Predicting poor outcomes in heart failure

Abstract

Background: Health plans must prioritize disease management efforts to reduce hospitalization and mortality rates in heart failure patients.

Methods and results: We developed a risk model to predict the 5-year risk of mortality or hospitalization for heart failure among patients at a large health maintenance organization. We identified 4696 patients who had an echocardiogram and a heart failure diagnosis from 1999 to 2004.We observed a 56% five-year risk of hospitalization for heart failure or death (95% confidence interval, 54% to 58%). The hazard ratios for echocardiogram data contributed statistically significantly to the model, but echocardiogram findings did not improve our ability to predict risk accurately once we had accounted for demographic characteristics and clinical findings. A more complex model demonstrated a modest capacity to accurately predict risk. Our risk model discriminated the highest- and lowest-risk patients with limited success-the observed risk was 3 times higher in the highest risk quintile, compared with the lowest-risk quintile.

Conclusions: Using data available from electronic health records, we developed a series of risk-prediction models for poor outcomes in patients with heart failure. We found that a relatively simple model is as effective as a more complex model, but that all the models predict with only modest accuracy. Until better prediction variables are available for heart failure patients, our prediction model may be valuable for prioritizing centralized disease management program efforts by stratifying patients according to their absolute risk of poor outcomes.

Figure 1

Calibration Plot for Model 2 (Panel A) and Model 3 (Panel B). The curves show the observed risk (solid lines) and predicted risk (dotted lines) of outcome (death or heart failure hospitalization) according to quintiles of predicted risk based on the risk score.