Artificial intelligence to diagnose concealed long QT syndrome

Long QT syndrome (LQTS) is characterized by prolongation of the QT interval and is associated with an increased risk of sudden cardiac death. However, although QT interval prolongation is the hallmark feature of LQTS, approximately 40% of patients with genetically confirmed LQTS have a normal corrected QT (QTc) at rest. Distinguishing patients with LQTS from those with a normal QTc is important to correctly diagnose disease, implement simple LQTS preventive measures, and initiate prophylactic therapy if necessary.

In the latest issue of JAMA Cardiology, Bos et al examined whether a convolutional neural networks (CNNs) could identify patients with LQTS from raw ECG data and, in particular, identify patients with electrocardiographically concealed LQTS with normal QT intervals.

The goal of the study was to test the ability of the CNN to distinguish patients with LQTS from those who were evaluated for LQTS but discharged without this diagnosis, especially among patients with genetically confirmed LQTS but a normal QTc value at rest (referred to as genotype positive/phenotype negative LQTS, normal QT interval LQTS, or concealed LQTS).

2509 patients were included who had a definitive clinical and/or genetic diagnosis of type 1, 2, or 3 LQTS (LQT1, 2, or 3) or were seen because of an initial suspicion for LQTS but were discharged without this diagnosis. A multilayer convolutional neural network was used to classify patients based on a 10-second, 12-lead ECG, AI-enhanced ECG (AI-ECG). The convolutional neural network was trained using 60% of the patients, validated in 10% of the patients, and tested on the remaining patients (30%).

In a diagnostic study using a deep neural network, the AI-ECG successfully distinguished patients with long QT syndrome (n = 967) from those who were evaluated for long QT syndrome but discharged without this diagnosis (n = 1092) presenting to a specialized arrhythmia clinic. The model performed better than the corrected QT alone, even in the setting of a normal QT interval.

"The AI-ECG model evaluated was able to distinguish patients with electrocardiographically concealed long QT syndrome from those without the syndrome and could potentially and provide a 78.7% accurate pregenetic test anticipation of LQTS genotype status", concluded the authors.

This study contributes to the important body of work demonstrating how machine learning algorithms, properly applied, can expand the utility of existing medical data sources, such as ECGs, to extract novel insights and improve performance, in many cases without significant additional invasiveness or cost.

Source: JAMA cardiology: Bos JM, Attia ZI, Albert DE, Noseworthy PA, Friedman PA, Ackerman MJ. Use of artificial intelligence and deep neural networks in evaluation of patients with electrocardiographically concealed long QT syndrome from the surface 12-lead electrocardiogram. JAMA Cardiol. Published online February 10, 2021. doi:10.1001/jamacardio.2020.7422