Does Information on Systolic and Diastolic Function Improve Prediction of a Cardiovascular Event by Left Ventricular Hypertrophy in Arterial Hypertension?
- 1 July 2010
- journal article
- research article
- Published by Ovid Technologies (Wolters Kluwer Health) in Hypertension
- Vol. 56 (1), 99-104
- https://doi.org/10.1161/hypertensionaha.110.150128
Abstract
Left ventricular (LV) mass (LVM) is the most important information requested in hypertensive patients referred for echocardiography. However, LV function also predicts cardiovascular (CV) risk independent of LVM. There is no evidence that addition of LV function significantly improves model prediction of CV risk compared with LVM alone. Thus, composite fatal and nonfatal CV or cerebrovascular events were evaluated in 5380 hypertensive outpatients (2336 women, 298 diabetics, and 1315 obese subjects) without prevalent CV disease (follow-up: 3.5±2.8 years). We compared 5 risk models using Cox regression and adjusting for age and sex: (1) LV mass normalized for height in meters 2.7 (LVMi); (2) LVMi, concentric LV geometry, by relative wall thickness (>0.43), ejection fraction, and transmitral diastolic pattern (by thirtiles of mitral deceleration index); (3) LVMi, LV geometry, midwall shortening, and mitral deceleration index thirtiles; (4) as No. 2 with the addition of left atrial dilatation (>23 mm); and (5) as No. 3 with the addition of left atrial dilatation. Individual hazard functions were compared using receiving operating characteristic curves and z statistics. Areas under the curves increased from 0.60 in the model with the sole LVMi to 0.62 in the others (all P values for differences were not significant). The additional information on systolic and diastolic function decreased the contribution (Wald statistics) of LVMi in the Cox model without improving the model ability to predict CV risk. We conclude that risk models with inclusion of information on LV geometry and systolic and diastolic function, in addition to LVMi, do not improve the prediction of CV events but rather redistribute the impact of individual predictors within the risk variance.Keywords
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