Familial hypercholesterolemia screening in children: population impact of phenotype, genotype, and cascade approaches

儿童家族性高胆固醇血症筛查：表型、基因型和级联方法对人群的影响

• 批准号: 9883835
• 负责人: Sarah D DE FERRANTI
• 金额: $ 80.45万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9883835
• 关键词: Adult; Adverse effects; Advisory Committees; Affect; Age; Algorithms; Atherosclerosis; Big Data; Biometry; Cardiology; Centers for Disease Control and Prevention (U.S.); Child; Childhood; Cholesterol; Clinical Trials; Clinical Trials Design; Clinical effectiveness; Cohort Studies; Computer Simulation; Conflict (Psychology); Coronary heart disease; County; Data; Decision Analysis; Diagnosis; Diagnostic; Diagnostic tests; Disease; Event; Familial Hypercholesterolemia; Family; Family history of; Family member; Future; General Population; Genetic Diseases; Genetic Screening; Genotype; Guidelines; Health; Health Benefit; Health Policy; Hepatocyte; Influentials; Intervention; Knowledge; LDL Cholesterol Lipoproteins; Laboratories; Life; Life Style; Low Prevalence; Low-Density Lipoproteins; Medical Care Costs; Methods; Mutation; Parents; Patients; Persons; Pharmaceutical Preparations; Phenotype; Population; Population Genetics; Prevalence; Preventive; Preventive service; Preventive treatment; Proxy; Puberty; Public Health; Randomized Controlled Trials; Recommendation; Recording of previous events; Role; Serum; Surveys; Testing; Time; Uncertainty; Visit; Youth; base; cardiovascular disorder epidemiology; cardiovascular disorder risk; clinical practice; cost; cost effective; cost effectiveness; diagnostic accuracy; economic evaluation; economic value; genetic testing; health economics; high risk; improved; improved outcome; lifestyle intervention; machine learning algorithm; machine learning method; models and simulation; pediatric patients; population based; population health; premature; premature atherosclerosis; prevent; screening; screening guidelines; screening program; treatment strategy; uptake

Project Summary Familial hypercholesterolemia (FH) is a common genetic disorder, affecting every 200-1000 people, depending on the population and diagnostic criteria. FH leads to lifetime raised low-density lipoprotein (LDL) cholesterol, a high risk for premature atherosclerosis and downstream coronary heart disease. FH is designated as Tier 1 disease by the Center for Disease Control and Prevention, notably one of only three such diseases, because it is common, is associated with a high risk of premature illness, and is treatable with lifestyle or medications. Great uncertainty exists about the optimal approach to FH screening, which is reflected in conflicting recommendations in national screening guidelines. We propose to synthesize high quality data from national surveys and population-based cohort studies in a health policy computer simulation model comparing the health and economic value of different FH screening strategies. This study will prioritize the optimal approaches to FH screening in the U.S. population, identifying optimal initial screening age and defining the role of genetic testing in screening. We have assembled a team of experts in pediatric preventive cardiology, decision analysis, cardiovascular disease epidemiology, population genetics, biostatistics, health economic evaluation, and computer simulation modeling in order to evaluate and compare different FH screening strategies in children and adults. We aim to use this expertise and these methods in order to:  Quantify diagnostic yield, clinical effectiveness, and economic value of universal FH phenotype screening in childhood or adulthood, and the added value of FH genotype screening  Compare universal FH screening to the alternatives of using family history or a Big Data-based algorithm to direct targeted screening limited to children and adults with possible FH diagnosis  Quantify the health and economic value of cascade screening families of FH cases We hypothesize that FH screening in childhood will be the highest value screening strategy in the U.S. population, and that genetic testing will improve diagnosis and treatment decisions most in cases of diagnostic uncertainty (e.g., borderline high cholesterol or absent family history). We hypothesize that a machine-learning algorithm will avoid the costs and complexity of universal screening, while yielding a similar case yield, as long as cholesterol testing is sufficiently common in children. This study will identify the optimal approach to FH screening in the U.S. population and the most influential data based on current knowledge and set the stage for efficiently designed clinical trials of FH screening. This study will be a test case for the concept of a “precision” population health approach to screening for genetically-determined diseases in the general population.

项目摘要 家族性高胆固醇血症（FH）是一种常见的遗传性疾病，影响每200-1000人， 取决于人群和诊断标准。FH导致终生低密度脂蛋白（LDL）升高 胆固醇，过早动脉粥样硬化和下游冠心病的高风险。FH是 被疾病控制和预防中心指定为1级疾病，特别是只有三个这样的疾病之一， 疾病，因为它是常见的，与过早患病的高风险有关，并且可以用 生活方式或药物。FH筛查的最佳方法存在很大的不确定性，即 反映在国家筛查指南中相互矛盾的建议中。 我们建议综合来自国家调查和基于人口的队列的高质量数据 卫生政策计算机模拟模型的研究， 不同的FH筛查策略。这项研究将优先考虑FH筛查的最佳方法， 美国人口，确定最佳的初始筛查年龄，并确定基因检测在筛查中的作用。 我们组建了一个儿科预防心脏病学、决策分析、心血管 疾病流行病学、群体遗传学、生物统计学、卫生经济学评价和计算机模拟 建模，以评估和比较儿童和成人中不同的FH筛查策略。我们的目标是 利用这些专门知识和方法，以便： 量化通用FH表型的诊断率、临床有效性和经济价值 儿童或成年期筛查，以及FH基因型筛查的附加值 将通用FH筛查与使用家族史或基于大数据的 仅限于可能诊断为FH的儿童和成人的定向靶向筛查算法 量化FH病例家庭级联筛查的健康和经济价值 我们推测，在美国，儿童期FH筛查将是最有价值的筛查策略。 基因检测将改善诊断和治疗决策，大多数情况下， 不确定性（例如，临界高胆固醇或无家族史）。我们假设机器学习 算法将避免通用筛选的成本和复杂性，同时产生类似的情况下产量，只要 因为胆固醇测试在儿童中足够普遍。 这项研究将确定在美国人群中进行FH筛查的最佳方法， 基于现有知识的有影响力的数据，并为有效设计FH临床试验奠定基础 筛选这项研究将是“精确”人口健康方法概念的一个试验案例， 在普通人群中筛查基因决定的疾病。