使用连续葡萄糖监测数据识别的离散葡萄糖概况及其与不良妊娠结果的关联
Ashley N Battarbee1, Sara M Sauer2, Ayodeji Sanusi1
1Center for Women's Reproductive Health, University of Alabama at Birmingham, Birmingham, AL; Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, AL.
American journal of obstetrics and gynecology
|March 25, 2024
概括
机器学习在孕期糖尿病患者的孕妇中确定了四种不同的葡萄糖特征. 控制不良的葡萄糖,特别是过夜高血糖症,显著增加了孕前和孕期新生儿的大小的风险.
科学领域:
- 产科和妇科 产科和妇科
- 内分泌学 在内分泌学.
- 围产儿医学 围产儿医学
背景情况:
- 持续的葡萄糖监测 (CGM) 有助于了解怀孕期间的葡萄糖动态.
- 有限的研究存在于联合CGM指标及其与不良妊娠结果的关联.
研究的目的:
- 利用机器学习来识别妊娠期糖尿病患者 (PGDM) 的孕妇不同的葡萄糖概况.
- 调查这些已识别的葡萄糖概况与不良妊娠结果之间的关联.
主要方法:
- 在177名患有1型或2型糖尿病的孕妇中,使用Dexcom G6 CGM进行了回顾性队列研究.
- 分析了39个每周的CGM指标,使用主要成分分析和k-means集群来定义4个葡萄糖概况.
- 多变量逻辑回归分析了个人资料和结果之间的关联,例如早产,孕前和新生儿并发症.
主要成果:
- 鉴定了四种不同的葡萄糖概况:控制良好的,高可变性,最小的昼夜变化和夜间高血糖.
- 高可变性概况与妊娠年龄大大的新生儿的概率增加有关 (aOR 3.34).
- 最小的昼夜变化特征与更高的早产率 (aOR 2.59),剖腹产 (aOR 2.76) 和NICU入院 (aOR 4.08) 相关.
- 夜间高血糖概况与更高的孕前 (aOR 2.54),LGA新生儿 (aOR 3.72),新生儿低血糖 (aOR 3.53) 和NICU入院 (aOR 3.15) 的几率相关.
结论:
- 机器学习有效地识别了PGDM孕妇的不同葡萄糖概况.
- 长期的孕产妇高血糖症对不良结果的风险比单独的高葡萄糖变异性更大.
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