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在输卵管外妊娠管理中的炎症指数,机器学习和人工智能

Uğurcan Zorlu¹, Senem Arda Düz², Gül Kurtaran³

¹Ankara Bilkent City Hospital, Clinic of Perinatology, Ankara, Türkiye.

Turkish journal of obstetrics and gynecology

|February 20, 2026

概括

血液炎症指数如AISI,SIRI和FAR有效预测管外妊娠 (TEP) 中的甲状腺素治疗成功. 机器学习模型与这些生物标志物相结合,改善了个性化的TEP管理.

科学领域:

生殖内分泌学生殖内分泌学
临床化学临床化学
医疗信息学医疗信息学

背景情况:

输卵管外妊娠 (TEP) 带来了重大风险,甲状腺素 (MTX) 是主要的医疗治疗方法.
预测MTX治疗结果对于有效的TEP管理和预防手术干预至关重要.
目前预测治疗成功的方法有局限性,需要新的预测标记.

研究的目的:

评估各种血液学和生化炎症指数在TEP中对MTX治疗结果的预测能力.
开发和评估机器学习 (ML) 模型,用于在接受MTX治疗的TEP患者中进行个性化风险分层.

主要方法:

一项对293名血液动力学稳定的TEP患者进行的回顾性队列研究,这些患者接受了单剂量MTX治疗.
计算和分析炎症指数:中性粒细胞与淋巴细胞的比率 (NLR),全身炎症反应指数 (SIRI),全身炎症总指数 (AISI) 和纤维素与白蛋白的比率 (FAR).
评估五个监督的ML算法,使用接收器操作特征分析来预测治疗成功,需要额外的MTX或手术.

主要成果:

单剂量MTX在65.5%的患者中成功;18.4%需要额外的MTX,16%需要手术.
在AISI,SIRI和FAR中,手术干预的预测准确度很高 (AUC分别为0.929,0.899,0.847).
纯粹的贝叶斯ML模型实现了卓越的外科预测 (准确率98.3%,AUC 0.998),而随机森林和梯度增强在预测额外的MTX需求方面表现出色.

结论:

系统性炎症综合指数 (AISI),系统性炎症反应指数 (SIRI) 和纤维素与白蛋白比率 (FAR) 是TEP中MTX失败和手术必要性的强有力的预测因素.
将这些炎症生物标志物与ML模型相结合,可显著提高预测性能,支持个性化的TEP管理策略.
建议在这些预测模型被广泛采用临床之前进行多中心前性验证.

关键词:

异卵性怀孕异卵性怀孕异卵性怀孕这是一种炎症炎症炎症炎症.炎症标志物炎症标志物机器学习是机器学习.甲基特雷克萨酸盐的使用方法

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