使用潜变量建模来识别早产的病因异质性
Kim Steven Betts1, Rosa Alati1, Peter Baker2
1School of Population Health, Curtin University, Perth, Western Australia, Australia.
Journal of paediatrics and child health
|September 21, 2024
概括
一小群患病率高的母亲在连续三次分娩中始终经历过早产. 识别这种高风险子组可以改善对早产的理解和结果.
科学领域:
- 生殖健康 生殖健康
- 围产期流行病学 围产期流行病学
- 孕产妇和胎儿医学 孕产妇和胎儿医学
背景情况:
- 过早分娩仍然是新生儿发病率和死亡率的主要原因.
- 对于有针对性的干预措施来说,识别具有重复早产风险高的母亲至关重要.
- 了解连续分娩多病症和复发的模式对于风险分层至关重要.
研究的目的:
- 为了确定一个特定的母亲的子组在高风险的早产.
- 根据多病症和连续三次分娩中复发的经验性类别来定义这个子组.
主要方法:
- 隐性类分析 (LCA) 用于确定不同的孕产妇健康轨迹.
- 分析了来自澳大利亚昆士兰州7714名母婴母婴,分别连续三次 (2009-2015年) 单胎分娩的数据.
- 评估了与特定类别相关的孕产妇和妊娠相关因素的相关性.
主要成果:
- 一个四类解决方案最好地描述了数据:"规范" (健康),早产/高发病率,分娩发病率和早产/低发病率.
- 一个小但高度病态的班级 (<2%的样本) 始终经历过早产.
- 高发病率和早产,低发病率类别在连续分娩中显示出强烈的连续性,独立于其他因素.
结论:
- 确定了一个独特的,高度病态的母亲类别,经常出现早产.
- 这个子组在连续的分娩中表现出强烈的连续性,这表明固有的风险因素.
- 对这一高风险群体的进一步调查可能会为早产的病因提供见解,并改善出生结果.
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