使用现实世界的数据和机器学习来选与心脏形相关的母亲和父亲的特征
Jeremy Brown1, Krista Huybrechts2, Loreen Straub2
1Harvard T.H. Chan School of Public health.
Research square
|July 1, 2024
概括
这项研究选了母亲和父亲的因素与婴儿心脏形的关联. 已知风险如糖尿病得到证实,并确定了潜在的新信号,如fingolimod使用.
科学领域:
- 心脏病学 心脏病学
- 遗传学 遗传学 是一个
- 公共卫生 公共卫生
背景情况:
- 先天性心脏形是婴儿疾病的主要原因之一.
- 了解这些形的原因对于有效的预防至关重要.
- 目前对风险因素的了解有限.
研究的目的:
- 查母亲和父亲的特征与活产婴儿的心脏形之间的关联.
- 为了确定潜在的新型风险因素,先天性心脏缺陷.
- 评估大规模数据查对识别妊娠结果风险因素的有用性.
主要方法:
- 使用的MarketScan美国保险索赔数据从2011年到2021年,包括720381例怀孕.
- 使用后勤回归来估计临床诊断/药物代码与心脏形之间的关联的几率比率.
- 应用了潜伏语义分析和阿普里奥里-SD算法来分组识别的风险代码.
主要成果:
- 选了2000个潜在的关联,识别了81个重要的代码,错误发现率为5%.
- 已确认的已知风险因素,包括孕产妇糖尿病和慢性高血压.
- 检测到潜在的新信号,例如母亲使用fingolimod或azathioprine.
结论:
- 查方法成功地确定了心脏形的已确定的风险因素.
- 潜在的新风险因素需要进一步调查,考虑到可能的混和偏见.
- 这种方法对发现各种妊娠结果的新风险因素充满希望.
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