Dong Wei, Xie Shasha
Department of Obstetrics and Gynecology, Xingtai People's Hospital Xingtai, Hebei Province, China.
Am J Transl Res. 2021 Apr 15;13(4):3238-3245. eCollection 2021.
To systematically explore the risk factors that influence cesarean section rate, and establish a prediction model to investigate a system effectively reducing cesarean section rates.
This retrospective study was carried out in the medical institutions in Xingtai city, where cesarean section could be conducted. The data of parturients who gave birth to children in the past five years were collected using the hospital information system. Based on the Robson's ten group classification system, parturients were grouped. The difference of cesarean section rate in each group and its main influencing factors were then analyzed. The above factors and factors such as age, education background, and knowledge on childbirth were independent variables, while cesarean section was the dependent variable. A logistic regression model was constructed to determine the correlation between relevant influencing factors and cesarean section.
In the past 5 years, cesarean section rate in Xingtai city had been maintained at a relatively high level. Cesarean section rates in the R2 and R5 groups were the highest. Parity, fetal position, number of fetuses, and gestational weeks were all factors affecting cesarean section rate (all P < 0.01). After screening the above factors using logistic regression analysis, a regression equation was established: logistic (p) = -1.061 + 1.107 * parity + 0.196 * fetal position + 2.245 * number of fetuses - 0.070 * gestational week + 0.234 * age - 0.278 * education background + 0.623 * knowledge on childbirth.
The Robson classification system plays an important role in evaluating and supervising parturients' conditions. Based on the Robson classification system, we find that parity, fetal position, number of fetuses, and gestational weeks are the main factors influencing cesarean section rate. Using logistic regression analysis, a prediction model, with guiding significance on the control of cesarean section rate, is established.
系统探讨影响剖宫产率的危险因素，建立预测模型，以研究有效降低剖宫产率的体系。
本回顾性研究在邢台市可进行剖宫产的医疗机构开展。利用医院信息系统收集过去五年分娩产妇的数据。基于罗布森十组分类系统对产妇进行分组。然后分析每组剖宫产率的差异及其主要影响因素。将上述因素以及年龄、教育背景、分娩知识等因素作为自变量，剖宫产作为因变量。构建逻辑回归模型以确定相关影响因素与剖宫产之间的相关性。
过去5年，邢台市剖宫产率一直维持在较高水平。R2组和R5组的剖宫产率最高。产次、胎位、胎儿数量和孕周均为影响剖宫产率的因素（均P<0.01）。经逻辑回归分析筛选上述因素后，建立回归方程：logistic(p)= -1.061 + 1.107×产次 + 0.196×胎位 + 2.245×胎儿数量 - 0.070×孕周 + 0.234×年龄 - 0.278×教育背景 + 0.623×分娩知识。
罗布森分类系统在评估和监测产妇情况方面发挥着重要作用。基于罗布森分类系统，我们发现产次、胎位、胎儿数量和孕周是影响剖宫产率的主要因素。通过逻辑回归分析，建立了对控制剖宫产率具有指导意义的预测模型。
