title: Clinical risk prediction tools in patients hospitalized with heart failure.
abstract: 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.
authors.affiliation: University of California, Los Angeles, CA, USA.
authors.forename: Gregg C
authors.initials: GC
authors.lastname: Fonarow
mesh_terms: D003661:Decision Support Techniques; D006333:Heart Failure / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy; D017052:Hospital Mortality; D006760:Hospitalization; D006801:Humans; D018579:Patient Selection; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors