Prognostic Assessment in Heart Failure Patients: An Unresolved Task
Author(s) -
Ana Carolina Alba
Publication year - 2018
Publication title -
revista argentina de cardiología
Language(s) - English
DOI - 10.7775/rac.86.5.14045
Heart failure (HF) represents the final stage of different diseases as hypertension, coronary artery disease, myocarditis, alcoholic cardiomyopathy and Chagas’ disease. Nowadays, the treatments of these conditions have improved; yet, many patients develop HF later in life with more number of comorbid disorders. Several risk factors modify the outcome of patients with HF and usually coexist at varied proportions. All these circumstances determine that the prognostic assessment of a particular HF patient is challenging. The human mind has limitations to bring together all that information and translate it into an accurate prognostic assessment, even in the case of well-informed physicians. Our limitation as physicians to establish an accurate prognosis in many disciplines has been recognized for over 70 years. (1-4) In order to overcome this limitation, several researchers have developed many predictive models over the past 30 years to estimate the risk of future adverse events in HF patients by combining a limited number of prognostic markers. Some of these predictive models have been validated in multiple populations and have demonstrated different performance. (5) In this issue of the Argentine Journal of Cardiology, Chirino et al. (6) have made an interesting analysis about the performance of two models to predict mortality in 704 HF patients in Argentina: the Cardiac and Comorbid Conditions Heart Failure (3C-HF) score and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score. These scorying systems were calculated using variables measured at hospital discharge or during an outpatient visit. The authors reported that both models demonstrate moderate ability to discriminate events and adequate calibration based on statistical methods. Some important points were highlighted during the discussion. Discrimination capacity or discrimination power is the ability of a prognostic model to differentiate between groups of patients depending on whether they
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