Within 30 days of hospital discharge, heart failure (HF) readmission rates nationally accumulate to more than 20%. Due to this high rate of unplanned re-hospitalization, predictive models are needed to identify patients who pose the highest readmission risk.Objective:
To evaluate the diagnosis and timing and to identify patient and clinical characteristics associated with 30 day readmissions among HF patients.Methods:
A retrospective analysis of electronic health records was conducted to study HF admissions during the period October 2008 to November 2014. Patients with a primary discharge diagnosis consistent with HF were included. Descriptive statistics were used to compare the readmitted and non-readmitted cohorts. Logistic regression was used to develop a predictive model to determine patient and clinical variables associated with 30 day readmission.Results:
Characteristics of the study cohort (n = 2420) are: a mean age of 72, predominantly male (55%), white (55%), currently not employed (91%), and utilizing Medicare as a payer (68%). Overall, 42% were married. Over the study time period there were 394 (16.3%) 30 day readmissions after 2420 hospitalizations. The 3 most common reasons for readmission were HF (36.0%), renal disorders (8.4%), and other cardiac diseases (6.9%). Analysis showed that 11.9% of patients readmitted during days 0–3, 15.2% during days 4–7, 31.5% during days 8–15, and 41.4% during days 16–30. The final multivariate predictive model included 5 variables that were associated with an increased risk for 30-day readmission: employment status as retired or disabled, > 1 emergency department visit in the past 90 days, length of stay >5 days during index visit, and a BUN value > 45 mg/dL.Conclusion:
This study provides a deeper understanding of patient and clinical characteristics that are associated with readmission in HF. Evaluation of these characteristics will provide additional information to guide strategies meant to reduce HF readmission rates.