使用计算表现型来预测脑麻的遗传亚组
medRxiv : the preprint server for health sciences
|February 24, 2025
概括
使用计费代码的新计算方法可以识别患有脑 (CP) 或CP伪装疾病的患者,这些患者可能需要进行基因检测. 这种方法在检测需要进一步基因评估的病例时显示出高准确度.
科学领域:
- 医学遗传学 医学遗传学
- 计算生物学 计算生物学
- 神经学 神经学
背景情况:
- 大脑 (CP) 在20-30%的病例中具有遗传起源.
- 鉴定需要基因检测的CP或CP掩饰疾病患者在临床上具有挑战性.
- 像CP伪装者和密码性CP这样的子组从遗传分析中受益.
研究的目的:
- 开发和评估一种计算表型化方法,用于识别患有不明原因的CP或CP伪装疾病的患者.
- 为了确定ICD-9/10计费代码对此识别过程的有用性.
- 为了促进相关患者群体的遗传测试.
主要方法:
- 开发了一个使用ICD-9/10计费代码的计算表型化策略.
- 该方法应用于波士顿儿童医院CP测序研究中的250名参与者队列.
- 手动图表审查作为比较的黄金标准.
主要成果:
- 在识别需要基因检测的患者时,计算表型实现了高灵敏度 (95%) 和负预测值 (94%).
- 具体性为72%,积极预测值为77%.
- 手动审查发现了8%的CP伪装者,42%的加密CP和50%的非加密CP.
结论:
- 使用计费代码进行计算表型化是一种可行的方法,用于识别可能从遗传测试中受益的CP患者.
- 这种方法显示出作为一个临床决策支持工具的承诺,集成到EHR系统中.
- 在更大的医疗保健系统中需要进一步验证,以确认现实世界的有效性.
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