使用真实世界的数据进行连续参考区间的完全自动估计的管道
Tatjana Ammer1,2, André Schützenmeister2, Hans-Ulrich Prokosch1
1Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Scientific reports
|August 18, 2023
概括
这项研究引入了一种自动化管道,用于创建连续的参考间隔,这对于解释所有年龄段的实验室结果至关重要. 这种方法消除了用户的输入,并通过精确捕捉与年龄相关的生理变化来改善临床决策.
科学领域:
- 临床化学 临床化学
- 生物统计学 生物统计学
- 计算生物学是一种计算生物学.
背景情况:
- 参考间隔对于准确的实验室测试解释至关重要.
- 连续参考间隔提供了更好的特定年龄的生理洞察力,但传统方法是限制性的.
- 现有的间接方法缺乏对诸如年龄等共变量依赖性的自动化.
研究的目的:
- 使用间接方法开发一个完全自动化的管道来估计使用间接方法的连续参考区间.
- 为了将年龄作为参考区间估计中的连续共变量整合起来.
- 为生成高精度百分位图和连续参考区间提供无参数的解决方案.
主要方法:
- 开发了一个集成的管道,用于自动连续参考区间估计.
- 利用一个广义的附加模型用于位置,规模和形状 (GAMLSS).
- 采用基于离散模型估计的间接方法 (RefineR).
主要成果:
- 管道自动生成连续的参考间隔,不受主观用户输入的影响.
- 结果表明与CALIPER,PEDREF和制造商的既定参考间隔有很好的一致性.
- 该方法可以将测试结果转换为z分数,并集成到实验室系统中.
结论:
- 开发的管道为间接连续参考区间估计提供了第一个无参数,全自动化的解决方案.
- 这种方法产生高精度百分位图和连续参考间隔.
- 这些发现有可能通过更好地解释实验室测试结果来加强临床决策.
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