摘要
目的 系统检索和评价产后母乳喂养行为中断的风险预测模型,为临床开发、优化、使用合适的模型提供参考依据.方法 检索Web of Science、Cochrane Library、PubMed、Embase、CINAHL、中国知网、中国生物医学文献数据库、维普以及万方数据库中有关母乳喂养行为中断的风险预测模型研究,检索时限为建库至2024年4月17日.2名研究者先独立筛选文献并提取数据,采用预测模型研究的偏倚风险评估工具(prediction model risk of bias assessment tool,PROB AST)评价纳入的研究.结果 共纳入13项研究,涉及18个模型.11项研究报告了区分度,7项研究报告了校准方法,6项研究进行内部验证,3项研究进行外部验证,1项研究采用内外部验证结合的方式.母亲的年龄、既往喂养经验、母乳喂养自我效能、多胎妊娠是报告中最常见的预测因子.6项研究适用性较好,但整体偏倚风险较高.结论 母乳喂养行为中断风险预测模型的研究尚处于发展阶段.模型的偏倚风险整体较高,主要集中在数据分析部分,未来应进一步规范报告流程与统计分析,并通过外部验证评估模型在临床实践中的有效性和可行性.
Abstract
Objective To systematically search and evaluate the risk prediction models of postpartum breastfeeding behavior interruption,and to provide reference for clinical development,optimization and application of appropriate models.Methods The literature on risk prediction models of breastfeeding behavior interruption were searched in Web of Science,Cochrane Library,PubMed,Embase,CINAHL,CNKI,CBM,VIP and Wanfang Data.The retrieval time spanned from the inception to April 17,2024.Two researchers independently screened the literature and extracted data,and the included studies were evaluated using the prediction model risk of bias assessment tool(PROBAST).Results A total of 13 studies were included,involving 18 models.Differentiation was reported in 11 studies;calibration methods were reported in 7 studies;internal validation was performed in 6 studies;external validation was performed in 3 studies,and a combination of internal and external validation was used in 1 study.Maternal age,previous feeding experience,breastfeeding self-efficacy,and multiple pregnancies were the most common predictors reported.Six studies had good applicability but a higher risk of overall bias.Conclusion The research on risk prediction models of breastfeeding behavior interruption is still in the development stage.The risk of bias of the models is generally high,which is mainly reflected in data analysis.In the future,the reporting process and statistical analysis should be further standardized,and the effectiveness and feasibility of the model in clinical practice should be evaluated through external verification.
国家科技期刊平台
NSTL
万方数据