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

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，每200-1000人中就有一例， 取决于人口和诊断标准。FH导致终身升高的低密度脂蛋白(LDL) 胆固醇是过早动脉粥样硬化和下游冠心病的高危因素。FH是 被疾病控制和预防中心指定为一级疾病，特别是仅有的三种此类疾病之一 疾病，因为它是常见的，与早期疾病的高风险有关，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。 我们建议综合来自全国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中比较健康和经济价值的研究 不同的跳频筛查策略。这项研究将优先考虑FH筛查的最佳方法 美国人口，确定最佳初始筛查年龄，并确定基因检测在筛查中的作用。 我们已经组建了一支儿科预防心脏病学、决策分析、心血管领域的专家团队 疾病流行病学、人口遗传学、生物统计学、卫生经济评估和计算机模拟 建模，以评估和比较儿童和成人不同的FH筛查策略。我们的目标是 使用此专业知识和这些方法来： 量化通用FH表型的诊断效果、临床有效性和经济价值 儿童或成人筛查和FH基因筛查的附加值 将通用FH筛查与使用家族病史或基于大数据的替代方案进行比较 定向筛查仅限于可能诊断为FH的儿童和成人的算法 量化FH病例级联筛查家系的健康和经济价值 我们假设，在儿童时期进行FH筛查将是美国最有价值的筛查策略。 基因检测将最大程度地改善诊断和治疗决策。 不确定性(例如，接近高胆固醇或无家族病史)。我们假设一个机器学习 算法将避免普遍筛查的成本和复杂性，同时产生类似的病例产量，只要 因为胆固醇测试在儿童中很常见。 这项研究将确定在美国人群中筛查FH的最佳方法，并将 基于现有知识的有影响力的数据，并为高效设计FH临床试验奠定基础 放映。这项研究将是对“精确”人口健康方法概念的一个测试案例 在普通人群中筛查基因决定的疾病。

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.