在RASopathies中优化临床红旗的诊断准确性
Emanuele Bobbio1, Martina Caiazza1, Emanuele Monda1
1Inherited and Rare Cardiovascular Diseases, Department of Translational Medical Sciences, University of Campania "Luigi Vanvitelli", Monaldi Hospital, Naples, Italy.
American journal of medical genetics. Part A
|March 11, 2026
概括
拉索病是一种遗传性疾病. 肺狭窄和面部形是关键指标,促使早期对RASopathies进行遗传测试,并提高诊断准确性.
科学领域:
- 遗传学 遗传学 是一个
- 心脏病学 心脏病学
- 机器学习 机器学习
背景情况:
- 拉索病是一种与RAS-MAPK通路相关的遗传性疾病,经常导致心脏缺陷和发育问题.
- 准确的RAS病症诊断对于及时干预和管理至关重要.
研究的目的:
- 在疑似RASopathy病例中评估基因检测的诊断产量.
- 使用机器学习识别可预测RASopathy的临床红旗.
主要方法:
- 在2020-2023年期间评估的669名患者的回顾性分析.
- 对于疑似RASopathy病例的基因检测 (外体序列) 和心血管评估.
- 机器学习 (随机森林) 用于13个临床红旗进行预测分析.
主要成果:
- 在34例疑似病例 (24例确诊病例) 中,RAS病的诊断收益率为71% .
- 肺狭窄症 (PVS) 和面部形状乱被确定为最强的预测因素.
- 使用PVS和面部异形的模型实现了0.86.6的AUC.
结论:
- PVS和面部异形是RASopathy诊断的关键指标,需要进行基因检测.
- 机器学习有效地确定了RASopathies的关键临床预测因素.
- 对于开发的ML模型,需要进一步的外部验证.
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