Andrew Xanthopoulos
Department of Cardiovascular Medicine, Heart and Vascular Institute, Kaufman Center for Heart Failure, Cleveland Clinic, Cleveland, OH, USA
Konstantinos Tryposkiadis
Independent Biostatistician, Athens, Greece
Gregory Giamouzis
Department of Cardiology, University General Hospital of Larissa, Larissa, Greece
Wajahat Lodhi
Respiratory Institute, Critical Care Medicine, Cleveland Clinic, Cleveland, OH, USA
Randall C. Starling
Department of Cardiovascular Medicine, Heart and Vascular Institute, Kaufman Center for Heart Failure, Cleveland Clinic, Cleveland, OH, USA
John Skoularigis
Department of Cardiology, University General Hospital of Larissa, Larissa, Greece
Filippos Triposkiadis
Department of Cardiology, University General Hospital of Larissa, Larissa, Greece
Περίληψη
Morbidity and mortality rates in heart failure (HF) remain unacceptably high, even after the implementation of the current best available treatments. Hence, there is an unmet need for timely identification and management of “high risk” HF patients. From a first glance, the idea of developing risk scores, which may serve as useful prognostic tools, seems appealing. However, a number of limitations stemming from their multivariable nature such as the need for sophisticated calculators’ use, the limited availability of several variables and the high cost, make the use of risk scores on daily clinical practice challenging. On top of this, there is a growing evidence that most prediction models do not perform well when they are externally validated. This review discusses the main risk scores both in acute (AHF) and chronic HF (CHF), highlights their strengths and limitations and summarizes the future perspectives of predictive models.
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