开发下一代参考间隔模型,以建立基于医疗数据的参考间隔:当前状态,算法和未来考虑
Chaochao Ma1,2, Zheng Yu3, Ling Qiu1,4
1Department of Laboratory Medicine, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Critical reviews in clinical laboratory sciences
|December 26, 2023
概括
下一代参考间隔模型使用大数据创建不断变化的参考间隔,改善跨不同患者人口统计学的诊断准确性. 这些先进的模型解决了传统间隔的局限性,增强了临床决策和精准医学.
科学领域:
- 实验室医学 实验室医学
- 临床病理学 临床病理学
- 生物信息学是一种生物信息学.
背景情况:
- 参考间隔对于解释实验室测试结果至关重要.
- 传统的RI,通常是静态的或按年龄/性别分组的,可能导致误解.
- 大数据和先进计算为RI开发提供了新的方法.
研究的目的:
- 审查下一代参考区间模型的发展.
- 突出这些先进模型的优势和挑战.
- 为其创建和验证提供一个框架.
主要方法:
- 使用直接或间接采样来进行参考个体选择.
- 在连续模型中使用曲线拟合方法 (splines,多项式回归).
- 利用大数据分析来构建模型.
主要成果:
- 目前对下一代参考区间模型的发展有限.
- 存在不同的方法,可能会导致混乱.
- 模型构建可能很复杂,特别是间接采样.
结论:
- 下一代参考间隔模型提供连续的,特定于年龄和性别的间隔,以提高准确性.
- 需要标准化指导方针和进一步的研究才能得到广泛采用.
- 这些模型是通过增强数据利用来推进精准医学的关键.
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