Chronic coronary syndrome

Cardiovascular diseases, especially coronary heart disease (CHD), are the leading cause of mortality in the world.

Current status

 

Cardiovascular diseases, especially coronary heart disease (CHD), are the leading cause of mortality in the world. The main symptom of CHD is chest pain. However, chest pain may occur after a relatively long period of so-called silent ischemia (without clinical symptoms) preceded by diastolic and systolic dysfunction with the subsequent development of ECG changes. Mortality and morbidity of CHD can be effectively reduced by pharmacological and interventional treatment. The ability to identify patients with silent myocardial ischemia on a large scale in early asymptomatic stages of the disease would translate into dramatic improvement in health care and cost savings. We believe that thanks to digital innovation and new devices with the ability to monitor various biological signals already widely available today, we will be able to effectively screen vulnerable patients at risk of developing CHD.

 

Our solution

 

Our unique approach is to develop a smartphone application capable of identifying patients suffering from silent chronic coronary heart disease based on finger photoplethysmography (PPG).

 

Research goals

 

  • To develop a smartphone application capable of identifying patients suffering from silent chronic coronary heart disease based on PPG measurements.
  • To compare its predictive power with existing diagnostic tools on a large population of patients. 

 

BioGuard CCS (Chronic Coronary Syndrome) is a prospective observational study that aims to improve the diagnostic options for patients with coronary heart disease (CHD).

 

Patients undergoing exercise ECG test will have their finger PPG wave measured using a smartphone camera and our BioGuard CCS application. Cardiac ischemia causes systolic dysfunction and systolic dysfunction leads to a transient decrease in cardiac output, which is subsequently reflected in slight changes in the shape of the PPG pulse wave. Complex relationships between variables in the PPG waveforms affected by reduced cardiac output will be analyzed by using advanced machine learning algorithms.