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确定需要上腺静脉采样的原发性阿尔多斯特症患者:一项多中心研究
Takumi Kitamoto1,2, Tsuyoshi Idé3, Yuta Tezuka4,5
1Endocrinology and Diabetes Center, Yokohama Rosai Hospital, Yokohama, 2220036, Japan. t.kitamoto@chiba-u.jp.
Scientific reports
|December 11, 2023
概括
一种新的机器学习模型可以识别从手术中受益的原发性阿尔多斯特症 (PA) 患者,减少对上腺静脉采样 (AVS) 的需求. 这种方法可以提高诊断的准确性和患者选择PA治疗.
科学领域:
- 内分泌学 在内分泌学.
- 医疗信息学 医疗信息学
- 手术瘤学手术瘤学
背景情况:
- 上腺静脉采样 (AVS) 对于确定原发性阿尔多斯顿主义 (PA) 的亚型至关重要,以指导治愈性高血压治疗.
- 有限的AVS可用性和队列异质性阻碍了开发有效的非AVS战略.
- 分段选择性AVS提供了更高的准确性来识别PA中的外科候选人.
研究的目的:
- 开发和验证用于PA亚型和治疗指示预测的机器学习模型.
- 为了解决跨中心数据异质性和PA队列中缺失的值.
- 通过准确识别将从手术中受益的患者,减少对AVS的依赖.
主要方法:
- 追溯分析了来自三个机构的210个PA案例,其中AVS具有细分选择性.
- 机器学习分类模型的开发与跨中心域调整.
- 应用一种新的数据归算技术来处理机构间的变化.
主要成果:
- 该模型确定了102名PA患者,他们在队列中受益于手术.
- 后勤回归实现了比随机森林和深度学习更高的准确性 (f-score 0.89,0.86),用于预测外科/医疗适应症.
- 综合流程图显示,只有35.2%的PA病例需要AVS,准确率为94.1%.
结论:
- 开发的机器学习模型有效地预测了PA患者的治疗益处.
- 这种方法可以显著减少对AVS的需求,优化患者选择手术.
- 该模型的跨中心适应能力使其在各个机构中具有更广泛的临床应用.
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