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
