复杂罕见疾病诊断中的客观化问题:从测试现有诊断支持系统对纤毛病症的测试中吸取的教训
Carole Faviez1,2,3, Xiaoyi Chen4,5,6, Nicolas Garcelon4,5,6
1Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, Paris, F-75006, France. carole.faviez@inserm.fr.
BMC medical informatics and decision making
|May 24, 2024
概括
诊断支持系统 (DSS) 显示了罕见疾病识别的潜力,但对于诸如纤毛病症等复杂病例需要改进. 需要进一步开发现实世界的临床应用和早期患者诊断.
科学领域:
- 医疗信息学 医疗信息学
- 罕见疾病研究 罕见疾病研究
- 临床决策支持 临床决策支持
背景情况:
- 罕见疾病影响全球4亿人,经常面临诊断延迟.
- 病是一种罕见的单一性疾病,由于遗传和表型异质性,它带来了重大诊断挑战.
- 使用电子健康记录 (EHR) 的诊断支持系统 (DSS) 可以帮助识别未被诊断的罕见疾病患者.
研究的目的:
- 为了评估三种在线罕见疾病的诊断性能,DSSs用于纤毛病.
- 评估DSS在从EHR数据中识别乳毛病病例的有用性.
- 确定DSS应用在罕见疾病诊断方面的挑战和改进领域.
主要方法:
- 使用了两组细毛病例数据集 (已证实/疑似) 和两组对照数据集.
- 从EHR中提取患者表型,并将其映射到人类表型本体学 (HPO) 术语中.
- 使用Orphanet本体学评估DSS诊断能力,将病例与对照进行比较.
主要成果:
- 表现最好的DSS在ROC曲线下的面积为0.72,低于公布的基准值.
- 没有一个被评估的DSS达到专家级别的诊断性能.
- 多系统性纤毛病病例比孤立病例更容易诊断;复杂的表型带来了挑战.
结论:
- 现实世界的DSS评估突出了诊断异质罕见疾病的复杂性.
- 提高DSS与EHR的互操作性,实时验证,数据质量和先进方法的改进至关重要.
- 增强的DSS可以支持早期诊断罕见疾病,改善患者接受治疗的资格.
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