BACKGROUND: Ultrasound (US) examination of heart and carotid arteries provides an accurate assessment of target organ damage (TOD) and may influence the stratification of the absolute cardiovascular risk profile. Microalbuminuria has recently proved to be a useful cost-effective marker of increased cardiovascular risk but is still too often neglected in clinical practice. OBJECTIVE: To evaluate how well artificial neural networks (ANNs) predict cardiovascular risk stratification by means of routine data and urinary albumin excretion, as compared to prediction by the clinical work-up suggested by the International Society of Hypertension (ISH), with and without ultrasound-determined TOD. METHODS: A group of 346 never previously treated essential hypertensives (212 men, 134 women, mean age 47 +/- 9 years) was studied. Risk was stratified according to the criteria suggested by the 1999 WHO/ISH guidelines; first, by routine procedures alone, and subsequently by reassessment, using data on cardiac and vascular structures obtained by US evaluation. The ANN was trained and tested to predict the overall cardiovascular risk on the basis of routine clinical data and urinary albumin excretion (UAE). The impact of these three approaches on the determination of cardiovascular risk profile was evaluated. RESULTS: According to the first classification, 5.5% (n = 19) of patients were considered at low risk, 47.3% (n = 164) at medium, 26.7% (n = 92) at high and 20.6% (n = 71) at very high risk. A marked change in risk stratification, namely an increase in the prevalence of high- and very-high-risk patients (2.3% low, 29.8% medium, 42.8% high and 25.2% very high risk; chi(2) 15.201, P < 0.0001), was obtained when US examination of TOD was taken into consideration. On the basis of routine clinical data and UAE, the artificial neural network successfully predicted overall cardiovascular risk and allocated patients in different classes as accurately as the US-based evaluation. CONCLUSIONS: The use of US techniques allows a more precise stratification of absolute cardiovascular risk in hypertensive patients as compared to routine clinical data. An ANN can accurately identify the patients' risk status by using low-cost routine data and UAE. These results further emphasize the value of UAE in the stratification of cardiovascular risk.
BACKGROUND: Ultrasound (US) examination of heart and carotid arteries provides an accurate assessment of target organ damage (TOD) and may influence the stratification of the absolute cardiovascular risk profile. Microalbuminuria has recently proved to be a useful cost-effective marker of increased cardiovascular risk but is still too often neglected in clinical practice. OBJECTIVE: To evaluate how well artificial neural networks (ANNs) predict cardiovascular risk stratification by means of routine data and urinary albumin excretion, as compared to prediction by the clinical work-up suggested by the International Society of Hypertension (ISH), with and without ultrasound-determined TOD. METHODS: A group of 346 never previously treated essential hypertensives (212 men, 134 women, mean age 47 +/- 9 years) was studied. Risk was stratified according to the criteria suggested by the 1999 WHO/ISH guidelines; first, by routine procedures alone, and subsequently by reassessment, using data on cardiac and vascular structures obtained by US evaluation. The ANN was trained and tested to predict the overall cardiovascular risk on the basis of routine clinical data and urinary albumin excretion (UAE). The impact of these three approaches on the determination of cardiovascular risk profile was evaluated. RESULTS: According to the first classification, 5.5% (n = 19) of patients were considered at low risk, 47.3% (n = 164) at medium, 26.7% (n = 92) at high and 20.6% (n = 71) at very high risk. A marked change in risk stratification, namely an increase in the prevalence of high- and very-high-risk patients (2.3% low, 29.8% medium, 42.8% high and 25.2% very high risk; chi(2) 15.201, P < 0.0001), was obtained when US examination of TOD was taken into consideration. On the basis of routine clinical data and UAE, the artificial neural network successfully predicted overall cardiovascular risk and allocated patients in different classes as accurately as the US-based evaluation. CONCLUSIONS: The use of US techniques allows a more precise stratification of absolute cardiovascular risk in hypertensivepatients as compared to routine clinical data. An ANN can accurately identify the patients' risk status by using low-cost routine data and UAE. These results further emphasize the value of UAE in the stratification of cardiovascular risk.
Authors: Greg Bloom; Ivana V Yang; David Boulware; Ka Yin Kwong; Domenico Coppola; Steven Eschrich; John Quackenbush; Timothy J Yeatman Journal: Am J Pathol Date: 2004-01 Impact factor: 4.307