Paredes S, Marques T, Rocha T, de Carvalho P, Henriques J, Morals J
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:2726-9. doi: 10.1109/EMBC.2014.6944186.
Cardiovascular disease (CVD) is the major cause of death in the world. Clinical guidelines recommend the use of risk assessment tools (scores) to identify the CVD risk of each patient as the correct stratification of patients may significantly contribute to the optimization of the health care strategies. This work further explores the personalization of CVD risk assessment, supported on the evidence that a specific CVD risk assessment tool may have good performance within a given group of patients and might perform poorly within other groups. Two main personalization methods based on the proper creation of groups of patients are presented: i) clustering patients approach; ii) similarity measures approach. These two methodologies were validated in a Portuguese population (460 Acute Coronary Syndrome with non-ST segment elevation (ACS-NSTEMI) patients). The similarity measures approach had the best performance, achieving maximum values of sensitivity, specificity and geometric mean of, respectively, 77.7%, 63.2%, 69.7%. These values represent an enhancement in relation to the best performance obtained with current CVD risk assessment tools applied in clinical practice (78.5%, 53.2%, 64.4%).
心血管疾病（CVD）是全球主要的死亡原因。临床指南建议使用风险评估工具（评分）来确定每位患者的心血管疾病风险，因为对患者进行正确分层可能会显著有助于优化医疗保健策略。这项工作进一步探索了心血管疾病风险评估的个性化，依据的证据是特定的心血管疾病风险评估工具在特定患者群体中可能表现良好，而在其他群体中可能表现不佳。本文介绍了基于合理划分患者群体的两种主要个性化方法：i）患者聚类法；ii）相似性度量法。这两种方法在葡萄牙人群（460例非ST段抬高型急性冠状动脉综合征（ACS-NSTEMI）患者）中得到了验证。相似性度量法表现最佳，其敏感性、特异性和几何平均值的最大值分别为77.7%、63.2%、69.7%。这些值相较于临床实践中应用的当前心血管疾病风险评估工具所获得的最佳表现（78.5%、53.2%、64.4%）有所提高。
