Background: An accurate prognostic stratification is essential for optimizing the clinical management and
treatment decision-making of patients with chronic heart failure (HF). Among the best available models,
we used the Cardiac and Comorbid Conditions HF (3C-HF) Score, to predict all-cause mortality in patients
with CHF. Methods: we selected and characterized the subgroup of patients at very high risk with the worst
mid-term prognosis belonging to the highest decile of 3C-HF score with the aim to assess predictors of survival
in subjects with an expected probability of 1-year mortality near to 45%.
Methods and results: We recruited 1777 consecutive chronic HF patients at 3 Italian Cardiology Units. Median age
was 76±10 years, 43% were female, and 32% had preserved ejection fraction. Subjects belonging to the highest decile
of 3C-HF score were 246 (13.8% of total population). During a median follow-up of 21 [12–40] months, 110 of
these patients (45%) survived and 136 (55%) died. The variables that contributed to survival prediction emerged
by Cox regression multivariate analysis were the lower degree of renal dysfunction and higher body mass index.
Conclusions: The prognostic stratification of chronic HF patients allows in daily practice to select patients at different
risk for death and identify prognosticators of survival in outliers at very high risk of death. The reasons why these
patients outlive the matching part of subjects who expectedly die are related to the maintenance of a satisfactory
renal function and body mass index.
