Current status
Heart failure affects approximately 1-2% of adult population in developed countries. In the population over 70 years, it is even more common with prevalence over 10%. Despite significant progress in both diagnostics and treatment, associated morbidity and mortality remains high. Depending on patient condition, 12-month all-cause mortality reaches 17% and up to 44% of patients require hospitalization within 12 months. Treatment of heart failure is complex and includes lifestyle changes and several pharmacological, interventional, and surgical options. Non-invasive and widely available diagnostic tool capable of providing information about cardiac pressures would be a great advantage in management of HF patients.
Our solution
Heart failure is characterized by high filling pressures in the heart resulting in the accumulation of excess fluids in the body. This retention of fluids can manifest as swelling of lower limbs or fluid collection in the lungs called pulmonary edema. These complications often require hospitalization. Increased filling pressures precede the onset of symptoms by days or even weeks.
The ability to measure these pressures at home would allow patients to adjust their treatment accordingly and prevent the worsening of the symptoms.
Our solution is based on development of a mobile digital tool capable of indirect measurements of left ventricular filling pressure using a unique algorithm utilising state of the art machine learning methods.
The ultimate solution is to develop a mobile digital tool for patients with heart failure that is:
- Non-invasive, painless and widely available,
- Capable of monitoring left ventricular filling pressure,
- Allows patient-tailored therapy as the patient can adjust the dose of medication themselves,
- Contributes to reducing hospitalizations and improving survival and the quality of life.
Research goals
- To develop a mobile digital tool capable of determining left atrial filling pressure.
- To compare its predictive power with existing diagnostic tools on a large population of patients.
