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护理学报 ›› 2023, Vol. 30 ›› Issue (23): 1-5.doi: 10.16460/j.issn1008-9969.2023.23.001

• 研究生园地 •    下一篇

基于Meta分析构建早产儿母亲泌乳建立风险预测评分模型

孙菲1, 刘敏2a, 胡珊珊2a, 吴蕾2b, 刘君2b, 李萍2c   

  1. 1.江南大学无锡医学院,江苏 无锡 214122;
    2.无锡市妇幼保健院 a.护理部;b.产科;c.新生儿科,江苏 无锡 214002
  • 收稿日期:2023-07-15 出版日期:2023-12-10 发布日期:2024-01-09
  • 通讯作者: 刘敏（1976-）,女,江苏无锡人,硕士,副主任护师,护理部主任。E-mail: liumin_76@163.com
  • 作者简介:孙菲（1999-）,女,河南信阳人,本科学历,硕士研究生在读,护师。
  • 基金资助:
    无锡市科技发展资金项目（Y20212036）; 无锡市中医药管理局科技项目（ZYKJ202113）; 无锡市妇幼健康科研和适宜技术推广项目（FYKY202201）

Construction of prediction scoring model for lactation risk of mothers experiencing premature birth: a Meta-analysis

SUN Fei1, LIU Min2a, HU Shan-shan2a, WU Lei2b, LIU Jun2b, LI Ping2c   

  1. 1. Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China;
    2a. Dept. of Nursing Administration; 2b. Dept. of Obstetrics; 2c.Dept. of Neonatology, Wuxi Maternal and Child Health Hospital, Wuxi 214002, China
  • Received:2023-07-15 Online:2023-12-10 Published:2024-01-09

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摘要： 目的 构建并验证早产儿母亲泌乳建立风险预测评分模型,为早期识别早产母亲泌乳建立危险人群提供借鉴。方法 运用Meta分析早产母亲泌乳建立的危险因素,以各危险因素综合危险度的自然对数为系数,以早产母亲泌乳建立失败发生率与未发生率比值的自然对数为模型的系数,建立模型;再根据各危险因素的系数对风险因素进行赋分,构建风险预测评分模型。收集2022年3—9月112例早产儿母亲资料,分析该风险预测评分模型的预测性能。结果 早产母亲泌乳建立模型Logit（P）=-0.072+0.389×是否年龄≥30岁+0.452×是否合并妊娠期高血压疾病+1.008×是否合并妊娠期糖尿病+0.434×是否产后抑郁+0.538×是否泌乳II期启动延迟+0.607×是否每日挤奶频次<8次+0.515×是否开奶时间≥6 h+0.445×是否每日睡眠时间<8 h;模型ROC曲线下面积为0.900（95%CI：0.841~0.958）,约登指数为0.717,临界值为2.070,概率值为88.8%,敏感度为0.889,特异度为0.828。早产母亲泌乳建立风险预测评分模型验证结果显示,得分为0~113分,评分>55.5分为高危人群,ROC曲线下面积为0.900（95%CI：0.842~0.958）,约登指数为0.717,敏感度为0.889,特异性为0.828,阳性预测值为82.8%,阴性预测值为88.9%,总体预测准确率为93.1%。结论 基于Meta分析构建的早产母亲泌乳建立风险预测评分模型有较好的预测效能,且临床使用便捷,可用于筛查早产母亲泌乳建立危险人群。

关键词: 泌乳建立, 预测模型, 风险评分, Meta分析, 危险因素, 母乳喂养

Abstract: Objective To construct and verify the prediction scoring model for lactation risk of mothers experiencing premature birth, and provide reference for early identification of risk group. Methods Meta-analysis was used to analyze the factors of lactation risk in mothers of premature infants, and the model was established using the natural logarithm of the overall risk level for each risk factor as the coefficient, and the natural logarithm of the ratio between the failure rate and non-failure rate of lactation among mothers of preterm infants as the model coefficient. The risk factors were then assigned scores based on their respective coefficient values to construct the model. The data of 112 mothers experiencing premature birth from March to September, 2022 were collected, and the predictive performance of the model was analyzed. Results The model was constructed with logit(P)=-0.072+0.389 age+0.452 gestational hypertension+1.008 gestational diabetes+0.434 postpartum depression+0.538 lactation phase II start-up delay+0.607 daily milking frequency+0.515 milk opening time+0.445 lack of sleep. The area under the ROC curve of the model was 0.900 (95%CI: 0.841~0.958); the Jordan index 0.717, and the critical value 2.070. The probability value of the model was 88.8%, with sensitivity and specificity of 0.889 and 0.828 respectively. The verification results of the model showed that the score ranged from 0 to 113, and a score above 55.5 indicated a high risk of lactation. The area under ROC curve was 0.900 (95%CI:0.842~0.958), and the Jordan index 0.717, with the sensitivity and specificity of 0.889 and 0.828. The positive predictive value was 82.8%, and the negative predictive value 82.8%. Conclusion The prediction scoring model of lactation risk of mothers experiencing premature birth based on meta-analysis has good prediction efficiency, and can be used for lactation screening of premature mothers and establishment of risk groups.

Key words: lactation establishment, prediction model, risk scoring, Meta analysis, risk factor, breastfeeding

中图分类号: 

  • R473.71
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