Factors influencing unplanned readmission within 30 days in patients with heart failure and their predictive value: a prospective study.

Abstract

Background: Heart failure imposes a significant healthcare burden, with early unplanned readmissions post-discharge linked to poor outcomes. Identifying risk factors and their predictive value is crucial for targeted interventions.

Objective: To investigate gender differences in cumulative hazard function and the factors influencing 30-day unplanned readmissions in heart failure patients, and to compare their predictive value.

Methods: A prospective study of heart failure patients hospitalized in Beijing Hospital from October 2023 to March 2024. Patients' nutritional status was assessed using the Mini-Nutritional Assessment Scale Short Version (MNA-SF), frailty was evaluated using the Groningen Frailty Index (GFI), and the Appendicular Skeletal Muscle Mass Index (ASMI) was calculated. Multifactorial Cox regression analysis was conducted, and ROC curves were plotted for predictive modeling.

Results: A total of 121 heart failure patients (60.3% males), aged (69.87 ± 11.9) years were included. With a median follow-up duration of 30 days, 25 (20.7%) patients with readmission. COX regression analysis stratified by gender showed that age, regular smoking, nutritional status, left ventricular ejection fraction(LVEF), brain natriuretic peptide(BNP), GFI, and ASMI were independent predictors of readmission within 30 days in patients with heart failure (P < 0.050). ROC curve analysis showed that age, BNP, ASMI, smoking status, LVEF, nutritional status, and GFI individually as well as in combination predicted readmission within 30 days in patients with heart failure; the joint model performed optimally, with an AUC value reaching 0.877 (95%CI 0.801 ∼ 0.952, P < 0.001), with a sensitivity of 0.920 and a specificity of 0.729.

Conclusion: A multifactorial approach including age, BNP, ASMI, smoking status, LVEF, nutritional status, and GFI predicts 30-day readmission risk, offering a basis for clinical intervention strategies to improve patient outcomes.