Predicting Risk in Patients Hospitalized for Acute Decompensated Heart Failure and Preserved Ejection Fraction: The Atherosclerosis Risk in Communities Study Heart Failure Community Surveillance.

BACKGROUND: Risk-prediction models specifically for hospitalized heart failure with preserved ejection fraction are lacking. METHODS AND RESULTS: We analyzed data from the ARIC (Atherosclerosis Risk in Communities) Study Heart Failure Community Surveillance to create and validate a risk score predicting mortality in patients ≥55 years of age admitted with acute decompensated heart failure with preserved ejection fraction (ejection fraction ≥50%). A modified version of the risk-prediction model for acute heart failure developed from patients in the EFFECT (Enhanced Feedback for Effective Cardiac Treatment) study was used as a composite predictor of 28-day and 1-year mortalities and evaluated together with other potential predictors in a stepwise logistic regression. The derivation sample consisted of 1852 hospitalizations from 2005 to 2011 (mean age, 77 years; 65% women; 74% white). Risk scores were created from the identified predictors and validated in hospitalizations from 2012 to 2013 (n=821). Mortality in the derivation and validation sample was 11% and 8% at 28 days and 34% and 31% at 1 year. The modified EFFECT score, including age, systolic blood pressure, blood urea nitrogen, sodium, cerebrovascular disease, chronic obstructive pulmonary disease, and hemoglobin, was a powerful predictor of mortality. Another important predictor for both 28-day and 1-year mortalities was hypoxia. The risk scores were well calibrated and had good discrimination in the derivation sample (area under the curve: 0.76 for 28-day and 0.72 for 1-year mortalities) and validation sample (area under the curve: 0.73 and 0.71, respectively). CONCLUSIONS: Mortality after acute decompensation in patients with heart failure with preserved ejection fraction is high, with one third of patients dying within a year. A prediction tool may allow for greater discrimination of the highest risk patients. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00005131.

Duke Scholars

Author Lisa Wruck Biostatistics & Bioinformatics, Division of Biostatistics