评估用于囊性纤维化治疗的精准医学工具,以实现种族和民族公平
Stephen P Colegate1, Anushka Palipana2, Emrah Gecili1,3
1Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Journal of clinical and translational science
|September 2, 2024
概括
预测囊性纤维化肺部恶化 (PEx) 的精准医学算法显示种族偏见. 这种与CF突变和位置相关的黑人患者表现不佳,突显了卫生数据和算法中的系统性种族主义.
科学领域:
- 医学研究 医学研究
- 卫生公平性健康公平性
- 精准医学是一门精准的医学.
背景情况:
- 囊性纤维化 (CF) 患者面临肺部恶化 (PEx),急性肺功能下降.
- 精准医学算法旨在预测PEx,但可能包含来自训练数据的偏差.
- 偏见的预测算法可以加剧健康不平等.
研究的目的:
- 在临床和基于位置的精准医学算法中评估种族和种族偏见,预测PEx.
- 确定导致不同种族群体PEx预测表现差异的因素.
主要方法:
- 一个非静止的高斯斯托卡斯过程模型被用来预测PEx在3,6和12个月内.
- 分析了来自美国CF基金会患者登记处的26,392名CF患者 (2003-2017) 的数据.
- 预测者被选,以了解模型性能差异.
主要成果:
- 与白人或其他种族相比,该算法对黑人患者的PEx预测不太准确.
- 在西班牙裔和非西班牙裔患者之间,预测准确度差异很小.
- 诸如F508del突变,烟雾暴露,接近CF中心和临床访问等因素因种族而异,影响了预测.
结论:
- 在PEx预测算法的准确性方面存在种族差异.
- CF突变,居住地点和临床因素有助于这些预测不准确性.
- 算法偏见反映了数据收集和精准医学开发中的系统性种族主义.
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