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October 1, 2025EP EuropaceOpen Access

Software-based analysis of T-wave morphology: identifying the electrocardiogram signature of high-risk long QT syndrome

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Authors

APAlessandra Pia PorrettaCMCharles MorgatESElodie Surget

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Overview

Automated T-wave morphology assessment predicts cardiac events in long QT syndrome patients, highlighting crucial genotype-specific markers.

Key Points

  • Automated T-wave morphology analysis identified predictors of cardiac events in long QT syndrome patients, enhancing diagnostic precision.
  • Significant findings indicated that the corrected QT interval (QTc) predicted cardiac events with an HR of 1.01 per 1 ms increase in the whole patient population.
  • Using automated analysis with software allowed the identification of unique ECG markers for different long QT syndrome genotypes, showcasing its clinical utility.
  • The study spanned 15 years of follow-up with 467 patients, including distinct genotype groups: LQT1, LQT2, and LQT3.

Cite This Study

Porretta et al. (2025) studied this question.

synapsesocial.com/papers/68dd89defe798ba2fc497d4ahttps://doi.org/10.1093/europace/euaf213
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Architectural T-Wave Analysis and Identification of On-Therapy Breakthrough Arrhythmic Risk in Type 1 and Type 2 Long-QT Syndrome2017 · 15 citations
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  3. 3T-wave morphology can distinguish healthy controls from LQTS patients2016 · 17 citations
  4. 4Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis2018 · 30 citations
  5. 5Improved Clinical Risk Stratification in Patients with Long QT Syndrome? Novel Insights from Multi-Channel ECGs2016 · 6 citations