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ORIGINAL RESEARCH article

Front. Cardiovasc. Med.
Sec. Cardiac Rhythmology
Volume 11 - 2024 | doi: 10.3389/fcvm.2024.1464303
This article is part of the Research Topic Artificial Intelligence for Arrhythmia Detection and Prediction View all articles

Detection of AI-enabled QRS fragmentation from 12-lead electrocardiogram and its clinical relevance for predicting malignant arrhythmia onset

Provisionally accepted
Sebastian Ingelaere Sebastian Ingelaere 1,2*Amalia Villa Gomez Amalia Villa Gomez 3*Carolina Varon Carolina Varon 3*Sabine Van Huffel Sabine Van Huffel 3Bert Vandenberk Bert Vandenberk 1,2Rik Willems Rik Willems 1,2*
  • 1 Department of Cardiovascular Sciences, Faculty of Medicine, KU Leuven, Leuven, Belgium
  • 2 University Hospitals Leuven, Leuven, Brussels, Belgium
  • 3 Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, Faculty of Engineering Sciences, KU Leuven, Leuven, Belgium

The final, formatted version of the article will be published soon.

    Background: Electrocardiographic markers differentiating between death caused by ventricular arrhythmias and non-arrhythmic death could improve the selection of patients for implantable cardioverter-defibrillator (ICD) implantation. QRS fragmentation (fQRS) is a parameter of interest, but subject to debate. We investigated the association of an automatically quantified probability of fragmentation with the outcome in ICD patients. Methods: From a single-center retrospective registry, all patients implanted with an ICD between January 1996 and December 2018 were eligible for inclusion. Patients with active pacing were excluded. From the electronical medical record, clinical characteristics at implantation were collected and a 12-lead ECG was exported and analyzed by a previously validated machine-learning algorithm to quantify the probability of fQRS. To compare fQRS(+) and fQRS(-) patients, dichotomization was performed using the Youden index. Patients with a high probability of fragmentation in any region (anterior, inferior or lateral), were labeled fQRS(+). The impact of this fQRS probability on outcomes was investigated using Cox regression. Results: A total of 1242 patients with a mean age of 62.6 ± 11.5 years and a reduced left ventricular ejection fraction of 31 ± 12% were included of which 227 (18.3%) were female. The vast majority suffered from ischemic heart disease (64.3%) and were implanted in primary prevention (63.8%). 538 (43.3%) had a high probability of fragmentation in any region. Patients with a high probability of fragmentation had more frequently dilated cardiomyopathy (39.4% vs 33.0%, p=0.019), left bundle branch block (40.8% vs 32.5%, p=0.006) and a higher use of cardiac resynchronization therapy with defibrillator (CRT-D) devices (33.9% vs 26.3%, p=0.004). After adjustment in a multivariable Cox model, there was no significant association between the probability of global or regional fQRS and appropriate ICD therapy, inappropriate shock and short-or long-term mortality. Conclusion: There was no association between the automatically quantified probability of the presence of fQRS and outcome. This lack of predictive value might be due to the algorithm used, which identifies only the presence but not the severity of fragmentation.

    Keywords: QRS fragmentation, Implantable-cardioverter defibrillator, sudden cardiac death Age (years), left ventricular ejection fraction, LVEF (%), creatinine (mg/dL), QTc interval, QTc (ms), ischemic heart disease, IHD, dilated cardiomyopathy, DCM

    Received: 13 Jul 2024; Accepted: 03 Oct 2024.

    Copyright: © 2024 Ingelaere, Villa Gomez, Varon, Van Huffel, Vandenberk and Willems. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Sebastian Ingelaere, Department of Cardiovascular Sciences, Faculty of Medicine, KU Leuven, Leuven, 3000, Belgium
    Amalia Villa Gomez, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, Faculty of Engineering Sciences, KU Leuven, Leuven, B-3001, Belgium
    Carolina Varon, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, Faculty of Engineering Sciences, KU Leuven, Leuven, B-3001, Belgium
    Rik Willems, Department of Cardiovascular Sciences, Faculty of Medicine, KU Leuven, Leuven, 3000, Belgium

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