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Identification of Concealed and Manifest Long QT Syndrome Using a Novel T Wave Analysis Program
A. Sugrue, PA. Noseworthy, V. Kremen, JM. Bos, B. Qiang, RK. Rohatgi, Y. Sapir, ZI. Attia, P. Brady, SJ. Asirvatham, PA. Friedman, MJ. Ackerman,
Circulation. Arrhythmia and electrophysiology.
2016 ;
9 (7) :
.
Language English Country United States
Document type Journal Article
Persistent link
https://www.medvik.cz/link/bmc17023855
Online
Full text
NLK
Free Medical Journals
from 2008 to 1 year ago
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from 2008-04-01
- MeSH
- Electrocardiography methods MeSH
- Genotype MeSH
- Humans MeSH
- Young Adult MeSH
- Signal Processing, Computer-Assisted MeSH
- Sensitivity and Specificity MeSH
- Long QT Syndrome classification diagnosis genetics MeSH
- Check Tag
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: Congenital long QT syndrome (LQTS) is characterized by QT prolongation. However, the QT interval itself is insufficient for diagnosis, unless the corrected QT interval is repeatedly ≥500 ms without an acquired explanation. Further, the majority of LQTS patients have a corrected QT interval below this threshold, and a significant minority has normal resting corrected QT interval values. Here, we aimed to develop and validate a novel, quantitative T wave morphological analysis program to differentiate LQTS patients from healthy controls. METHODS AND RESULTS: We analyzed a genotyped cohort of 420 patients (22±16 years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a novel, proprietary T wave analysis program that quantitates subtle changes in T wave morphology. The top 3 discriminating features in each ECG lead were determined and the lead with the best discrimination selected. Classification was performed using a linear discriminant classifier and validated on an untouched cohort. The top 3 features were Tpeak-Tend interval, T wave left slope, and T wave center of gravity x axis (last 25% of the T wave). Lead V6 had the best discrimination. It could distinguish 86.8% of LQTS patients from healthy controls. Moreover, it distinguished 83.33% of patients with concealed LQTS from controls, despite having essentially identical resting corrected QT interval values. CONCLUSIONS: T wave quantitative analysis on the 12-lead surface ECG provides an effective, novel tool to distinguish patients with either LQT1/LQT2 from healthy matched controls. It can provide guidance while mutation-specific genetic testing is in motion for family members.
Electrical and Computer Engineering Ben Gurion University of the Negev Beer Sheva Israel
From the Division of Internal Medicine Mayo Clinic Rochester MN
References provided by Crossref.org
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- $a Sugrue, Alan $u From the Division of Internal Medicine (A.S.), Division of Cardiovascular Diseases (P.A.N., V.K., B.Q., R.K.R., Z.I.A., P.B., S.J.A., P.A.F., M.J.A.), Department of Pediatric and Adolescent Medicine, Division of Pediatric Cardiology (J.M.B., S.J.A., M.J.A.), Mayo Clinic, Rochester, MN; Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Czech Republic (V.K.); Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel (Y.S., Z.I.A.); Windland Smith Rice Sudden Death Genomics Laboratory, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN (J.M.B., M.J.A.). ackerman.michael@mayo.edu.
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- $a BACKGROUND: Congenital long QT syndrome (LQTS) is characterized by QT prolongation. However, the QT interval itself is insufficient for diagnosis, unless the corrected QT interval is repeatedly ≥500 ms without an acquired explanation. Further, the majority of LQTS patients have a corrected QT interval below this threshold, and a significant minority has normal resting corrected QT interval values. Here, we aimed to develop and validate a novel, quantitative T wave morphological analysis program to differentiate LQTS patients from healthy controls. METHODS AND RESULTS: We analyzed a genotyped cohort of 420 patients (22±16 years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a novel, proprietary T wave analysis program that quantitates subtle changes in T wave morphology. The top 3 discriminating features in each ECG lead were determined and the lead with the best discrimination selected. Classification was performed using a linear discriminant classifier and validated on an untouched cohort. The top 3 features were Tpeak-Tend interval, T wave left slope, and T wave center of gravity x axis (last 25% of the T wave). Lead V6 had the best discrimination. It could distinguish 86.8% of LQTS patients from healthy controls. Moreover, it distinguished 83.33% of patients with concealed LQTS from controls, despite having essentially identical resting corrected QT interval values. CONCLUSIONS: T wave quantitative analysis on the 12-lead surface ECG provides an effective, novel tool to distinguish patients with either LQT1/LQT2 from healthy matched controls. It can provide guidance while mutation-specific genetic testing is in motion for family members.
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