A Multivariate Model for Predicting Mortality in Patients with Heart Failure and Systolic Dysfunction

Background: Heart failure is a leading cause of morbidity and mortality, but there are no reliable models based on readily available clinical variables to predict outcomes in patients taking angiotensin-converting enzyme (ACE) inhibitors.

Methods: A multivariate statistical model to predict mortality was developed in a random sample (n = 4277 patients [67%]) of the 6422 patients enrolled in the Digitalis Investigation Group trial who had a depressed ejection fraction (<or=45%), were in sinus rhythm, and were taking ACE inhibitors. The model was then validated in the remaining 2145 patients.

Results: Total mortality in the derivation sample was 11.2% (n = 480) at 12 months and 29.9% (n = 1277) at 36 months. Lower ejection fraction, worse renal function, cardiomegaly, worse functional class, signs or symptoms of heart failure, lower blood pressure, and lower body mass index were associated with reduced 12-month survival. This model provided good predictions of mortality in the verification sample. The same variables, along with age and the baseline use of nitrates, were also predictive of 36-month mortality.

Conclusion: Routine clinical variables can be used to predict short- and long-term mortality in patients with heart failure and systolic dysfunction who are treated with ACE inhibitors.