Predicting mortality after kidney transplantation: a clinical tool

Abstract

An increasing number of patients referred for transplantation are older and have complex comorbidity affecting outcome. Patient counseling is often empiric and time consuming. For the physician there are few clinical tools available to help quantify survival chances after transplantation. We used registry data to develop a series of tables that could be used in the clinical setting to predict survival probability. Using data from the Canadian Organ Replacement Registry, we generated clinical survival tables using Cox's regression model. Model covariates included age, race, gender, treatment period, primary renal disease cause, donor source, months on dialysis and comorbidities. A total of 6324 patients were included, 22% had > or =1 comorbid condition at baseline. After adjustment for age, gender and cause of renal disease, increased comorbidity was strongly associated with reduced patient-survival (P < 0.05). Age and comorbidity specific clinical survival tables showing the expected 1-, 3- and 5-year patient survival probabilities were generated. Separate tables were created for diabetics, nondiabetics, living-donor organs and deceased-donor transplantation. Patient-specific survival data can be estimated from registry data. We suggest annual or biannual tables generated by national registries across Europe and N. America, may be useful to those physicians faced with counseling patients and families.