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基于类固醇的查原发性阿尔多斯特:抗高血压药物的影响
Georgiana Constantinescu1, Sven Gruber2, Sybille Fuld1
1Department of Medicine III, University Hospital Carl Gustav Carus (G.C., S.F., C.P., G.F.E.).
Hypertension (Dallas, Tex. : 1979)
|July 31, 2024
概括
机器学习类固醇概率评分为诊断原发性阿尔多斯 (PA) 提供了素独立的方法,显示抗高血压药物的干扰比阿尔多斯与素比率 (ARR) 低. 这种方法可以提高PA测试的诊断准确性,当药物停用是不可行的.
科学领域:
- 内分泌学 在内分泌学.
- 临床化学 临床化学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 由于抗高血压药物干扰测试结果,特别是renin,诊断原发性阿尔多斯特隆症 (PA) 是具有挑战性的.
- 阿尔多与的比率 (ARR) 是常用的,但容易受到药物干扰的影响.
- 需要一种独立于雷宁测量的方法来提高PA诊断准确度.
研究的目的:
- 评估基于机器学习的类固醇概率得分,作为PA的素独立诊断工具.
- 为了将类固醇概率得分的诊断性能与干扰药物的影响下ARR进行比较.
- 评估特定抗高血压药物对类固醇和诊断准确性的影响.
主要方法:
- 对839名为PA测试的患者进行前性多中心队列研究.
- 利用了血类固醇组学和阿尔多与素的比率 (ARR).
- 分析了接收器操作特征曲线和个人药物对类固醇标记物和诊断分数的影响.
主要成果:
- 与ARR相比,类固醇概率得分显示干扰抗高血压药物的影响最小.
- 矿物甲酸类受体对抗剂导致假阳性类固醇概率得分和假阴性ARRs.
- 其他抗高血压药物对类固醇概率得分的影响很小或不存在,对ARR有优势.
结论:
- 基于机器学习的类固醇概率得分为PA诊断提供了一个有前途的素独立方法.
- 这些评分不太容易受到常见抗高血压药物的干扰,而不是ARR.
- 类固醇概率得分提高了诊断可靠性,当药物取消是不可能的.
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