Refining predictive models in critically ill patients with acute renal failure

Abstract

Mortality rates in acute renal failure remain extremely high, and risk-adjustment tools are needed for quality improvement initiatives and design (stratification) and analysis of clinical trials. A total of 605 patients with acute renal failure in the intensive care unit during 1989-1995 were evaluated, and demographic, historical, laboratory, and physiologic variables were linked with in-hospital death rates using multivariable logistic regression. Three hundred and fourteen (51.9%) patients died in-hospital. The following variables were significantly associated with in-hospital death: age (odds ratio [OR], 1.02 per yr), male gender (OR, 2.36), respiratory (OR, 2.62), liver (OR, 3.06), and hematologic failure (OR, 3.40), creatinine (OR, 0.71 per mg/dl), blood urea nitrogen (OR, 1.02 per mg/dl), log urine output (OR, 0.64 per log ml/d), and heart rate (OR, 1.01 per beat/min). The area under the receiver operating characteristic curve was 0.83, indicating good model discrimination. The model was superior in all performance metrics to six generic and four acute renal failure-specific predictive models. A disease-specific severity of illness equation was developed using routinely available and specific clinical variables. Cross-validation of the model and additional bedside experience will be needed before it can be effectively applied across centers, particularly in the context of clinical trials.