Heart failure (HF) is a significant cause of morbidity, mortality, and health care expenditures. Patients hospitalized with HF are at particularly high risk for mortality. The mortality rates reported for patients hospitalized with HF, although high, can vary significantly. There are a large number of individual variables that are predictive of prognosis in patients hospitalized with HF. Investigators have developed and validated clinical risk models to allow health care providers to more reliably identify HF patients at lower, intermediate, and higher risk for mortality based on admission patient characteristics, vital signs, physical examination findings, laboratory and diagnostic study results, and biomarkers. Use of clinical risk prediction tools may be helpful in triaging patients hospitalized with HF and guiding medical decision making. This article discusses the mortality predictors and risk stratification models for patients hospitalized with HF, and provides a perspective on the value of integrating these risk tools into clinical practice.