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

Antonio Leon-Justel1, Jose I Morgado Garcia-Polavieja2, Ana Isabel Alvarez-Rios3

  • 1Macarena University Hospital, Dr. Fedriani nº3, 41009, Seville, Spain. antonio.leonj.sspa@juntadeandalucia.es.

Insights

A personalized follow-up for heart failure (HF) patients significantly reduced hospital readmissions and emergency visits. This approach also improved quality of life and resulted in substantial cost savings for HF care.

Area of Science:

  • Cardiology
  • Health Services Research

Background:

  • Heart failure (HF) presents a significant global health and economic burden.
  • Personalized management strategies are crucial for improving outcomes in patients with heart failure with reduced left ventricular ejection fraction (HFrEF).

Purpose of the Study:

  • To assess the impact of a personalized follow-up protocol on patient outcomes and healthcare costs in HF management.
  • To evaluate the effectiveness of a risk-stratification and personalized care model for HF patients.

Main Methods:

  • A before-and-after interventional study design involving 192 HF patients.
  • Primary endpoint: reduction in HF-related hospital readmissions.
  • Secondary endpoints: emergency department (ED) visits, quality of life (NYHA score), and cost-analysis.

Main Results:

  • Hospital admission rates decreased by 19.8%, with total admissions reduced by 32 and length of stay by 7 days.
  • Emergency department visits fell by 44%, and 31% of patients showed improved functional class.
  • Significant cost savings of €72,769 per patient were achieved, totaling €139,717.65 for the group over one year.

Conclusions:

  • Personalized follow-up care for HF patients yields significant improvements in clinical outcomes.
  • The intervention led to substantial reductions in hospital readmissions and associated healthcare costs.
  • Focusing on high-risk HF cohorts is essential for minimizing readmission risks and optimizing resource allocation.
Abstract

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