建模妊娠期体重增加的方法:使用安全网人口的电子健康记录数据进行实证应用
Anna Booman1,2, Kimberly K Vesco3, Rachel Springer4
1Oregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA. booman@ohsu.edu.
BMC pregnancy and childbirth
|January 17, 2025
概括
孕期体重增加 (GWG) 指标在妊娠前的BMI类别之间有很大差异,特别是在服务不足的人群中. 需要对季度特定的GWG进行更多研究,特别是肥胖的第一季度减肥.
科学领域:
- 围产期研究 围产期研究
- 孕产妇健康 孕产妇健康
- 肥胖药物 肥胖药物 肥胖药物
背景情况:
- 怀孕期体重增加 (GWG) 的量化是至关重要的,但在临床环境中是不发达的,特别是对于服务不足的群体.
- 现有的GWG指标可能不能准确地反映多样化的人口.
- 评估了六个不同的GWG指标的实用性.
研究的目的:
- 为了比较六种不同的妊娠期体重增加 (GWG) 度量.
- 分析妊娠前BMI类别中的GWG变化.
- 为各种人群制定精细的GWG指导方针和指标提供信息.
主要方法:
- 利用了44801名孕妇的临床数据.
- 包括广泛的纵向测量和多样化的人口代表.
- 对比了六个GWG指标:总GWG,线性速率,依从性,AUC和z分数在BMI分类中.
主要成果:
- 较高的妊娠前BMI与较低的总GWG相关,但三个月特定的模式不同.
- 在肥胖类中观察到第一季度的体重减轻,在体重不足的个体中增加.
- 与BMI相关的最大GWG变化发生在第二个三个月,反映在AUC和z-score指标中.
结论:
- 在BMI类别中,GWG指标有所不同,影响着有色人种女性和体型较大或收入较低的女性等服务不足的子组.
- 研究结果支持制定BMI类别特定的GWG指导方针,特别是针对肥胖子类和体重不足.
- 进一步的研究对于安全的第一季度减肥水平和肥胖类别特定的建议至关重要.
相关概念视频
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
01:30Methods of Documentation VII: EMR
824
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
824
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
26
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
26