Circulation, Vol 81, 797-804, Copyright © 1990 by American Heart Association
PJ Vatterott, KR Bailey and SC Hammill
To improve the predictive accuracy of the signal-averaged
electrocardiogram, we created a linear logistic model for predicting
ventricular tachycardia during electrophysiologic testing. This signal-
averaged electrocardiographic model was created from data obtained from 214
patients undergoing electrophysiologic testing (70 had ventricular
tachycardia during electrophysiologic testing) by using stepwise logistic
regression to rank eight clinical and nine signal-averaged
electrocardiographic variables. The best predictors were ejection fraction,
history of infarction, ventricular ectopic pairs or nonsustained
ventricular tachycardia on Holter monitoring, QRS duration after 25-Hz
filtering, and root mean square voltage of the terminal 40 msec of the QRS
complex after 40- and 80-Hz filtering. Cross validation (a statistical
technique that can be used to accurately evaluate how a predictive model
will perform on a prospective patient population) was used to validate the
model. After cross validation, the model's sensitivity was 91% and
specificity was 59% for predicting ventricular tachycardia during
electrophysiologic testing. This model compared favorably with established
25-Hz late-potential criteria (QRS duration of more than 110 msec and root
mean square voltage of less than 25 microV of the terminal 40 msec of the
QRS complex; sensitivity, 64%; specificity, 85%) and with established 40-Hz
late-potential criteria (QRS duration of more than 114 msec or root mean
square voltage of less than 20 microV of the terminal 40 msec of the QRS
complex or duration of the low-amplitude signal less than 40 microV at the
terminal QRS complex that is greater than 38 msec; sensitivity, 84%;
specificity, 54%).(ABSTRACT TRUNCATED AT 250 WORDS)
ARTICLES
Improving the predictive ability of the signal-averaged electrocardiogram with a linear logistic model incorporating clinical variables
Division of Cardiovascular Diseases and Internal Medicine, Mayo Clinic, Rochester, Minnesota.
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