Quantifying cardiovascular calcification at very old age for personalized risk classification

量化高龄心血管钙化以进行个性化风险分类

• 批准号: 10153851
• 负责人: MICHAEL J BLAHA
• 金额: $ 73.86万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10153851
• 关键词: Adult; Age; Age-Years; Aging; Ancillary Study; Aspirin; Atherosclerosis; Atherosclerosis Risk in Communities; Biological; Blood Vessels; Cardiac; Cardiovascular Diseases; Cardiovascular system; Classification; Clinical; Communities; Coronary heart disease; Data; Decision Making; Dementia; Discrimination; Drug Interactions; Elderly; Electronic Health Record; Equilibrium; Heart failure; Individual; Inferior; Knowledge; Life; Life Style; Lung diseases; Malignant Neoplasms; Measures; Modeling; Morbidity - disease rate; Natriuretic Peptides; Older Population; Participant; Patient Preferences; Patient-Focused Outcomes; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Plant Roots; Population; Predisposition; Prevalence; Preventive; Preventive therapy; Preventive treatment; Primary Prevention; Prognosis; Recording of previous events; Risk; Risk Factors; Scanning; Stroke; Surveys; Thoracic aorta; Time; Troponin; United States National Institutes of Health; Variant; Visit; Weight; X-Ray Computed Tomography; aged; base; calcification; cardiovascular disorder prevention; cardiovascular risk factor; clinical risk; cohort; comorbidity; coronary artery calcium; coronary calcium scoring; density; design; evidence base; high risk; human very old age (85+); improved; middle age; mortality risk; predictive modeling; preference; prognostic; risk prediction; risk prediction model; risk stratification; shared decision making; tool

ABSTRACT: Quantifying cardiovascular calcification at very old age for personalized risk classification Predicted risk is central to decision-making in primary prevention of atherosclerotic cardiovascular disease (ASCVD). However, classifying risk in older adults ≥75 years remains extremely challenging. By placing heavy weight on age, existing risk prediction models universally assign high risk status and therefore recommend treatment for nearly all older adults, raising concerns about overmedication, drug-drug interactions, and lack of personalization. Competing risk of mortality and morbidity due to other conditions (e.g., cancer, lung disease, and dementia) is another issue in this population, requiring a fine balance between prevention of CVD based on accurate risk prediction vs. management of other comorbidities. Unfortunately, such accurate risk prediction is not achievable with one-time assessment of traditional cardiovascular risk factors at older ages because this approach does not capture cumulative lifetime exposures and individuals' susceptibility. In this context, coronary artery calcium (CAC) is promising, as it directly quantifies a composite of cumulative exposure and an individual's susceptibility to risk factors. Indeed, a high CAC score is one of the most potent predictors of ASCVD. Recently, zero and low CAC has been shown to be useful for “de-risking”, identifying individuals who are at low absolute risk in whom preventive therapy may not result in net benefit. Recent studies also demonstrate that extra-coronary calcium (ECC) (i.e., aortic root/valve, mitral annulus, and thoracic aorta) detected on a routine CAC scan provides risk information beyond CAC. However, prevalence and prognostic data for CAC and ECC among very old adults are surprisingly sparse. Thus, we propose to perform non-contrasted cardiac-gated computed tomography among ~3,100 participants in the Atherosclerosis Risk in Communities (ARIC) Study during forthcoming visits between 2017-19 and to develop a dedicated CVD risk classification tool incorporating CAC and ECC for older adults ≥75 years. To maximize the usefulness of our CVD risk classification tool, we will develop benefit-harm charts incorporating our prediction models and patient preferences for two representative scenarios, preventive statin and aspirin pharmacotherapy. Aim 1: To develop risk classification models for CVD risk in the 75-and-older population. Aim 2: To evaluate the interplay between 30-year cumulative exposures and CAC/ECC for assessment of healthy vascular aging (e.g., low or zero CAC) and estimation of CVD prognosis. Aim 3: To evaluate the effect of preventive treatment preferences and CAC/ECC-based risk information on the balance of benefits and harms of preventive pharmacotherapy in adults ≥75 years of age. This project will deliver accurate risk classification tools for CVD based on established risk factors and CAC/ECC for the 75-and-older population. The comparison of 30-year cumulative risk factor exposures vs. CAC/ECC will be of value clinically and biologically. Our benefit-harm charts will directly inform evidence-based shared decision-making in the context of primary prevention of CVD among older adults aged 75 and older.

摘要：对高龄心血管钙化进行量化，以进行个性化风险分类 预测风险是动脉粥样硬化性心血管疾病一级预防决策的核心 (ASCVD)。然而，对75岁的老年人≥的风险进行分类仍然具有极大的挑战性。通过将沉重的 根据年龄的权重，现有的风险预测模型普遍认为高风险状态，因此建议 几乎所有老年人都接受治疗，这引发了人们对过度用药、药物-药物相互作用和缺乏 个性化。其他疾病(如癌症、肺部疾病、 和痴呆症)是这一人群的另一个问题，需要在预防心血管疾病和基于 关于准确的风险预测与其他并发症的管理。不幸的是，如此准确的风险预测 在老年人中对传统心血管危险因素进行一次性评估是不可能实现的，因为 该方法不包括累积的终身接触和个人的易感性。 在这种情况下，冠状动脉钙(CAC)是很有希望的，因为它直接量化了累积的 暴露和个人对风险因素的易感性。事实上，高CAC分数是最有效的方法之一 ASCVD的预测因素。最近，零和低CAC已被证明是有用的“去风险”，识别 绝对风险较低、预防性治疗可能不会带来净收益的个人。近期 研究还表明，冠脉外钙(ECC)(即主动脉根部/瓣膜、二尖瓣环和胸腔) 在常规CAC扫描中检测到的(大动脉)可提供CAC以外的风险信息。然而，流行率和 高龄人群中CAC和ECC的预后数据令人惊讶地稀少。因此，我们建议执行 年动脉粥样硬化风险的~3,100名参与者的非对比心脏门控CT检查 在2017-19年即将到来的访问期间进行社区(ARIC)研究，并制定专门的心血管疾病风险 结合CAC和ECC的老年人分类工具≥75岁。为了最大限度地利用我们的 CVD风险分类工具，我们将开发结合我们的预测模型和患者的益处-危害图表 倾向于两种有代表性的方案，预防性他汀类药物和阿司匹林药物疗法。 目的1：建立75岁及以上人群心血管疾病风险分类模型。 目的2：评估30年累积暴露和CAC/ECC之间的相互作用，以评估 健康的血管老化(例如，低CAC或零CAC)和CVD预后的估计。 目的3：评价预防性治疗偏好和基于CAC/ECC的风险信息对 ≥75岁成人预防性药物治疗的利弊平衡。 该项目将为心血管疾病提供准确的风险分类工具，基于既定的风险因素和 适用于75岁及以上人群的CAC/ECC。30年累积风险因素暴露与 CAC/ECC具有临床和生物学价值。我们的利害图将直接提供基于证据的信息 75岁及以上老年人在心血管疾病初级预防方面的共同决策。