AHM · Acorai Heart Monitor - Non-invasive multi-sensor device for heart failure monitoring

Heart Failure is a progressive clinical syndrome with multiple etiologies. It is associated with nearly three million hospitalizations in the EU annually, representing 30% of all readmissions, and accounts for up to 5% of the entire healthcare expenditure. Despite modern medical treatment, HF is a life-threatening and debilitating condition with a poor prognosis. The Acorai Heart Monitor (AHM) is a monitoring device designed to detect and estimate hemodynamic parameters non-invasively. AHM is a handheld electronic device that features multiple sensors to collect data from 4 sensor types based upon patented the SAVE sensor system to estimate the absolute pressure of the heart failure patient's cardiac system. The sensor data are processed within the medical device with machine learning (ML) methods, the results are then displayed to be read by a qualified healthcare professional (HCP).Delivering Intracardiac pressure to HCPs to guide the treatment of patients with HF leads to improved quality of life. It lowers the frequency of hospital admissions, yet all current tools require an invasive procedure. AHM is a multi-patient non-invasive device that would dramatically disrupt the paradigm of HF treatment and lead to significant cost saving in the EU whilst improving the overall prognosis of patients who suffers from HF. With the growing prevalence of HF globally the AHM is a tool with applicability in multiple workflows and represents a first-of-its-kind device to revolutionise HF management.AHM has already been validated in 300 patients in a Swedish proof of concept study and now needs the support to bring this device to market and collect data via a large multi-national study to prove its effectiveness. The AHM is fully patent protected with three granted patent families and four patents pending covering the core components of the device and its clinical application. This is additionally protected by large proprietary datasets and a unique ML model.