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首页> 外文期刊>Journal of hypertension >Microalbuminuria identifies overall cardiovascular risk in essential hypertension: an artificial neural network-based approach.
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Microalbuminuria identifies overall cardiovascular risk in essential hypertension: an artificial neural network-based approach.

机译:微量白蛋白尿可确定原发性高血压的总体心血管风险:一种基于人工神经网络的方法。

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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.
机译:背景:心脏和颈动脉的超声检查可以准确评估靶器官损伤(TOD),并可能影响绝对心血管风险的分层。微量白蛋白尿最近被证明是增加心血管疾病风险的有用的成本有效标志,但在临床实践中仍然经常被忽视。目的:与国际高血压学会(ISH)建议的临床检查结果相比,通过常规数据和尿白蛋白排泄评估人工神经网络(ANN)对心血管风险分层的预测效果如何超声测定的TOD。方法:研究了一组346例从未接受过治疗的原发性高血压(男性212例,女性134例,平均年龄47 +/- 9岁)。根据1999年WHO / ISH指南建议的标准对风险进行了分层;首先,仅使用常规程序,然后通过重新评估,使用通过US评估获得的心脏和血管结构数据。对ANN进行了训练和测试,以根据常规临床数据和尿白蛋白排泄(UAE)预测总体心血管风险。评价了这三种方法对确定心血管风险的影响。结果:根据第一个分类,低风险患者占5.5%(n = 19),中度患者占47.3%(n = 164),高风险患者占26.7%(n = 92),高风险患者占20.6%(n = 71)高风险。风险分层的显着变化,即高危和极高危患者的患病率增加(低危2.3%,中危29.8%,高危42.8%和高危25.2%; chi(2)15.201,P < 0.0001)是在考虑美国TOD检验时获得的。根据常规临床数据和阿联酋,人工神经网络成功地预测了整体心血管风险,并像基于美国的评估一样准确地将患者分为不同的类别。结论:与常规临床数据相比,使用US技术可以对高血压患者的绝对心血管风险进行更精确的分层。人工神经网络可以使用低成本的常规数据和阿联酋来准确识别患者的风险状况。这些结果进一步强调了阿联酋在心血管风险分层中的价值。

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