Cardiovascular risk stratification after acute coronary syndrome (ACS)
Patient risk assessment following life-threatening medical events
The ability to extract predictive features from physiological signals is important because millions of acute coronary events occur each year in the United States alone, resulting in 1 out of 6 deaths in the US. Further, 8–19% of those Americans who had a heart attack will die within 12 months of discharge from the hospital. Unfortunately, existing risk score matrices, such as Global Registry of Acute Coronary Events (GRACE) and Thrombolysis In Myocardial Infarction (TIMI), only identify a subset of high-risk patients, and a significant number of deaths will occur in populations that are not traditionally considered to be high risk. Hence, there is a need for tools to discriminate risks further.
This invention pertains to a novel feature extraction and machine learning procedure that can be used in the prediction of adverse medical outcomes. The accuracy of predictions made by computers using machine learning is predicted on the computer’s ability to extract the right information from the data. More specifically, this invention extracts features from the electrocardiogram (ECG) in a frequency domain that adjusts for patient heart. This extraction procedure and machine learning is then used to derive improved versions of state-of-the-art electrocardiographic (ECG) risk metrics for better cardiovascular risk stratification after an acute coronary syndrome (ACS). We demonstrate that our method works on different characteristics of interest, such as ECG morphology and heart rate. The methods presented in this invention have potential utility in predicting the occurrence of other adverse outcomes and using other quasi-periodic physiological signals.
Improved cardiovascular risk stratification after an acute cardiac event
Applicable in predicting other adverse outcomes and using other signals