Key result
A systematic review of 17 studies demonstrated that T wave morphology biomarkers supplement QTc assessment in differentiating long QT syndrome genotypes and predicting cardiac events.
Why the study?
Between 25% and 50% of patients with congenital LQTS demonstrate a normal resting QTc, and while T wave morphology can distinguish congenital LQTS subtypes, its role in acquired LQTS is unclear.
Do T wave morphology biomarkers improve the diagnosis and risk stratification of congenital and acquired long QT syndrome compared to QTc assessment alone?
Systematic Review
Do T wave morphology biomarkers improve the diagnosis and risk stratification of congenital and acquired long QT syndrome compared to QTc assessment alone?
T wave morphology biomarkers provide supplementary diagnostic and prognostic value beyond standard QTc assessment in both congenital and acquired long QT syndrome.
Answers are grounded in this paper and cite their sources.
T wave morphology biomarkers warrant consideration as QTc adjuncts in LQTS; extends systematic evidence for genotype differentiation and event prediction.
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 (ERD 30% ) and late repolarization (LRD 30% ), with ERD reflecting hERG‐specific blockade. Cardiac events were predicted in cLQTS by T wave flatness, notching, and inversion in leads II and V 5 , left slope in lead V 6 ; 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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Tardo et al. (2022) conducted a systematic review in Congenital and acquired long QT syndrome. T wave morphology biomarkers vs. Standard QTc assessment was evaluated on Diagnostic capabilities of T wave morphology biomarkers. A systematic review of 17 studies demonstrated that T wave morphology biomarkers supplement QTc assessment in differentiating long QT syndrome genotypes and predicting cardiac events.
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