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.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.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.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.
View details for DOI 10.1249/MSS.0000000000000962
View details for PubMedID 27116644