纵向双胞胎生长不一致的模式和不良周产期结果
Smriti Prasad1, Isil Ayhan1, Doaa Mohammed1
1Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, London, United Kingdom.
American journal of obstetrics and gynecology
|January 9, 2025
概括
双胞胎怀孕中高度交织的胎儿生长失调显著增加了不良周产期结果的风险. 结合生长不一致性模式和大脑胎盘比率多普勒评估的预测模型在识别这些风险方面提供了卓越的准确性.
科学领域:
- 围产科和孕产妇医学
- 胎儿生长和发育 胎儿生长和发育
- 医学超声波和多普勒评估
背景情况:
- 双胞胎怀孕中生长失调是不良围产期结果的已知危险因素.
- 相互交织的生长失调的进展模式和多普勒评估的作用需要进一步研究.
研究的目的:
- 纵向评估双胞胎怀孕中的交织生长和多普勒异调.
- 使用无监督学习识别不一致性进展的独特模式.
- 评估这些模式对不良围产期结果的预测价值.
主要方法:
- 823例双胞胎怀孕的回顾性队列研究,在18周后至少进行3次超声波检查.
- 排除特定的高风险双胞胎怀孕 (例如单胎,TTTS,异常).
- 分析使用多层混合效应回归和k-平均集群来识别不一致模式;通过ROC曲线评估预测准确性.
主要成果:
- 确定了五种不同的交织成长不一致模式:低稳定,轻度下降,低增长,轻度增长和高稳定.
- 高稳定不一致性模式显示,围产期发病率 (46.5%) 和死亡率 (9.3%) 显著上升.
- 一个整合异常模式和脑胎盘比率 (CPR) 异常的预测模型实现了不良结果的最高AUC (0.802).
结论:
- 无监督机器学习确定了5种相互交织的胎儿生长异常的不同轨迹.
- 持续的高不一致性表明剂量-反应关系与不良围产期结局.
- 不一致轨迹和心肺复苏失调的组合模型提供了对周围生育不良结果的优越预测.
关键词:
不和 不和 不和 不和负面的负面影响人工智能的人工智能是人工智能.胎儿死亡 胎儿死亡胎儿生长限制 限制胎儿生长在子宫内死亡.在纵向的长度上.机器学习是机器学习.疾病发病率 疾病发病率 疾病发病率死亡率 死亡率多重怀孕是一个多重怀孕.新生儿新生儿新生儿新生儿新生儿单位 新生儿单位结果就是结果.周产阶段 周产阶段单身怀孕 单身怀孕对于妊娠年龄来说小.一个死产的死亡.双胞胎 双胞胎 双胞胎 双胞胎 双胞胎更多相关视频
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