Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram

Abstract

Importance: 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.

Objective: To determine whether artificial intelligence (AI) using deep neural networks is better than the QTc alone in distinguishing patients with concealed LQTS from those with a normal QTc using a 12-lead electrocardiogram (ECG).

Design, setting, and participants: A diagnostic case-control study was performed using all available 12-lead ECGs from 2059 patients presenting to a specialized genetic heart rhythm clinic. Patients were included if they 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%). The study was conducted from January 1, 1999, to December 31, 2018.

Main outcomes and measures: The goal of the study was to test the ability of the convolutional neural network 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).

Results: Of the 2059 patients included, 1180 were men (57%); mean (SD) age at first ECG was 21.6 (15.6) years. All 12-lead ECGs from 967 patients with LQTS and 1092 who were evaluated for LQTS but discharged without this diagnosis were included for AI-ECG analysis. Based on the ECG-derived QTc alone, patients were classified with an area under the curve (AUC) value of 0.824 (95% CI, 0.79-0.858); using AI-ECG, the AUC was 0.900 (95% CI, 0.876-0.925). Furthermore, in the subset of patients who had a normal resting QTc (<450 milliseconds), the QTc alone distinguished those with LQTS from those without LQTS with an AUC of 0.741 (95% CI, 0.689-0.794), whereas the AI-ECG increased this discrimination to an AUC of 0.863 (95% CI, 0.824-0.903). In addition, the AI-ECG was able to distinguish the 3 main genotypic subgroups (LQT1, LQT2, and LQT3) with an AUC of 0.921 (95% CI, 0.890-0.951) for LQT1 compared with LQT2 and 3, 0.944 (95% CI, 0.918-0.970) for LQT2 compared with LQT1 and 3, and 0.863 (95% CI, 0.792-0.934) for LQT3 compared with LQT1 and 2.

Conclusions and relevance: In this study, the AI-ECG was found to distinguish patients with electrocardiographically concealed LQTS from those discharged without a diagnosis of LQTS and provide a nearly 80% accurate pregenetic test anticipation of LQTS genotype status. This model may aid in the detection of LQTS in patients presenting to an arrhythmia clinic and, with validation, may be the stepping stone to similar tools to be developed for use in the general population.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Albert reported having a patent to artificial intelligence (AI) for QT assessment pending. Dr Friedman reported having a licensed agreement with AliveCor related to LongQT know-how and AI tools. Dr Ackerman is a consultant for Abbott, Audentes Therapeutics, Biotronik, Boston Scientific, Daiichi Sankyo, Invitae, LQT Therapeutic, Medtronic, MyoKardia, and UpToDate. Drs Bos, Attia, Noseworthy, Friedman, and Ackerman, and Mayo Clinic have an equity/royalty relationship with AliveCor Inc but without remuneration thus far. Dr Albert is an equity partner and employee of AliveCor Inc. No other disclosures were reported.

Figures

Figure 1.. Study Cohort Selection

Selection of study cohorts, subgroup assignment for long QT syndrome (LQTS) detection and LQTS genotype classification, and breakdown of training, internal validation, and testing sets. Among all patients seen in the Genetic Heart Rhythm Clinic (2984), a total of 2059 were diagnosed with LQTS or discharged without an arrhythmia diagnosis. For these patients, a total of 9085 ECGs were available. These patients were tested for models of LQTS detection or LQTS genotype (LQTS genotype classification) and both models were trained, validated, and tested on a 60%-10%-30% split.

Figure 2.. Performance of a Convolutional Neural Network (CNN) in Long QT Syndrome (LQTS) Detection

Receiver operating characteristic curves and confusion matrices for LQTS detection analyses showing results of CNN performance on the patient’s first Mayo Clinic electrocardiogram (ECG) (A) or mean of all of a patient’s ECGs (B). NN indicates neural network; QTc, corrected QT.

Figure 3.. Performance of Convolutional Neural Network (CNN) in Concealed Long QT Syndrome (LQTS) Detection

Receiver operating characteristics curve and confusion matrix showing performance of the CNN to distinguish patients with concealed LQTS (corrected QT [QTc] ≤450 milliseconds) from those dismissed normal. NN indicates neural network.

Figure 4.. Performance of Convolutional Neural Network (CNN) in Long QT Syndrome (LQTS) Genotype Classification

Receiver operating characteristics curve (A) and confusion matrix (B) showing performance of the CNN to distinguish the main genetic subtypes of LQTS. LQT1 indicates type 1 LQTS; LQT2, type 2 LQTS; LQT3, type 3 LQTS.