Key result
Standard screening using Bazett's corrected QT interval failed to identify 75% of athletes in the top 1% for uncorrected QT interval for further Long QT syndrome investigation.
Why the study?
Does using uncorrected QT interval percentiles improve the identification of athletes at risk for LQTS compared to standard Bazett's correction?
Cross-Sectional (n=2,077)
Does using uncorrected QT interval percentiles improve the identification of athletes at risk for LQTS compared to standard Bazett's correction?
Standard QT correction methods fail to identify a large majority of young athletes with extreme QT prolongation, suggesting uncorrected QT percentiles may be a better screening approach.
Standard screening may miss athletes at LQTS risk; leaves open whether refined QT thresholds improve detection.
PURPOSE: Sudden cardiac death is the leading cause of death in athletes. Long QT syndrome (LQTS) is one of the most common cardiogenetic diseases that can lead to sudden cardiac death and is identified by QT interval prolongation on an ECG. Recommendations for QT monitoring in athletes are adopted from nonathlete populations. To improve screening, ECG data of athletes are assessed to determine a more appropriate method for QT interval estimation. METHODS: ECG (CardeaScreen) data were collected from June 2010 to March 2015. ECG data with HR greater than 100 bpm were excluded. Fiducial points of outliers were manually corrected if the QRS onset or the T wave offset was misidentified. A model of best fit was determined and compared across four QT correction factors. Classification analysis was used to compare the Bazett's corrected QT interval to the 99th percentile of uncorrected QT interval. RESULTS: High school (n = 597), college (n = 1207), and professional athletes (n = 273) (N = 2077) were analyzed. Mean age was 19 ± 3.5 yr. QT interval varied by cohort (HS = 388 ± 30, Col = 410 ± 33, Pro = 407 ± 27, p < 0.0001). A nonlinear power function with a cubic exponent of -0.349 fit the data the best (R = 0.64). Of the four common correction factors, Fridericia had the lowest residual dependence to HR (m = -0.10). With standard screening, 75% of athletes within the top 1% for QT interval were not identified for further investigation for LQTS. CONCLUSION: Up to 75% of athletes possessing an uncorrected QT interval greater than 99% of the population are not identified for investigation for LQTS using the recommended criteria. We propose a new method of risk stratification that replaces QT interval correction. Further study is needed to establish QT interval distributions and risk thresholds in athletes.
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Pickham et al. (2016) conducted a cross-sectional in Long QT syndrome screening (n=2,077). Standard screening (Bazett's corrected QT interval) vs. 99th percentile of uncorrected QT interval was evaluated on Identification of athletes within the top 1% for QT interval for further investigation for LQTS. Standard screening using Bazett's corrected QT interval failed to identify 75% of athletes in the top 1% for uncorrected QT interval for further Long QT syndrome investigation.
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