a prospective, observational, single centre study to assess the feasibility of the data harvested by the acorai intracardiac pressure monitoring (icpm) system in predicting prognosis in patients discharged from hospital after treatment for acute decompensated heart failure

Conditions: Heart Failure; Decompensated Heart Failure

Summary: acorai is developing the acorai heart monitor, a handheld electronic device for monitoring pressures inside the heart in a non-invasive manner, by placing the device on the chest of a patient. currently these intracardiac pressures are measured in an invasive way, during a right heart catheterisation procedure. this procedure carries risks to the patient. there is a clinical need for a non -invasive, easy to use, tool to monitor patients that suffer from heart failure. the study aim is to use the acorai device to collect the intracardiac pressure measurements from patients admitted to hospital with heart failure and who are awaiting discharge home. using the data, we will assess the feasibility of the acorai derived cardiac output measurements, and assess what data best predicts survival, death, or major adverse cardiac events (mace)

there is a need for the development of a tool that can help to identify those hf patients at risk of deterioration. the ideal tool would be quick, easy to use, non-invasive, inexpensive and can be performed by any healthcare practitioner or patient themselves (i.e. point of care testing). the acorai heart monitor device is an extended smartphone case equipped with a combination of microphonic sensors, inertial measurement units, electrocardiographic sensors and photoplethysmography sensor, in a configuration that is designed to enhance low frequency sound and seismic signals pertaining to intracardiac pressure. each sensor technology has been validated to minimize the risk to the patient. sensor data is collected to provide clinicians with the patients intracardiac pressures. this is performed by placing the monitoring device on the chest, with a small smart-wrist watch, for a period of five minutes in the supine position. a feasibility study showed acorai device cardiac output measurements correlate highly with the gold standard right heart catheterisation (rhc) cardiac output measurement. intracardiac pressure monitoring is important in hf patients. studies also show that raised right atrial pressure and pulmonary capillary wedge pressure are predictors of worse outcomes with increased risk of death, cardiovascular hospitalisation or transplantation. clinicians can only gather intracardiac pressure measurements by undertaking a rhc procedure with fluoroscopy guidance. however, this is an invasive procedure, with radiation exposure, that carries risks including bleeding, stroke, infection and pneumothorax. this is a prospective, observational, single-site, non-randomised, non-significant risk, single arm, clinical investigation designed to collect non-invasive cardiac output measurements from the acorai heart monitor device the objective of the study is to assess the feasibility of the acorai icpm system to predict survival and hospitalisation at 1 year in hospitalised heart failure patients deemed medically fit for discharge.

Eligibility criteria:
inclusion criteria: subjects must meet all the following criteria to be eligible for participation in the clinical investigation 1. subjects medically fit for discharge following an admission to hospital with decompensated heart failure requiring intravenous diuretic therapy 2. subject is willing and physically able to comply with the specified evaluations as per clinical investigation plan, as assessed by the investigator. 3. subject is able to provide written consent to participate in the study exclusion criteria: 1. pregnancy 2. discretionary exclusion when, in the opinion of the investigator, the inclusion of a potential subject is not in their best interest or not in the interest of compliant performance of the clinical trial

Keywords: heart failure decompensated heart failure acorai artificial intelligence machine learning generisation machine learning generization