Abstract
BACKGROUND: Chronic heart failure (CHF), which affects >5 million Americans, accounts for >1 million hospitalizations annually. As a part of the Hospital Readmission Reduction Program, the Affordable Care Act requires that the Centers for Medicare and Medicaid Services reduce payments to hospitals with excess readmissions. This study sought to develop a scale that reliably predicts readmission rates among patients with CHF. METHODS: The State Inpatient Database (2006-2011) was utilized, and discharge data including demographic and clinical characteristics on 642,448 patients with CHF from California and New York (derivation cohort) and 365,359 patients with CHF from Florida and Washington (validation cohort) were extracted. The Readmission After Heart Failure (RAHF) scale was developed to predict readmission risk. RESULTS: The 30-day readmission rates were 9.42 and 9.17% (derivation and validation cohorts, respectively). Age <65 years, male gender, first income quartile, African American race, race other than African American or Caucasian, Medicare, Medicaid, self-pay/no insurance, drug abuse, renal failure, chronic pulmonary disorder, diabetes, depression, and fluid and electrolyte disorder were associated with higher readmission risk after hospitalization for CHF. The RAHF scale was created and explained the 95% of readmission variability within the validation cohort. The RAHF scale was then used to define the following three levels of risk for readmission: low (RAHF score <12; 7.58% readmission rate), moderate (RAHF score 12-15; 9.78% readmission rate), and high (RAHF score >15; 12.04% readmission rate). The relative risk of readmission was 1.67 for the high-risk group compared with the low-risk group. CONCLUSION: The RAHF scale reliably predicts a patient's 30-day CHF readmission risk based on demographic and clinical factors present upon initial admission. By risk-stratifying patients, using models such as the RAHF scale, strategies tailored to each patient can be implemented to improve patient outcomes and reduce health care costs.