Biomarkers-based personalized follow-up in chronic heart failure improves patient’s outcomes and reduces care associate cost

Abstract

Abstract Background Heart failure (HF) is a major and growing medical and economic problem, with high prevalence and incidence rates worldwide. Cardiac Biomarker is emerging as a novel tool for improving management of patients with HF with a reduced left ventricular ejection fraction (HFrEF). Methods This is a before and after interventional study, that assesses the impact of a personalized follow-up procedure for HF on patient’s outcomes and care associated cost, based on a clinical model of risk stratification and personalized management according to that risk. A total of 192 patients were enrolled and studied before the intervention and again after the intervention. The primary objective was the rate of readmissions, due to a HF. Secondary outcome compared the rate of ED visits and quality of life improvement assessed by the number of patients who had reduced NYHA score. A cost-analysis was also performed on these data. Results Admission rates significantly decreased by 19.8% after the intervention (from 30.2 to 10.4), the total hospital admissions were reduced by 32 (from 78 to 46) and the total length of stay was reduced by 7 days (from 15 to 9 days). The rate of ED visits was reduced by 44% (from 64 to 20). Thirty-one percent of patients had an improved functional class score after the intervention, whereas only 7.8% got worse. The overall cost saving associated with the intervention was € 72,769 per patient (from € 201,189 to € 128,420) and €139,717.65 for the whole group over 1 year. Conclusions A personalized follow-up of HF patients led to important outcome benefits and resulted in cost savings, mainly due to the reduction of patient hospitalization readmissions and a significant reduction of care-associated costs, suggesting that greater attention should be given to this high-risk cohort to minimize the risk of hospitalization readmissions.

Keywords

• Heart failure
• Personalized medicine
• Patient value
• Patient outcomes
• Budget impact
• Biomarkers