阴道分娩后的妊娠间隔和单子早产之间的非线性关联:一项回顾性队列研究
Tingting Zhuang1, Yu Zhang2, Xueli Ren3
1Postgraduate Training Base of Jinzhou Medical University (General Hospital of Northern Theater Command), No.83, Wenhua Road, Shenhe District, Shenyang, 110016, China.
BMC pregnancy and childbirth
|March 12, 2025
概括
阴道分娩后11个月或更短的短期间隔 (IPI) 和24个月或更长的长时间间隔 (IPI) 与早产 (PTB) 的风险更高有关. 降低PTB风险的最佳IPI约为23个月.
科学领域:
- 生殖健康 生殖健康
- 孕产妇和胎儿医学 孕产妇和胎儿医学
- 产科 产科 产科 产科 产科
背景情况:
- 阴道分娩后的妊娠间隔 (IPI) 和随后的单独早产 (PTB) 之间的关系仍未得到充分研究.
- 了解这种关联对于优化母亲和婴儿的结果至关重要.
研究的目的:
- 调查阴道分娩后的妊娠间隔 (IPI) 与单子妊娠中早产风险 (PTB) 之间的关联.
- 确定最佳的妊娠间隔,以减少PTB风险.
主要方法:
- 利用了2022年国家生命统计系统 (NVSS) 的出生数据.
- 采用多项逻辑回归和限制立方线 (RCS) 模型来分析IPI和PTB之间的关联.
- 进行了值效应分析,使用两部分线性回归来确定非线性关系.
主要成果:
- 对1,517,106名受试者的分析显示,阴道分娩后的IPI与PTB风险之间存在J形关联.
- 与18-23个月参考组相比,≤11个月和≥24个月的妊娠间隔与PTB的风险增加有显著的关联.
- 在约23个月的妊娠间隔中观察到PTB的最低风险.
结论:
- 在阴道分娩后的妊娠间隔 (IPI) 和早产 (PTB) 风险之间存在一个J形的非线性关系.
- 短 (≤11个月) 和长 (≥24个月) 的妊娠间隔与增加PTB风险有关.
- 大约23个月的妊娠间隔似乎是最小化阴道分娩后PTB风险的最佳选择.
相关概念视频
Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
Correlation
11.5K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.5K
Confounding in Epidemiological Studies
123
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
123
Teratogenicity
2.3K
The ability of a drug to produce structural deformations and functional abnormalities in the developing embryo or the fetus is called teratogenicity, and the drug producing this effect is known as a teratogen. Teratogenic effects include stillbirth, miscarriage, intrauterine growth restriction, and neurocognitive delay. A teratogen may affect the embryo at different stages of development, which is important in determining the type and extent of the damage. During blastocyst formation, the early...
2.3K
Correlation and Regression
1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.2K
Bias in Epidemiological Studies
120
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
120


