Derivation and Validation of a 30-Day Heart Failure Readmission Model

• We examined a real-world cohort of patients discharged with heart failure.
• From this data, we derive and validate a heart failure readmission model.
• Our model discriminates other widely used models with fewer data points.
• Local factors, including zip code, remain important in patients' readmission risk assessments.

In 2006, there were >1 million hospital admissions for heart failure (HF), and the estimated cost to the United States in 2009 was >$37.2 billion. Better models to target aggressive therapy to patients at the highest risk for readmission are clearly needed. We studied 3,413 consecutive admissions for HF based on discharge diagnosis codes from October 2007 to August 2011 from a single academic center. We randomly generated derivation and validation sets in a 3:1 ratio. We used generalized estimating equations to develop our models, accounting for repeated hospitalizations and the Hosmer-Lemeshow test to examine model calibration. The 30-day readmission rate was 24.2% in the derivation set. Of 25 candidate variables, the best fitting model included creatinine, troponin, hematocrit, and hyponatremia at discharge; race; zip code of residence; discharge hour; and number of hospitalizations in the previous year. Insignificant variables included intravenous diuretic use on day of discharge, discharge service, diabetes, atrial fibrillation, age, and gender. The risk of 30-day readmission increased with increasing decile of predicted risk in both the validation and derivation cohorts. The area under the receiver operating characteristic curve for the model was 0.69 in the derivation set and 0.66 in the validation set. In conclusion, we derived and validated a simple model relating discharge-specific characteristics at risk of 30-day readmission. Application of this approach may facilitate targeted intervention to reduce the burden of rehospitalization in patients with HF, but our results suggest that the best readmission models may require incorporation of both clinical and local system factors for optimal prediction.