预测可能流产的第一季度妊娠结果:多变量逻辑回归和机器学习模型的比较
L Sammut1, P Bezzina1, V Gibbs2
1Department of Radiography, Faculty of Health Sciences, University of Malta, Malta.
Radiography (London, England : 1995)
|September 5, 2025
概括
通过结合超声波 (US) 和生物化学 (BC) 标志物,可以更好地预测可能流产的结果. 机器学习模型,如随机森林, 提高准确性,
科学领域:
- 生殖医学
- 产妇和胎儿医学
- 临床预测模型
背景情况:
- 可能流产 (TM) 影响高达30%的已知的怀孕,增加不良后果的风险.
- 早期预测 TM 妇女的妊娠结果对于及时干预和管理至关重要.
- 目前对TM结果的预测工具需要进一步改进以提高准确性.
研究的目的:
- 评估第一季度超声波 (US) 和生化 (BC) 标志物的预测价值.
- 使用多变量逻辑回归 (MLR) 和随机森林 (RF) 模型评估美国和BC标志物的联合预测性能.
- 探索机器学习在改善临床风险分层中的潜力.
主要方法:
- 对118名具有可行的单胎妊娠的妇女进行前性队列研究,这些妇女经历了流产威胁的症状.
- 收集的数据包括第一季度的美国标志物 (例如,子宫长度,妊娠囊径),BC标志物 (例如,孕激素,sFlt-1:PlGF比率) 和孕产妇因素.
- 怀孕结果被跟踪到期;预测模型 (MLR,RF) 已开发并评估准确性.
主要成果:
- 在118个TM病例中观察到77%的活产率和23%的流产率.
- MLR确定了孕激素,子宫长度,平均妊娠囊径,热囊细胞厚度,sFlt-1:PlGF比率和母亲年龄作为重要的预测因素.
- 随机森林建模实现了93.1%的准确性 (AUC=0.97),显著超过了MLR (82.7%的准确性,AUC=0.89).
结论:
- 第一个三个月的超声波和生化标志物对可能流产的预测具有显著的价值.
- 与传统的回归模型相比,机器学习,特别是随机森林,在预测威胁流产的结果方面表现出卓越的表现.
- 这些发现支持开发先进的工具,用于个性化风险分层,监测和咨询可能流产的情况.
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