CONTEXT: Estimation of mortality risk in patients hospitalized with acute decompensated heart failure (ADHF) may help clinicians guide care. OBJECTIVE: To develop a practical user-friendly bedside tool for risk stratification for patients hospitalized with ADHF. DESIGN, SETTING, AND PATIENTS: The Acute Decompensated Heart Failure National Registry (ADHERE) of patients hospitalized with a primary diagnosis of ADHF in 263 hospitals in the United States was queried with analysis of patient data to develop a risk stratification model. The first 33,046 hospitalizations (derivation cohort; October 2001-February 2003) were analyzed to develop the model and then the validity of the model was prospectively tested using data from 32,229 subsequent hospitalizations (validation cohort; March-July 2003). Patients had a mean age of 72.5 years and 52% were female. MAIN OUTCOME MEASURE: Variables predicting mortality in ADHF. RESULTS: When the derivation and validation cohorts are combined, 37,772 (58%) of 65,275 patient-records had coronary artery disease. Of a combined cohort consisting of 52,164 patient-records, 23,910 (46%) had preserved left ventricular systolic function. In-hospital mortality was similar in the derivation (4.2%) and validation (4.0%) cohorts. Recursive partitioning of the derivation cohort for 39 variables indicated that the best single predictor for mortality was high admission levels of blood urea nitrogen (> or =43 mg/dL [15.35 mmol/L]) followed by low admission systolic blood pressure (<115 mm Hg) and then by high levels of serum creatinine (> or =2.75 mg/dL [243.1 micromol/L]). A simple risk tree identified patient groups with mortality ranging from 2.1% to 21.9%. The odds ratio for mortality between patients identified as high and low risk was 12.9 (95% confidence interval, 10.4-15.9) and similar results were seen when this risk stratification was applied prospectively to the validation cohort. CONCLUSIONS: These results suggest that ADHF patients at low, intermediate, and high risk for in-hospital mortality can be easily identified using vital sign and laboratory data obtained on hospital admission. The ADHERE risk tree provides clinicians with a validated, practical bedside tool for mortality risk stratification.