在医院出院时分层化产后抑郁症的风险
Mark A Clapp1, Victor M Castro2,3, Pilar Verhaak2
1Department of Obstetrics and Gynecology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.
medRxiv : the preprint server for health sciences
|June 10, 2024
概括
一种新的风险分层模型可以在出院前识别患产后抑郁症 (PPD) 高风险的患者. 该工具有助于针对性干预,预防和管理PPD.
科学领域:
- 产科和妇科 产科和妇科
- 精神病学是一个精神病学.
- 医疗保健中的机器学习
背景情况:
- 产后抑郁症 (PPD) 显著导致母亲的发病率和死亡率.
- 定期对PPD进行查具有局限性,特别是在资源有限的环境中.
- 风险分层模型可以为PPD提供有针对性的干预措施.
研究的目的:
- 开发和验证一个可概括的PPD风险分层模型.
- 利用在常规临床护理中收集的信息来预测PPD风险.
- 为了确定没有抑郁病史的患有PPD风险的患者.
主要方法:
- 从2017年至2022年,在多家医院对29168人进行了回顾性队列研究.
- 构建和外部验证弹性网模型以预测PPD.
- 该模型使用了社会人口统计学因素,病史和产前查数据.
主要成果:
- 9.3%的个体在产后6个月内经历了PPD.
- 该模型显示了良好的区分 (AUC 0.721) 和校准.
- 在90%的特异性下,PPV为28.0%,NPV为92.2%.
结论:
- 一个简单的机器学习模型可以有效地分层PPD在出院前的风险.
- 这个工具有助于个人化产后护理计划为PPD.
- 早期风险识别有助于预防,查和管理PPD.
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