Automated T‐wave analysis can differentiate acquired <scp>QT</scp> prolongation from congenital long <scp>QT</scp> syndrome

Sugrue, Alan; Noseworthy, Peter A.; Kremen, Václav; Bos, J. Martijn; Qiang, Bo; Rohatgi, Ram K.; Sapir, Yehu; Attia, Zachi I.; Brady, Peter A.; Caraballo, Pedro J.; Asirvatham, Samuel J.; Friedman, Paul A.; Ackerman, Michael J.

doi:10.1111/anec.12455

Cited by 9 publications

(20 citation statements)

References 39 publications

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“…Until now, the diagnosis of LQTS relies on genetic testing and the Schwartz Score, taking into account ECG and clinical parameters [ 1 ]. Specific ECG parameters (e.g., T-wave morphologies [ 4 ], QT-interval changes upon stress (e.g., epinephrine or treadmill QT-stress test [ 5 , 6 ]) or echocardiographic markers can help to unmask LQTS but still lack relevant diagnostic value [ 7 , 8 , 9 ]. Hence, reasonable simple tests are still lacking, leaving the resting 12-lead ECG as the most essential diagnostic tool for LQTS detection [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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

Doldi

Plagwitz

Hoffmann

et al. 2022

JPM

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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.

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Section: Introductionmentioning

confidence: 99%

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

Doldi

Plagwitz

Hoffmann

et al. 2022

JPM

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“…9 In addition, although previous studies have identified unique ECG features associated with LQTS or its specific genetic subtypes, these features have mostly involved specific, human-selected features of the ECG, such as shape, slope, and overall morphologic characteristics of the T wave. 2,6,7,14 In a step to involve AI, Hermans and colleagues 15 built upon these T wave morphologic studies by developing a machine learning, support-vector model showing that vectorcardiographic parameters from the T wave can improve diagnosis of LQTS with the capacity to distinguish patients with LQTS from genotypenegative family members with an AUC up to 0.901 based on the model used. In contrast, our AI-ECG used unsupervised feature extraction in which an agnostic approach of the complete ECG waveform across all 12 leads of the ECG, as compared with the T wave alone, was analyzed during training, with network feature selection based on minimizing an error function.…”

Section: Discussionmentioning

confidence: 99%

“…1 Although corrected QT (QTc) values exceeding the 99th percentile in men (≥470 milliseconds) or women (≥480 milliseconds) are the thresholds for further evaluation for LQTS as an otherwise incidental finding in ECG-based screening programs, individuals with a QTc greater than or equal to 500 milliseconds are at increased risk of LQTS-associated events. Over the years, in addition to the QTc, several ECG features, such as specific T wave shapes for the LQTS genetic subtypes, 2 QT prolongation on treadmill exercise testing, 3 following epinephrine challenge, 4 or upon brisk standing, 5 as well as novel ECG-or echocardiogram-derived risk factors, such as T wave morphologic characteristics and the electromechanical window, [6][7][8] have improved diagnosis and risk stratification for these patients.…”

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confidence: 99%

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

Bos

Attia

Albert³

et al. 2021

JAMA Cardiol

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IMPORTANCELong 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 PARTICIPANTSA 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 MEASURESThe 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). RESULTSOf 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 pati...

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“…Personalized risk stratification for diLQTS is aided by the Pro‐QTc score and Tisdale score diagnostic tools (Haugaa et al, 2013; Schwartz & Woosley, 2016; Tisdale et al, 2013). TWM has also been shown to enhance discrimination of abnormal repolarization in aLQTS (Couderc et al, 2011; Graff et al, 2009; Graff et al, 2010; Heijman & Crijns, 2015; Johannessen et al, 2014; Sugrue et al, 2015; Sugrue, Noseworthy, et al, 2017; Vicente et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

Tardo

Peck

Subbiah

et al. 2022

Noninvasive Electrocardiol

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Introduction QTc prolongation is key in diagnosing long QT syndrome (LQTS), however 25%–50% with congenital LQTS (cLQTS) demonstrate a normal resting QTc. T wave morphology (TWM) can distinguish cLQTS subtypes but its role in acquired LQTS (aLQTS) is unclear. Methods Electronic databases were searched using the terms “LQTS,” “long QT syndrome,” “QTc prolongation,” “prolonged QT,” and “T wave,” “T wave morphology,” “T wave pattern,” “T wave biomarkers.” Whole text articles assessing TWM, independent of QTc, were included. Results Seventeen studies met criteria. TWM measurements included T‐wave amplitude, duration, magnitude, Tpeak‐Tend, QTpeak, left and right slope, center of gravity (COG), sigmoidal and polynomial classifiers, repolarizing integral, morphology combination score (MCS) and principal component analysis (PCA); and vectorcardiographic biomarkers. cLQTS were distinguished from controls by sigmoidal and polynomial classifiers, MCS, QTpeak, Tpeak‐Tend, left slope; and COG x axis. MCS detected aLQTS more significantly than QTc. Flatness, asymmetry and notching, J‐Tpeak; and Tpeak‐Tend correlated with QTc in aLQTS. Multichannel block in aLQTS was identified by early repolarization (ERD30%) and late repolarization (LRD30%), with ERD reflecting hERG‐specific blockade. Cardiac events were predicted in cLQTS by T wave flatness, notching, and inversion in leads II and V5, left slope in lead V6; and COG last 25% in lead I. T wave right slope in lead I and T‐roundness achieved this in aLQTS. Conclusion Numerous TWM biomarkers which supplement QTc assessment were identified. Their diagnostic capabilities include differentiation of genotypes, identification of concealed LQTS, differentiating aLQTS from cLQTS; and determining multichannel versus hERG channel blockade.

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Automated T‐wave analysis can differentiate acquired QT prolongation from congenital long QT syndrome

Abstract: T-wave morphological analysis on lead V of the surface ECG could successfully differentiate congenital from acquired causes of QT prolongation.

Cited by 9 publications

References 39 publications

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

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

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

The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

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