主管:国家卫生健康委员会
主办:国家卫生健康委医院管理研究所
中国科技核心期刊(中国科技论文统计源期刊)
中国科学引文数据库(CSCD)核心库期刊
《中文核心期刊要目总览》核心期刊

中国护理管理 ›› 2023, Vol. 23 ›› Issue (11): 1637-1642.doi: 10.3969/j.issn.1672-1756.2023.11.008

• 论著 • 上一篇    下一篇

产妇泌乳启动延迟风险预测模型及评估工具的构建及验证

胡珊珊 刘敏 孙菲 刘君 蒋盘华   

  1. 无锡市妇幼保健院护理部,214002 江苏省无锡市(胡珊珊,刘敏);产科(刘君,蒋盘华);江南大学无锡医学院(孙菲)
  • 出版日期:2023-11-15 发布日期:2023-11-15
  • 通讯作者: 刘敏,硕士,主任护师,护理部主任,E-mail:liumin_76@163.com
  • 作者简介:胡珊珊,硕士,主管护师,护理部主任助理
  • 基金资助:
    无锡市科技局科技项目(Y20212036);无锡市中医药科技项目(ZYKJ202113);无锡市妇幼健康科研项目(FYKY202201)

Construction and validation of risk prediction model and evaluation tool for delayed onset of lactogenesis

HU Shanshan, LIU Min, SUN Fei, LIU Jun, JIANG Panhua   

  1. Department of Nursing, Wuxi Maternal and Child Health Hospital, Wuxi, Jiangsu province, 214002, China
  • Online:2023-11-15 Published:2023-11-15
  • Contact: E-mail:liumin_76@163.com

摘要: 目的:构建并验证产妇泌乳启动延迟风险预测模型及评估工具,为早期识别泌乳启动延迟危险人群提供借鉴。方法:对产妇泌乳启动延迟的风险因素进行Meta分析,以各风险因素OR值的自然对数为模型的β系数,以产后72?h产妇泌乳启动延迟发生率与未发生率比值的自然对数为模型的a系数,建立预测模型及评估工具。收集420例产妇资料,分析模型及评估工具的预测性能。结果:产妇泌乳启动延迟发生率为29.76%,预测模型为Logit(P)=-0.859+0.174×高龄+0.148×初产+0.113×剖宫产+0.239×孕前超重或肥胖+0.182×孕期增重过多+0.166×妊娠期糖尿病+0.336×妊娠期高血压疾病+0.223×开奶时间晚+0.315×焦虑+0.285×抑郁,模型ROC曲线下面积为0.765,95%CI(0.717,0.812),敏感度为0.736,特异度为0.712。评估工具得分为0~20分,分值≥6分为高危人群,工具ROC曲线下面积为0.751,95%CI(0.702,0.799),敏感度为0.688,特异度为0.719。结论:基于Meta分析建立的产妇泌乳启动延迟风险预测模型和评估工具有较好的预测能力,可作为早期识别泌乳启动延迟高危人群的工具。

关键词: 泌乳启动;风险因素;预测模型;评估工具;Meta分析

Abstract: Objective: To construct and validate the risk prediction model and evaluation tool for delayed onset of lactogenesis, to provide an effective tool for early identification of group at risk of delayed onset of lactation. Methods: A Meta-analysis of risk factors for delayed onset of maternal lactation was performed, and the natural logarithm of the combined risk of each risk factor was used as the β coefficient of the model, and the natural logarithm of the ratio of the incidence of delayed onset of maternal lactation to the non-incidence rate at 72 hours after delivery was used as the a coefficient of the model to build the prediction model. Data of 420 maternal cases were collected, and the predictive performance of the model was analyzed. Results: The prediction model for delayed lactation onset was logit (P)= -0.859+0.174×advanced age+0.148×primiparity+0.113×caesarean+0.239×pre-pregnancy overweight or obese+0.182×excessive weight gain during pregnancy+0.166×gestational diabetes mellitus+0.336×hypertensive disorder complicating pregnancy+0.223×breastfeeding started later+0.315×anxiety+0.285×depression. The area under ROC for the model was 0.765, 95%CI (0.717, 0.812), the sensitivity was 0.736, the specificity was 0.712. The risk assessment tool score was 0-20, the score ≥6 was high-risk group, and the area under ROC for the tool was 0.751, 95%CI (0.702, 0.799), the sensitivity was 0.688, and the specificity was 0.719. Conclusion: The risk prediction model and evaluation tool for delayed onset of lactogenesis based on Meta-analysis have good predictive power, and can be used as a tool for early prediction of delayed onset of lactogenesis.

Key words: onset of lactogenesis; risk factor; prediction model; assessment tool; Meta-analysis

中图分类号:  R473.71;R197