Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models

• Published in: Journal of personalized medicine 2022-07, Vol.12 (7), p.1135
• Main Authors: Doldi, Florian, Plagwitz, Lucas, Hoffmann, Lea Philine, Rath, Benjamin, Frommeyer, Gerrit, Reinke, Florian, Leitz, Patrick, Büscher, Antonius, Güner, Fatih, Brix, Tobias, Wegner, Felix Konrad, Willy, Kevin, Hanel, Yvonne, Dittmann, Sven, Haverkamp, Wilhelm, Schulze-Bahr, Eric, Varghese, Julian, Eckardt, Lars
• Format: Article
• Language: English
• Subjects: Accuracy; Age; Artificial intelligence; Business metrics; Cardiovascular diseases; Channelopathy; Comorbidity; Congenital diseases; Deep learning; EKG; Electronic health records; Machine learning; Medical records; Mutation; Neural networks; Patients; Phenotypes; Precision medicine; Statistical analysis; Syncope; Time series
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm12071135

Introduction: The long-QT syndrome (LQTS) is the most common ion channelopathy, typically presenting with a prolonged QT interval and clinical symptoms such as syncope or sudden cardiac death. Patients may present with a concealed phenotype making the diagnosis challenging. Correctly diagnosing at-risk patients is pivotal to starting early preventive treatment. Objective: Identification of congenital and often concealed LQTS by utilizing novel deep learning network architectures, which are specifically designed for multichannel time series and therefore particularly suitable for ECG data. Design and Results: A retrospective artificial intelligence (AI)-based analysis was performed using a 12-lead ECG of genetically confirmed LQTS (n = 124), including 41 patients with a concealed LQTS (33%), and validated against a control cohort (n = 161 of patients) without known LQTS or without QT-prolonging drug treatment but any other cardiovascular disease. The performance of a fully convolutional network (FCN) used in prior studies was compared with a different, novel convolutional neural network model (XceptionTime). We found that the XceptionTime model was able to achieve a higher balanced accuracy score (91.8%) than the associated FCN metric (83.6%), indicating improved prediction possibilities of novel AI architectures. The predictive accuracy prevailed independently of age and QTc parameters. Conclusions: In this study, the XceptionTime model outperformed the FCN model for LQTS patients with even better results than in prior studies. Even when a patient cohort with cardiovascular comorbidities is used. AI-based ECG analysis is a promising step for correct LQTS patient identification, especially if common diagnostic measures might be misleading.