Predicting costs among medicare beneficiaries with heart failure

Abstract

Disease management programs that target patients with the highest risk of subsequent costs may help payers and providers control health care costs, but identifying these patients prospectively is challenging. We hypothesized that medical history and clinical data from a heart failure registry could be used to prospectively identify patients with heart failure most likely to incur high costs. We linked Medicare inpatient claims to clinical registry data for patients with heart failure and calculated total Medicare costs during the year after the index heart failure hospitalization. We defined patients as having high costs if they were in the upper 5% (>$76,500) of the distribution. We used logistic regression models to identify patient and clinical characteristics associated with high costs. Costs for 40,317 patients in the study varied widely. Patients in the upper 5% of the cost distribution incurred mean costs of $117,193 ± 55,550 during 1 year of follow-up compared to $17,086 ± 17,792 for the lower-cost group. Demographic and clinical characteristics associated with high costs included younger age and black race; history of anemia, chronic obstructive pulmonary disease, ischemic heart disease, diabetes mellitus, or peripheral vascular disease; serum creatinine level; and systolic blood pressure at admission. Mean 1-year Medicare costs for patients whom the model predicted would exceed the high-cost threshold were >2 times the costs for patients below the threshold. In conclusion, a model based on variables from clinical registries can identify a group of patients with heart failure who on average will incur higher costs in the first year after hospitalization.

Figure. Percent Predicted High-Cost vs Observed High-Cost Within Deciles