A classification model of five electrocardiographic repolarization variables identified patients with long QT syndrome with an estimated sensitivity of 92.6% and specificity of 95.8%.
Case-Control (n=352)
Does a classification model based on new electrocardiographic repolarization variables accurately identify patients with long QT syndrome compared to normal subjects?
A novel classification model incorporating five quantitative ECG repolarization variables can accurately differentiate patients with long QT syndrome from normal subjects.
Effect estimate: Sensitivity 92.6%, Specificity 95.8% (95% CI 81.6-100% (Sensitivity), 93.6-98.1% (Specificity))
p-value: p=<0.001
The long QT syndrome is electrocardiographically characterized by a prolonged QT interval and by several other, more subtle, ST-T-U wave abnormalities, most of which have not been quantified. To determine the possible usefulness of several new electrocardiographic characteristics in identifying patients with known long QT syndrome, logistic regression models were applied to a data base of seven new, relatively independent, electrocardiographic repolarization variables. These were measured on digitized 12-lead electrocardiograms of315 normal subjects and 37 patients with the long QT syndrome (members of well-identified long QT syndrome families, QTc>0.44 second, 27% symptomatic), who ranged in age from 17 to 60 years. Electrocardiographic variables that independently differentiated (p<0.001) patients with long QT syndrome from normal subjects included quantitative measures of repolarization: early duration, rate, T wave symmetry, late phenomena, and heterogeneity. All selected repolarization variables except the early duration variable were essentially independent of the QTc (r 2<0.15), and all contributed significantly to the identification of patients with long QT syndrome. A classification model of five electrocardiographic predictor variables resulted in an estimated sensitivity (95% confidence interval) of 92.6% (81.6-100%) and an estimated specificity (95% confidence interval) of 95.8% (93.6-98.1%). This model performed significantly better than an alternative classification model that was based on the early duration variable as
Bibas et al. (Wed,) conducted a case-control in Long QT Syndrome (n=352). Classification model of five electrocardiographic repolarization variables vs. Alternative classification model based on the early duration variable was evaluated on Identification of patients with long QT syndrome (Sensitivity 92.6%, Specificity 95.8%, 95% CI 81.6-100% (Sensitivity), 93.6-98.1% (Specificity), p=<0.001). A classification model of five electrocardiographic repolarization variables identified patients with long QT syndrome with an estimated sensitivity of 92.6% and specificity of 95.8%.