Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain. Electronic address: https://twitter.com/fsanchezcabo.
Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain; CIBER de enfermedades CardioVasculares (CIBERCV), Spain; Hospital Universitari Son Espases & Health Research Institute of the Balearic Islands (IdISBa), Mallorca, Spain. Electronic address: https://twitter.com/RosselloXavier.
J Am Coll Cardiol. 2020 Oct 6;76(14):1674-1685. doi: 10.1016/j.jacc.2020.08.017.
Clinical practice guidelines recommend assessment of subclinical atherosclerosis using imaging techniques in individuals with intermediate atherosclerotic cardiovascular risk according to standard risk prediction tools.
The purpose of this study was to develop a machine-learning model based on routine, quantitative, and easily measured variables to predict the presence and extent of subclinical atherosclerosis (SA) in young, asymptomatic individuals. The risk of having SA estimated by this model could be used to refine risk estimation and optimize the use of imaging for risk assessment.
The Elastic Net (EN) model was built to predict SA extent, defined by a combined metric of the coronary artery calcification score and 2-dimensional vascular ultrasound. The performance of the model for the prediction of SA extension and progression was compared with traditional risk scores of cardiovascular disease (CVD). An external independent cohort was used for validation.
EN-PESA (Progression of Early Subclinical Atherosclerosis) yielded a c-statistic of 0.88 for the prediction of generalized subclinical atherosclerosis. Moreover, EN-PESA was found to be a predictor of 3-year progression independent of the baseline extension of SA. EN-PESA assigned an intermediate to high cardiovascular risk to 40.1% (n = 1,411) of the PESA individuals, a significantly larger number than atherosclerotic CVD (n = 267) and SCORE (Systematic Coronary Risk Evaluation) (n = 507) risk scores. In total, 86.8% of the individuals with an increased risk based on EN-PESA presented signs of SA at baseline or a significant progression of SA over 3 years.
The EN-PESA model uses age, systolic blood pressure, and 10 commonly used blood/urine tests and dietary intake values to identify young, asymptomatic individuals with an increased risk of CVD based on their extension and progression of SA. These individuals are likely to benefit from imaging tests or pharmacological treatment. (Progression of Early Subclinical Atherosclerosis [PESA]; NCT01410318).
临床实践指南建议根据标准风险预测工具，对具有中等动脉粥样硬化性心血管风险的个体使用影像学技术评估亚临床动脉粥样硬化。
本研究旨在开发一种基于常规、定量和易于测量变量的机器学习模型，以预测年轻无症状个体亚临床动脉粥样硬化（SA）的存在和程度。该模型估计的 SA 风险可用于细化风险评估并优化影像学在风险评估中的应用。
建立弹性网（EN）模型来预测 SA 程度，定义为冠状动脉钙化评分和二维血管超声的综合指标。比较该模型对 SA 扩展和进展的预测性能与心血管疾病（CVD）的传统风险评分。使用外部独立队列进行验证。
EN-PESA（早期亚临床动脉粥样硬化进展）对广义亚临床动脉粥样硬化的预测获得了 0.88 的 c 统计量。此外，EN-PESA 被发现是独立于 SA 基线扩展的 3 年进展的预测因子。EN-PESA 将 40.1%（n=1411）的 PESA 个体分配为中至高心血管风险，这一比例显著高于动脉粥样硬化性 CVD（n=267）和 SCORE（系统冠状动脉风险评估）（n=507）风险评分。总的来说，基于 EN-PESA 风险增加的个体中有 86.8%在基线时存在 SA 迹象或在 3 年内 SA 显著进展。
EN-PESA 模型使用年龄、收缩压以及 10 项常用的血液/尿液检查和饮食摄入值，根据 SA 的扩展和进展来识别具有 CVD 风险增加的年轻无症状个体。这些个体可能受益于影像学检查或药物治疗。（早期亚临床动脉粥样硬化进展[PESA]；NCT01410318）。
