Heart failure is currently a leading cause of death, hospitalization, and disability in the world, with more than six million people suffering from HF in the United States alone. HF has a major negative impact on patients’ quality of life. It is a difficult disease to treat, requires a strict medication regimen, periodic visits to the doctor, and often results in repeated hospitalizations with about 27% of patients readmitted to hospital within 30 days of discharge after the initial treatment. Many of HF patients never fully regain their pre-hospitalization quality of life and, in fact, experience deterioration of health condition after every subsequent rehospitalization.

While being a heavy burden to patients, HF also represents a very significant economic cost to the healthcare system. With about 1 million hospitalization per year, the cost of treating HF in the United States is currently close to $40 billion and is expected to double by the year 2030. HF-related expenses account for approximately 2% of all healthcare expenditure in the United States with more than $17 billion spent annually on unplanned hospital readmissions.

Corsigna home monitoring solution implemented as a cornerstone of an integrated HF management program aims to improve clinical outcomes for heart failure patients and to reduce the hospital readmission rates. Our solution combines data from multiple sensors with sophisticated machine learning analysis techniques to accurately and reliably derive the cardiac function.

Currently reliable measurement of cardiac function is possible only in hospital setting. Home monitoring and analysis of cardiac function allows for early detection of onset of decompensation with sufficient lead time to allow intervention by the clinical team before a rehospitalization occurs.

While other proposed solutions to monitor cardiac function in home setting involve costly surgical procedures to implant sensors into the patient’s heart, Corsigna solution is non-invasive and economical: it does not require any surgical procedures and uses data from commonly available sensors.