Prognosis

The evaluation of a patient would be incomplete without an initial and periodic assessment of short- and long-term prognosis. However, the likelihood of survival can be determined reliably only in populations and not in individual patients. Numerous factors have been used as prognostic indicators, including demographics (age, sex, race), symptoms (New York Heart Association classification), comorbidities (hypertension, diabetes, cachexia, anaemia, and renal and hepatic dysfunction), and objective clinical parameters (e.g., ejection fraction, left ventricular size, volume, mass and shape, exercise capacity, and serum levels of sodium, noradrenaline [norepinephrine], renin, B-type natriuretic peptide, uric acid, angiotensin II, aldosterone, tumour necrosis factor-alpha, endothelin). Multi-variate analysis of these variables has helped to identify the most significant predictors of survival, and prognostic models have been developed and validated.[276][277][278][279][280][281][282][283]​ However, all existing models to predict the risk of death or need for urgent transplantation have features that may limit their applicability. Haemoglobin A1c was also found to be an independent progressive risk factor for cardiovascular death, hospitalisation, and mortality, even in non-diabetic patients.[284]

The most comprehensive prognostic model is the Seattle Heart Failure Model. The Seattle Heart Failure Model Opens in new window This model has been implemented as an interactive programme that employs the Seattle Heart Failure Score to estimate mean, 1-, 2-, and 5-year survival and the benefit of adding medicines and/or devices for an individual patient.[278]

Despite standard medical therapy the survival for patients with end-stage HF is poor.

Despite optimal medical therapy including cardiac re-synchronisation therapy, only 65% of patients in NYHA class 4 are alive at a mean follow-up of 17 months.[285]

The 5-year survival in patients with stage D HF is only 20%.[286]

One study looking at comorbidities in two large cohorts of patients (one from 2002 and one from 2017) found that in the earlier cohort, cerebrovascular disease, diabetes mellitus and CKD were independent predictors for adverse outcome (HF hospitalisation and all-cause mortality during a total follow-up of 1.5 years), and atrial fibrillation/flutter showed a non-significant trend towards poor outcome. In the later 2017 cohort, the independent predictors were anaemia, obesity and COPD, with CKD showing a trend towards poor outcome. Other chronic conditions included in the model were not independently associated with prognosis; however, an overall higher comorbidity burden was associated with an increased risk of HF hospitalisation or all-cause mortality.[8]​

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