A prediction model for left ventricular mass in patients at high cardiovascular risk

作者：Meijs Matthijs F L; Vergouwe Yvonne; Cramer Maarten J M; Vonken Evert Jan A; Velthuis Birgitta K; Verton David J; van der Graaf Yolanda; Visseren Frank L; Mali Willem P; Doevendans Pieter A; Bots Michiel L^*

来源：European Journal of Cardiovascular Prevention & Rehabilitation, 2010, 17(6): 621-627.

DOI：10.1097/HJR.0b013e328332d4bc

• cardiac magnetic resonance imaging
• left ventricular mass
• prediction model
• prevention

Background Left ventricular (LV) mass has a continuous relation with cardiovascular risk, and regression of LV mass induced by pharmacological treatment is associated with improved prognosis. Therefore, early identification of patients with a large LV mass is desired. We developed a model to predict LV mass in individual hypertensives at high cardiovascular risk. Design and methods We analyzed data of 536 hypertensives with symptomatic extracardiac atherosclerotic disease or marked risk factors for atherosclerosis from a cross-sectional study in a tertiary referral center. LV mass was measured by cardiac MRI. We developed the prediction rule with multivariable linear regression analysis and stepwise backward elimination. Internal validation was assessed with bootstrap sampling to obtain an estimate of model performance (R(2)) that may be expected for new patients.
Results Important predictors for LV mass included sex, height, body mass index, systolic blood pressure, and previous aneurysm of the abdominal aorta. R(2) of the prediction model was 45% after internal validation, which was considerably higher than the R(2) of previously reported models (range 1-38%). Addition of electrocardiography data showed limited improvement of the model performance (R(2) = 47%).
Conclusion We present a prediction model for LV mass in hypertensives at high cardiovascular risk. After external validation, this model may be used in clinical practice to estimate LV mass for early identification of large LV mass. The predictions of the model may support appropriate medical care in the prevention of cardiovascular disease.

• 出版日期2010-12