多基因风险评分增强预测模型与冠状动脉疾病临床风险评分的预测准确性
Joshua Elliott1, Barbara Bodinier1, Tom A Bond1
1Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
JAMA
|February 19, 2020
概括
将多基因风险评分添加到冠状动脉疾病 (CAD) 预测模型中,可以轻微提高准确性. 在广泛使用这些遗传风险信息之前,还需要进行进一步的研究.
科学领域:
- 心血管疾病流行病学
- 遗传学和基因组学
- 预测模型
背景情况:
- 冠状动脉疾病的多基因风险评分 (PRS) 与现有的风险预测模型的临床实用性仍然不清楚.
- 已建立的模型,如聚合队列方程,为风险评估提供了基准.
研究的目的:
- 确定CAD的PRS是否能提高风险预测的准确性,超出目前的聚合队列方程.
- 评估PRS在一个庞大而多样化的群体中的增量预测值.
主要方法:
- 一项观察性研究利用了2006-2010年的英国生物库数据,包括用于PRS优化的病例控制样本和用于预测准确性的更大队列.
- 该研究使用PRS,聚合队列方程和它们的组合来评估事件CAD风险的歧视 (C统计),校准和重新分类.
- 在重新分类分析中使用了7.5%的风险门.
主要成果:
- 将PRS与聚合队列方程相结合,得出C统计值为0.78,比单独聚合队列方程略有改善 (0.76).
- 在7.5%的风险门下,添加PRS导致事故CAD的净重新分类改善4.0%.
- 虽然在统计学上显著,但预测准确度和风险分层的改善是适度的,并且适用于很少一部分人.
结论:
- 将CAD的PRS纳入聚合队列方程可以在统计学上显著但适度地提高预测事件CAD.
- 目前的证据表明广泛实施的临床影响有限,需要进一步调查.
- 这项研究强调了基因信息的潜力, 但强调在临床采用之前需要仔细评估.
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