使用常规患者数据估计参考变化值:一种新的病理数据库方法
Eirik Åsen Røys1,2, Kristin Viste1,2, Ralf Kellmann1
1Hormone Laboratory, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway.
Clinical chemistry
|November 4, 2024
概括
使用常规患者数据计算参考变化值 (RCV) 的新方法提供了比传统方法更具临床相关性的结果. 这种方法通过考虑患者测试中的现实世界的变化来增强实验室实践.
科学领域:
- 临床化学 临床化学
- 实验室医学 实验室医学
- 生物标记分析 生物标记分析
背景情况:
- 传统的基准变化值 (RCVs) 计算使用主体内生物变化 (CVI) 和分析变化 (CVA) 不包括分析前变化或患者特定的CVI.
- 这种遗漏导致RCVs可能不准确地反映常规临床实践或与临床医生的期望保持一致.
- 建议采用一种新的方法,直接从例行患者数据中提取RCV,以提高临床相关性.
研究的目的:
- 开发和验证一种使用例行患者数据估计参考变化值 (RCV) 的新方法.
- 评估从当地实验室数据中得出的RCVs与传统方法相比的临床相关性.
- 为了证明这种新方法在不同结果分布的各种生物标志物的适用性.
主要方法:
- 使用refineR算法从从实验室信息系统 (LIS) 获得的连续患者数据计算RCV.
- 该模型在表现出不同结果比率分布的生物标志物上进行了测试,从正常到日志正常.
- 结果与基于常规公式的RCV进行了比较,并使用蒙特卡洛模拟进行了验证.
主要成果:
- 来自LIS数据的RCVs报告了多种生物标志物,包括11-deoxycortisol,17-hydroxyprogesterone,albumin,androstenedione,cortisol,cortisone,creatinine,酸盐和.
- 基于公式的RCV估计显示了可比但略低的值.
- 蒙特卡洛模拟证实了LIS数据驱动的RCV方法的有效性和适用性.
结论:
- 参考变化值 (RCV) 可以有效地直接从患者结果中估计,而不需要对连续结果比率的分布做假设.
- 这种方法使实验室能够建立根据其特定的当地实践和患者群体量身定制的RCV.
- 拟议的方法为在常规实验室诊断中进行RCV确定提供了更实用和临床相关的替代方案.
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