QT intervals and QT dispersion as measures of left ventricular hypertrophy in an unselected hypertensive population

Am J Hypertens. 2001 May;14(5 Pt 1):455-62. doi: 10.1016/s0895-7061(00)01292-9.

Abstract

Electrocardiographic (ECG) QT intervals and dispersion correlate with echocardiographic left ventricular mass index (LVMI) in groups of selected essential hypertensives. We tested the strength of this relationship in a large group of unselected hypertensives to assess whether QT measurements may be a simple screening test for LVH in clinical practice. In a cross-sectional study of 386 unselected hypertensive subjects, maximal QT intervals (QTmax), QT dispersion (QTdisp), and ECG voltages (Sokolow-Lyon and Cornell sex-specific voltages) were measured from 12-lead ECG. The LVMI correlated most strongly with Cornell voltage (linear regression r = 0.44, P < .001). The strongest relationship between LVMI and QT parameters was with QTmax, (r = 0.25, P < .001). This relationship weakened using heart rate-corrected QTmax. Correlations between LVMI and QTdisp were weak, whether or not they were corrected for heart rate. Sokolow-Lyon voltages, Cornell voltage and QTmax, but not QTdisp, were independently predictive of LVMI after adjustment for age, sex, race, and the other ECG parameters. Receiver operating characteristic (ROC) curve analyses demonstrated that no QT parameter performed better than simple ECG voltage criteria in the detection of LVH. In conclusion, QTmax, the QT parameter most strongly associated with LVMI, was independently associated with LVMI after adjustment for standard ECG voltage criteria. However, as an isolated measure it was no better than simple ECG voltage criteria as a screening test for LVH in clinical practice.

MeSH terms

  • Adult
  • Blood Pressure / physiology
  • Cross-Sectional Studies
  • Electrocardiography*
  • Female
  • Heart Ventricles / physiopathology
  • Humans
  • Hypertension / physiopathology*
  • Hypertrophy, Left Ventricular / physiopathology*
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • ROC Curve