State-of-the-art analysis of electrocardiogram findings in sudden cardiac death.

Abstract

Sudden cardiac death (SCD) is a significant public health issue, and efforts to prevent it have involved the analysis of various modalities, including echocardiography, cardiac CT, cardiac MRI, genetic testing and ECG. The ECG, invented >100 years ago, is the oldest diagnostic tool among these examinations. Left ventricular hypertrophy and QT prolongation were first identified as risk markers for SCD in the 1960s and 1970s. However, since the beginning of the 21st century, advances in digitalised ECG data have unveiled various additional important findings. In vitro experimental studies have also contributed to the discovery of these new markers. Newly proposed markers include the fragmented QRS complex, the interval between the peak and the end of the T wave and J waves. Many studies have validated the clinical significance of these new ECG markers in predicting SCD risk. Recently, artificial intelligence (AI) has been employed to analyse ECG data to identify the high-risk populations. While the results of AI studies are not yet sufficient for routine clinical practice, ongoing advancements are expected to improve their accuracy in the near future.