对于重复流产的怀孕结果的风险预测模型:叙述性的系统性审查
Qiliang Jian1, Fangxiang Mu1, Kexin Wang1
1Department of Reproductive Medicine, Lanzhou University Second Hospital, Lanzhou, China.
Frontiers in endocrinology
|June 13, 2025
概括
对重复流产 (RPL) 的风险预测模型有希望,但在临床应用中存在方法上的缺陷和高偏差. 未来的研究需要提高数据质量和可靠工具的验证.
科学领域:
- 生殖医学 生殖医学
- 临床流行病学 临床流行病学
- 生物统计学 生物统计学
背景情况:
- 复发性流产 (RPL) 是一个重大的临床挑战,常常有不明原因.
- 现有的RPL怀孕结果风险预测模型经常缺乏客观的评估和验证.
研究的目的:
- 系统地审查和评估已公布的风险预测模型,用于重复流产的妇女的怀孕结果.
- 评估这些模型的方法质量,偏差风险和适用性.
主要方法:
- 在2024年8月,在主要数据库 (PubMed,Embase,Web of Science,CNKI,CMAJ) 进行了全面的文献搜索.
- 包括的研究报告了RPL怀孕结果临床预测模型的开发和/或验证.
- 使用PROBAST工具评估了偏差风险和适用性,采用CHARMS检查清单进行数据提取.
主要成果:
- 包括15项涉及22个RPL风险预测模型的研究.
- 后勤回归是主要的建模技术,预测因素包括母亲的年龄和先前的妊娠损失史.
- 虽然许多模型显示出良好的区分性表现 (AUC>0.80),但由于方法上的缺陷,特别是验证 (只有3次外部验证),所有模型都被评估为具有适用性偏差的高风险.
结论:
- 目前对RPL怀孕结果的风险预测模型具有高偏差风险,限制了它们的临床适用性.
- 模型开发和验证中的方法缺陷是关键问题.
- 未来的研究必须优先考虑数据质量,样本多样性和模型透明度,以提高可靠性和广泛适用性.
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