Predicting the Risk of 1-Year Mortality in Incident Dialysis Patients

BACKGROUND: Administrative databases are increasingly being used to study the incident dialysis population and have important advantages. However, traditional methods of risk adjustment have limitations in this patient population. OBJECTIVE: Our objective was to develop a prognostic index for 1-year mortality in incident dialysis patients using administrative data that was applicable to ambulatory patients, used objective definitions of candidate predictor variables, and was easily replicated in other environments. RESEARCH DESIGN: Anonymized, administrative health data housed at the Institute for Clinical Evaluative Sciences in Toronto, Canada were used to identify a population-based sample of 16,205 patients who initiated dialysis between July 1, 1998 and March 31, 2005. The cohort was divided into derivation, validation, and testing samples and 4 different strategies were used to derive candidate logistic regression models for 1-year mortality. The final risk prediction model was selected based on discriminatory ability (as measured by the c-statistic) and a risk prediction score was derived using methods adopted from the Framingham Heart Study. Calibration of the predictive model was assessed graphically. RESULTS: The risk of death during the first year of dialysis therapy was 16.4% in the derivation sample. The final model had a c-statistic of 0.765, 0.763, and 0.756 in the derivation, validation, and testing samples, respectively. Plots of actual versus predicted risk of death at 1-year showed good calibration. CONCLUSION: The prognostic index and summary risk score accurately predict 1-year mortality in incident dialysis patients and can be used for the purposes of risk adjustment

Dialysis · Disease · Environmental health · Framingham Risk Score · Intensive care medicine · Logistic regression · Population · Risk assessment · Sample size determination · Statistic · Statistics · Acute Kidney Injury Research · Chronic Kidney Disease and Diabetes · Computer Science · Dialysis and Renal Disease Management · Emergency Medicine · Internal Medicine · Mathematics · Medicine

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