Predictors of 30-Day Heart Failure Readmissions: A Retrospective Logistic Regression Analysis

Background Hospital readmissions remain a major quality and cost concern in healthcare. Heart failure (HF) is one of the leading causes of 30-day readmissions among Medicare beneficiaries. Identifying modifiable predictors of readmission can guide nursing-led interventions that strengthen transitional care and improve patient outcomes. Objective Identifying predictors of increased 30-day readmissions may have important implications for designing interventions to improve patient outcomes and reduce costs. This study's objective was to retrospectively identify predictors of 30-­day recurrent ED visits among Medicare patients diagnosed with heart failure (HF). Methods A retrospective quantitative chart review was conducted for Medicare patients aged 65 years and older who were readmitted within 30 days of discharge during a six-month period from January to June. Logistic regression analysis examined the relationship between demographic, clinical, and behavioral variables and 30-day readmission. Variables included hypertension, diabetes, obesity, smoking, medication non-compliance, lack of follow-up, age, and gender. Results This retrospective study reports predictors independently associated with 30-­day readmissions for heart failure. Among 120 patients, 37% were readmitted within 30 days. Logistic regression revealed two significant predictors: medication non-compliance (OR = 3.64, 95% CI [1.42–9.30], p = .007) and lack of follow-up within 7 days (OR = 2.78, 95% CI [1.12–6.92], p = .027). Other clinical factors such as hypertension, diabetes, and obesity were not independently significant. Conclusion Medication non-compliance and lack of early follow-up remain strong predictors of heart failure readmissions. Both are modifiable through nursing interventions focused on education, follow-up coordination, and patient engagement. Nurse-led transitional care programs are critical to reducing readmissions and improving outcomes in this high-risk population. Relevance to Clinical Practice: Recognizing key readmission drivers enables nurses to enhance discharge planning, patient teaching, and care coordination for at-risk rural populations.