impact factor: 4.939