Melillo Paolo, Izzo Raffaele, Orrico Ada, Scala Paolo, Attanasio Marcella, Mirra Marco, De Luca Nicola, Pecchia Leandro
Multidisciplinary Department of Medical, Surgical and Dental Sciences, Second University of Naples, Naples, Italy; SHARE Project, Italian Ministry of Education, Scientific Research and University, Rome, Italy.
Department of Translational Medical Sciences, University of Naples Federico II, Naples, Italy.
PLoS One. 2015 Mar 20;10(3):e0118504. doi: 10.1371/journal.pone.0118504. eCollection 2015.
There is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients.
A database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i.e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events.
The best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors.
Combination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events.
心率变异性与血管事件风险相关已达成共识。然而，心率变异性对血管事件的预测价值尚不完全明确。本研究的目的是基于数据挖掘算法开发新的预测模型，为高血压患者提供自动风险分层工具。
专门收集了一个包含139份动态心电图记录及高血压患者临床数据的数据库，这些患者至少随访了12个月。经历过血管事件（即心肌梗死、中风、晕厥事件）的受试者被视为高危受试者。使用了几种数据挖掘算法（如支持向量机、基于树的分类器、人工神经网络）来开发自动分类器，并通过评估受试者工作特征曲线来测试其准确性。此外，我们还测试了超声心动图参数，这些参数已被证明是未来血管事件的有力预测指标。
最佳预测模型基于随机森林，能够识别高危高血压患者，其敏感性和特异性分别为71.4%和87.8%。基于心率变异性的分类器显示出比传统超声心动图参数更高的预测价值，而传统超声心动图参数被认为是重要的心血管危险因素。
结合数据挖掘算法分析的心率变异性测量方法，可能是识别未来有发生血管事件高风险的高血压患者的可靠工具。
