aHarold Simmons Center for Kidney Disease Research and Epidemiology, Division of Nephrology and Hypertension, University of California, Irvine Medical Center, Orange, CAbDivision of General Internal Medicine and Primary Care, University of California, Irvine Medical Center, Orange, CAcKaiser Permanente Southern California, Pasadena, CAdInstitute for Clinical and Translational Science, University of California, Irvine, CAeNephrology Section, Tibor Rubin Veterans Affairs Medical Center, Long Beach, CAfDivision of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, TNgDivision of Transplant Surgery, Methodist University Hospital Transplant Institute, Memphis, TNhDivision of Transplant Surgery, Department of Surgery, University of Tennessee Health Science Center, Memphis, TNiDepartment of Transplantation and Surgery, Semmelweis University, Budapest, HungaryjNational Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MDkNephrology Section, Memphis Veterans Affairs Medical Center, Memphis, TNlDepartment of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA
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Objective:To develop and validate a risk prediction model that would help individualize treatment and improve the shared decision-making process between clinicians and patients.Patients and Methods:We developed a risk prediction tool for mortality during the first year of dialysis based on pre–end-stage renal disease characteristics in a cohort of 35,878 US veterans with incident end-stage renal disease who transitioned to dialysis treatment between October 1, 2007, and March 31, 2014 and then externally validated this tool among 4284 patients in the Kaiser Permanente Southern California (KPSC) health care system who transitioned to dialysis treatment between January 1, 2007, and September 30, 2015.Results:To ensure model goodness of fit, 2 separate models were selected for patients whose last estimated glomerular filtration rate (eGFR) before dialysis initiation was less than 15 mL/min per 1.73 m2 or 15 mL/min per 1.73 m2 or higher. Model discrimination in the internal validation cohort of veterans resulted in C statistics of 0.71 (95% CI, 0.70-0.72) and 0.66 (95% CI, 0.65-0.67) among patients with eGFR lower than 15 mL/min per 1.73 m2 and 15 mL/min per 1.73 m2 or higher, respectively. In the KPSC external validation cohort, the developed risk score exhibited C statistics of 0.77 (95% CI, 0.74-0.79) in men and 0.74 (95% CI, 0.71-0.76) in women with eGFR lower than 15 mL/min per 1.73 m2 and 0.71 (95% CI, 0.67-0.74) in men and 0.67 (95% CI, 0.62-0.72) in women with eGFR of 15 mL/min per 1.73 m2 or higher.Conclusion:A new risk prediction tool for mortality during the first year after transition to dialysis (available at www.DialysisScore.com) was developed in the large national Veterans Affairs cohort and validated with good performance in the racially, ethnically, and gender diverse KPSC cohort. This risk prediction tool will help identify high-risk populations and guide management strategies at the transition to dialysis.