Rachel E Patzer1, Mohua Basu, Christian P Larsen, Stephen O Pastan, Sumit Mohan, Michael Patzer, Michael Konomos, William M McClellan, Janice Lea, David Howard, Jennifer Gander, Kimberly Jacob Arriola. 1. 1 Department of Surgery, Division of Transplantation, Emory University, Atlanta, GA. 2 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. 3 Emory University School of Medicine, Atlanta, GA. 4 Department of Medicine, Renal Division, Emory University School of Medicine, Atlanta, GA. 5 Department of Medicine, Division of Nephrology, Columbia University Medical Center, New York, NY. 6 Appex.io, Atlanta, GA. 7 Department of Health Policy and Management, Rollins School of Public Health, Atlanta, GA. 8 Department of Behavioral Sciences and Health Education, Rollins School of Public Health, Emory University, Atlanta, GA.
Abstract
BACKGROUND: Despite a significant survival advantage of kidney transplantation compared with dialysis, nearly one third of end-stage renal disease (ESRD) patients are not educated about kidney transplantation as a treatment option at the time of ESRD diagnosis. Access to individualized, evidence-based prognostic information is needed to facilitate and encourage shared decision making about the clinical implications of whether to pursue transplantation or long-term dialysis. METHODS: We used a national cohort of incident ESRD patients in the US Renal Data System surveillance registry from 2005 to 2011 to develop and validate prediction models for risk of 1- and 3-year mortality among dialysis versus kidney transplantation. Using these data, we developed a mobile clinical decision aid that provides estimates of risks of death and survival on dialysis compared with kidney transplantation patients. RESULTS: Factors included in the mortality risk prediction models for dialysis and transplantation included age, race/ethnicity, dialysis vintage, and comorbidities, including diabetes, hypertension, cardiovascular disease, and low albumin. Among the validation cohorts, the discriminatory ability of the model for 3-year mortality was moderate (c statistic, 0.7047; 95% confidence interval, 0.7029-0.7065 for dialysis and 0.7015; 95% confidence interval, 0.6875-0.7155 for transplant). We used these risk prediction models to develop an electronic, user-friendly, mobile (iPad, iPhone, and website) clinical decision aid called iChoose Kidney. CONCLUSIONS: The use of a mobile clinical decision aid comparing individualized mortality risk estimates for dialysis versus transplantation could enhance communication between ESRD patients and their clinicians when making decisions about treatment options.
BACKGROUND: Despite a significant survival advantage of kidney transplantation compared with dialysis, nearly one third of end-stage renal disease (ESRD) patients are not educated about kidney transplantation as a treatment option at the time of ESRD diagnosis. Access to individualized, evidence-based prognostic information is needed to facilitate and encourage shared decision making about the clinical implications of whether to pursue transplantation or long-term dialysis. METHODS: We used a national cohort of incident ESRDpatients in the US Renal Data System surveillance registry from 2005 to 2011 to develop and validate prediction models for risk of 1- and 3-year mortality among dialysis versus kidney transplantation. Using these data, we developed a mobile clinical decision aid that provides estimates of risks of death and survival on dialysis compared with kidney transplantation patients. RESULTS: Factors included in the mortality risk prediction models for dialysis and transplantation included age, race/ethnicity, dialysis vintage, and comorbidities, including diabetes, hypertension, cardiovascular disease, and low albumin. Among the validation cohorts, the discriminatory ability of the model for 3-year mortality was moderate (c statistic, 0.7047; 95% confidence interval, 0.7029-0.7065 for dialysis and 0.7015; 95% confidence interval, 0.6875-0.7155 for transplant). We used these risk prediction models to develop an electronic, user-friendly, mobile (iPad, iPhone, and website) clinical decision aid called iChoose Kidney. CONCLUSIONS: The use of a mobile clinical decision aid comparing individualized mortality risk estimates for dialysis versus transplantation could enhance communication between ESRDpatients and their clinicians when making decisions about treatment options.
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