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Indexing Disease Progression at Study Entry With Individuals At-RIsk for Huntington Disease

机译:在研究入场时将疾病进展指数与亨廷顿氏病患者按风险分类

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The identification of clinical and biological markers-of disease in persons at risk for Huntington disease (HD) has increased in efforts to better quantify and characterize the epoch of prodrome prior to clinical diagnosis. Such efforts are critical in the design and implementation of clinical trials for HD so that interventions can occur at a time most likely to increase neuronal survival and maximize daily functioning. A prime consideration in the examination of prodromal individuals is their proximity to diagnosis. It is necessary to quantify proximity so that individual differences in key marker variables can be properly interpreted. We take a data-driven approach to develop an index that can be viewed as a proxy for time to HD diagnosis known as the CAG-Age Product Scaled or CAP_S. CAP_S is an observed utility variable computed for all genetically at-risk individuals based on age at study entry and CAG repeat length. Results of a longitudinal receiver operating characteristic (ROC) analysis showed that CAP_S had a relatively strong ability to predict individuals who became diagnosed, especially in the first 2 years. Bootstrap validation provided evidence that CAP_S computed on a new sample from the same population could have similar discriminatory power. Cutoffs for the empirical CAP_S distribution can be used to create a classification for mutation-positive individuals (Low-Med-High), which is, useful for comparison with the naturally occurring mutation-negative Control group. The classification is an improvement over the one currently in use as it is based on observed data rather than model-based estimated values.
机译:在有亨廷顿病(HD)风险的人群中,对临床和生物学疾病标志物的鉴定已在努力更好地量化和表征临床诊断之前的时期。这些努力对于HD的临床试验的设计和实施至关重要,因此可以在最有可能增加神经元存活率和最大化日常功能的时间进行干预。检查前驱个体时,首要考虑因素是其接近诊断的能力。必须量化接近度,以便可以正确解释关键标记变量中的各个差异。我们采用数据驱动的方法来开发一个索引,该索引可以被视为HD诊断时间的代理,称为CAG-Age Product Scaled或CAP_S。 CAP_S是一个观察到的效用变量,该变量是根据研究开始时的年龄和CAG重复长度为所有有遗传风险的个体计算的。纵向接收器工作特性(ROC)分析的结果表明,CAP_S具有相对较强的预测被诊断出的个体的能力,尤其是在头2年。引导程序验证提供了证据,表明从相同总体的新样本中计算出的CAP_S可能具有相似的歧视能力。经验CAP_S分布的临界值可用于为突变阳性个体(低-中-高)创建分类,这对于与自然发生的突变阴性对照组进行比较很有用。该分类是对当前使用的分类的一种改进,因为它基于观察数据而不是基于模型的估计值。

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