No maggic in predicting heart failure readmissions.

Document Type

Conference Proceeding

Publication Date


Publication Title

J Am Coll Cardiol


Background: Readmission for acute decompensated heart failure represents a significant burden to healthcare in the United States with nearly a quarter of patients being readmitted by 30 days. Here, we evaluate the ability of two robust risk tools derived from ambulatory cohorts, the MAGGIC score and Seattle Heart Failure Model (SHFM), to predict 30-day readmission in an urban heart failure population. Methods: This single center retrospective cohort study analyzed a total of 1172 patients admitted for acute systolic or combined systolic-diastolic heart failure. All patients were assigned a discharge NYHA class 2 status. The primary endpoint was readmission with primary diagnosis of decompensated heart failure. Patients with end-stage renal disease, isolated diastolic heart failure, history of or awaiting heart transplant, and ventricular assist devices were excluded. Data from all variables identified in the MAGICC and SHFM risk scores were abstracted from this cohort, as well as BNP and renal function. The association of SHFM or MAGICC variables and 30-day readmission risk was evaluated with univariate binary logistic regression models. Results: Of 1172 patients included in the study, there were 245 readmissions. 43 patients who expired or placed in hospice at the index encounter were removed prior to analysis, leaving 1129 in the final analysis. The average age was 69.3 years, with 43% female and 49% white. Neither risk tool, SHFM (OR 0.96, 95% CI 0.79-1.17, p=0.7) or MAGGIC (OR 1.0, 95% CI 0.98-1.02, p = 0.892), demonstrated utility in stratifying hospitalized patients at discharge for risk of readmission. Conclusions: The MAGGIC and Seattle Heart Failure Model are two robust risk models of all-cause mortality in ambulatory heart failure patients but fail to predict 30-day readmission in acute decompensated heart failure. Assessing readmission risk in a heart failure patient is complex and likely dependent on many patient-level factors that are not readily quantifiable in current risk-prediction tools. Traditional risk factors identified for mortality may not be sufficient in predicting readmission risk and future endeavors may benefit from evaluating socioeconomic influences.





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