B. L. Siracuse1, J. Sond1, K. Mahendraraj1, C. S. Lau1,2, R. S. Chamberlain1,2,3 1Saint Barnabas Medical Center,Surgery,Livingston, NJ, USA 2St. George’s University School Of Medicine,St. George’s, St. George’s, Grenada 3Rutgers University,Surgery,Newark, NJ, USA
Introduction: Congestive heart failure (CHF) affects over 5 million Americans, innumerable surgical patients, and accounts for over 1 million hospitalizations annually. The Affordable Care Act established the Hospital Readmission Reduction Program (HRRP) requiring the Centers for Medicare and Medicaid Services (CMS) to reduce payments to hospitals with excess readmissions as of October 2012. Identifying surgical and non-surgical patients at greatest readmission risk should permit the adoption of risk preventive strategies prior to admission or surgical therapy. This study sought to develop a predictive readmission nomogram that could identify CHF patients at higher readmission risk and permit the implementation of readmission risk reduction strategies.
Methods: Discharge data on 642,448 patients from New York and California (derivation cohort) and 365,359 patients from Washington and Florida (validation cohort) were abstracted from the State Inpatient Database (SID), a part of the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality (2006-2011). Demographic and clinical characteristics of CHF patients readmitted were abstracted including age, gender, race, and medical comorbidities. The Readmission After Heart Failure (RAHF) Score scale was developed to predict readmission risk.
Results: Readmission rates for males and females were 10.04% and 8.83% for the derivation cohort and 9.69% and 8.62% for the validation cohort, respectively. Factors determined to be associated with increased risk of readmission after CHF hospitalization included age <65 (OR 1.14; 95% CI, 1.11-1.18), male gender (OR 1.13; 95% CI, 1.11-1.15), 1st income quartile (OR 1.09; 95% CI, 1.07-1.12), African American race (OR 1.34; 95% CI, 1.30-1.37), race other than African American or Caucasian (OR 1.10; 95% CI, 1.07-1.12), Medicare (OR 1.33; 95% CI, 1.29-1.38), Medicaid (OR 1.72; 95% CI, 1.65-1.78), self-pay/no insurance (OR 1.14; 95% CI, 1.07-1.22), drug abuse (OR 1.65; 95% CI, 1.57-1.73), renal failure (OR 1.37; 95% CI, 1.34-1.39), chronic pulmonary disorder (OR 1.15; 95% CI, 1.13-1.17), diabetes (OR 1.12; 95% CI, 1.10-1.14), depression (OR 1.08; 95% CI, 1.05-1.12), and fluid and electrolyte disorder (OR 1.03; 95% CI, 1.01-1.05). The RAHF Scale was created. When it was applied to the validation cohort, it explained 96% of readmission variability within the cohort.
Conclusions: The RAHF Scale reliably predicts an individual patient’s 30 day CHF readmission risk based on specific factors present at initial admission. Risk stratification models, such as the RAHF Scale, can identify high-risk surgical and non-surgical patients thereby permitting the implementation of patient-specific readmission-reduction strategies to improve patient care, reduce surgical complications, as well as reducing readmissions and healthcare expenditures.
