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Published on: June 20, 2020
Deep Neural Network Analysis of the 12-Lead Electrocardiogram Distinguishes Patients With Congenital Long QT Syndrome
J Martijn Bos1, Kan Liu2, Zachi I Attia2
1Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN; Department of Molecular Pharmacology and Experimental Therapeutics, Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, MN.
Objective:
To test whether an artificial intelligence (AI) deep neural network (DNN)-derived analysis of the 12-lead electrocardiogram (ECG) can distinguish patients with long QT syndrome (LQTS) from those with acquired QT prolongation.
Methods:
The study cohort included all patients with genetically confirmed LQTS evaluated in the Windland Smith Rice Genetic Heart Rhythm Clinic and controls from Mayo Clinic's ECG data vault comprising more than 2.5 million patients. For the AI-DNN model, every patient and control with 1 or more ECGs above age- and sex-specific 99th percentile values for QTc (>460 ms for all patients [male/female] <13 years of age or >470 ms for men and >480 ms for women above this age) were included. LQTS patients were age and sex matched to controls at a 1:5 ratio. An AI-DNN involving a multilayer convolutional neural network was developed to classify patients.
Results:
Of the 1,599 patients with genetically confirmed LQTS, 808 had 1 or more ECGs with QTc above the defined thresholds (2987 ECGs) compared with 361,069 of 2.5 million controls (14% of Mayo Clinic patients having an ECG, "presumed negative"; 989,313 ECGs). Following age and sex matching and splitting, 3,309 (training), 411 (validation), and 887 (testing) ECGs were used. This model distinguished patients with LQTS from those with acquired QT prolongation with an area under the curve of 0.896 (accuracy 85%, sensitivity 77%, specificity 87%). The model remained robust with areas under the curve close to or above 0.9, independent of matching ratio (range, 1:5 to 1:2000) or type of ECG data used (rhythm strip of median beat) and after excluding patients with wide QRS or ventricular pacemaker.
Conclusion:
For patients with a QTc exceeding its 99th percentile values, this novel AI-DNN functions as an LQTS mutation detector, being able to identify patients with abnormal QT prolongation secondary to an LQTS-causative mutation rather than with acquired QT prolongation. This algorithm may facilitate screening for this potentially lethal yet highly treatable genetic heart disease.
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