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Identify high-risk HF patients, guide them into care programs, and reduce hospitalizations and costs.
Heart failure (HF), characterized by the heart's impaired ability to pump blood, affects 6.5 million U.S. adults and leads to fluid retention and shortness of breath (2). Direct medical costs for HF are expected to reach $70 billion by 2030 (1), with 33% of Medicare spending dedicated to treating this condition (4). After diagnosis, half of the patients survive only five years, and one in ten make it to ten years (3)(5). Hospitalizations for HF, often preventable, are a key factor in these high costs and constitute the primary cause of hospital admissions among patients over 65, with a significant rate of 30-day readmissions (6)(7). Addressing this issue is critical to improving patient outcomes and reducing healthcare expenditures.
A predictive model called “HeartSafe AI” has been designed to predict readmissions for patients with heart failure. This model uses clinical data such as blood pressure and cholesterol levels to predict the occurrence of heart failure exacerbations, helping healthcare providers deliver targeted care aimed at reducing the likelihood of subsequent hospitalizations.
User person: Cardiologists, Cardiac Rehabilitation Specialists, Cardiology Physician Assistants.
The selection of variables and their inclusion in the model is based on statistical reports on heart disease and stroke (1), as well as research on the financial implications (2) and the epidemiology of heart failure (3). Cost analyzes (4), prevention of hospital admissions for decompensated heart failure (5), rehospitalization rates (6), and the importance of self-care in heart failure (7) contribute to a deep understanding of the factors that lead to readmissions.
