Despite significant improvements in medical care, acute renal failure (ARF) remains a high risk for mortality. It is important to be able to predict the outcome in these patients in view of the emotional and ethical needs of the patients and to address questions of efficiency and quality of care. We analyzed the risk factors predicting mortality prospectively in a group of 265 patients using univariate and multiple logistic regression analysis. A prognostic model was evolved that included 10 variables. The model showed good discrimination [(receiver operating characteristic (ROC) area=0.91) and correctly classified 88.30% of patients. The variables significantly associated with mortality were coma odds ratio (OR)=9.8], oliguria (OR=4.9), jaundice (OR=3.7), hypotension (OR=3.1), assisted ventilation (OR=2.3), hospital acquired ARF (OR=2.3), sepsis (OR=2.2), and hypoalbuminemia (OR=1.7). Age and male gender were included in the model as they are clinically important. The score was validated in the same sample by boot strapping. It was also validated in a prospective sample of 194 patients. The model was calibrated by the Hosmer-Lemeshow goodness-of-fit test. It was compared with two generic illness scores and one specific ARF score and was found to be superior to them. The model was verified in different subgroups of ARF like hospital acquired, community acquired, intensive care settings, nonintensive care settings, due to sepsis, due to nonsepsis etiologies, and showed good predictability and discrimination.