对于妊娠糖尿病的LASSO回归衍生的第一季度 (9-14+6周) 风险分层模型:在回顾性中国队列中开发,验证和开放访问的网络工具
Xiao Wang1, Jin Zhang1, Shitong Zhan1
1Department of Obstetrics and Gynaecology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201,600, China.
Archives of gynecology and obstetrics
|March 11, 2026
概括
一个新的妊娠糖尿病 (GDM) 预测模型使用常规的第一季度数据进行早期风险分层. 这种资源高效的工具有助于及时干预,改善母亲和新生儿的结果.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 怀孕期糖尿病 (GDM) 的鉴定对于预防孕产妇和新生儿不良后果至关重要.
- 目前的GDM预测模型受到不一致的变量选择和不切实际的生物标志物的限制.
- 需要有效的GDM预测工具,利用随时可用的临床数据.
研究的目的:
- 开发和验证一个资源高效的GDM预测模型.
- 用常规可用的第一季度临床指标来预测GDM.
- 部署验证的模型作为临床应用的开放访问网络工具.
主要方法:
- 分析了1818个怀孕的回顾性队列,随机分为培训 (70%) 和验证 (30%) 组.
- 对比了包括后勤回归和LASSO回归 (1SE规则,MIN规则) 在内的预测选策略.
- 用ROC曲线 (AUC),校准图和临床决策/影响曲线来评估模型性能.
主要成果:
- 拉索-1SE模型表现出最佳的准确性和简单性,AUC为0.717 (95% CI 0.681-0.753).
- 该模型表现出良好的灵敏度 (69.7%),特异性 (64.9%) 和高正预测值 (92.3%).
- 强大的校准和临床实用性得到证实,导致开发了一个名ogram和web计算器.
结论:
- 成功开发了一个资源效率高的工具,用于早期GDM风险分层.
- 该工具利用了常规的临床变量,提高了各种医疗机构的可访问性.
- 这有助于及时进行干预,可能改善妊娠结果.
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