Key result
Adding T-wave morphology to standard ECG features improves LQTS diagnostic AUC to ~0.90.
Why the study?
Diagnosing long QT syndrome is challenging due to considerable overlap of QTc-intervals between LQTS patients and healthy controls, necessitating improved diagnostic methods.
Does a support vector machine-based extended model including T-wave morphology markers improve the diagnosis of long QT syndrome compared to standard ECG parameters?
Cohort (n=688)
No
Does a support vector machine-based extended model including T-wave morphology markers improve the diagnosis of long QT syndrome compared to standard ECG parameters?
Automated assessment of T-wave morphology using machine learning improves the identification of long QT syndrome patients compared to standard QTc intervals.
Answers are grounded in this paper and cite their sources.
May improve LQTS detection in gene-positive cohorts; hypothesis-generating and requires prospective validation before clinical adoption.
AIMS: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from 12-lead electrocardiograms (ECGs) in diagnosing LQTS in a large cohort of gene-positive LQTS patients and gene-negative family members using a support vector machine. METHODS AND RESULTS: A retrospective study was performed including 688 digital 12-lead ECGs recorded from genotype-positive LQTS patients and genotype-negative relatives at their first visit. Two models were trained and tested equally: a baseline model with age, gender, RR-interval, QT-interval, and QTc-intervals as inputs and an extended model including morphology features as well. The best performing baseline model showed an area under the receiver-operating characteristic curve (AUC) of 0.821, whereas the extended model showed an AUC of 0.901. Sensitivity and specificity at the maximal Youden's indexes changed from 0.694 and 0.829 with the baseline model to 0.820 and 0.861 with the extended model. Compared with clinically used QTc-interval cut-off values (>480 ms), the extended model showed a major drop in false negative classifications of LQTS patients. CONCLUSION: The support vector machine-based extended model with T-wave morphology markers resulted in a major rise in sensitivity and specificity at the maximal Youden's index. From this, it can be concluded that T-wave morphology assessment has an added value in the diagnosis of LQTS.
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Hermans et al. (2018) conducted a cohort in Long QT syndrome (LQTS) (n=688). Support vector machine-based extended model including T-wave morphology markers vs. Baseline model using age, gender, RR-interval, QT-interval, and QTc-intervals was evaluated on Area under the receiver-operating characteristic curve (AUC) for diagnosing LQTS (95% CI 0.893-90.87). A support vector machine-based extended model incorporating T-wave morphology markers improved the area under the receiver-operating characteristic curve for diagnosing long QT syndrome from 0.821 to 0.901 compared to a baseline model.
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