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To adjust adequately for comorbidity and severity of illness in quality improvement efforts and prospective clinical trials, predictors of death after acute renal failure (ARF) must be accurately identified. Most epidemiological studies of ARF in the critically ill have been based at single centers, or have examined exposures at single time points using discrete outcomes (e.g., in-hospital mortality). We analyzed data from the Program to Improve Care in Acute Renal Disease (PICARD), a multi-center observational study of ARF. We determined correlates of mortality in 618 patients with ARF in intensive care units using three distinct analytic approaches. The predictive power of models using information obtained on the day of ARF diagnosis was extremely low. At the time of consultation, advanced age, oliguria, hepatic failure, respiratory failure, sepsis, and thrombocytopenia were associated with mortality. Upon initiation of dialysis for ARF, advanced age, hepatic failure, respiratory failure, sepsis, and thrombocytopenia were associated with mortality; higher blood urea nitrogen and lower serum creatinine were also associated with mortality in logistic regression models. Models incorporating time-varying covariates enhanced predictive power by reducing misclassification and incorporating day-to-day changes in extra-renal organ system failure and the provision of dialysis during the course of ARF. Using data from the PICARD multi-center cohort study of ARF in critically ill patients, we developed several predictive models for prognostic stratification and risk-adjustment. By incorporating exposures over time, the discriminatory power of predictive models in ARF can be significantly improved.