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剖宫产的决定因素:一项分类树分析

Determinants of cesarean delivery: a classification tree analysis.

作者信息

Stivanello Elisa, Rucci Paola, Lenzi Jacopo, Fantini Maria Pia

机构信息

Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum - University of Bologna, Via San Giacomo 12, 40126 Bologna, Italy.

出版信息

BMC Pregnancy Childbirth. 2014 Jun 28;14:215. doi: 10.1186/1471-2393-14-215.

Abstract

BACKGROUND

Cesarean delivery (CD) rates are rising in many parts of the world. To define strategies to reduce them, it is important to identify their clinical and organizational determinants. The objective of this cross-sectional study is to identify sub-types of women at higher risk of CD using demographic, clinical and organizational variables.

METHODS

All hospital discharge records of women who delivered between 2005 and mid-2010 in the Emilia-Romagna Region of Italy were retrieved and linked with birth certificates. Sociodemographic and clinical information was retrieved from the two data sources. Organizational variables included activity volume (number of births per year), hospital type, and hour and day of delivery. A classification tree analysis was used to identify the variables and the combinations of variables that best discriminated cesarean from vaginal delivery.

RESULTS

The classification tree analysis indicated that the most important variables discriminating the sub-groups of women at different risk of cesarean section were: previous cesarean, mal-position/mal-presentation, fetal distress, and abruptio placentae or placenta previa or ante-partum hemorrhage. These variables account for more than 60% of all cesarean deliveries. A sensitivity analysis identified multiparity and fetal weight as additional discriminatory variables.

CONCLUSIONS

Clinical variables are important predictors of CD. To reduce the CD rate, audit activities should examine in more detail the clinical conditions for which the need of CD is questionable or inappropriate.

摘要

背景

剖宫产率在世界许多地区都在上升。为了确定降低剖宫产率的策略,识别其临床和组织决定因素很重要。这项横断面研究的目的是使用人口统计学、临床和组织变量来识别剖宫产风险较高的女性亚组。

方法

检索了2005年至2010年年中在意大利艾米利亚-罗马涅地区分娩的所有女性的医院出院记录,并与出生证明相关联。社会人口统计学和临床信息从这两个数据源中获取。组织变量包括活动量(每年分娩数)、医院类型以及分娩时间和日期。使用分类树分析来识别最能区分剖宫产和阴道分娩的变量以及变量组合。

结果

分类树分析表明,区分剖宫产不同风险女性亚组的最重要变量是:既往剖宫产史、胎位异常/胎先露异常、胎儿窘迫以及胎盘早剥或前置胎盘或产前出血。这些变量占所有剖宫产分娩的60%以上。敏感性分析确定多胎妊娠和胎儿体重为额外的鉴别变量。

结论

临床变量是剖宫产的重要预测因素。为了降低剖宫产率,审核活动应更详细地检查剖宫产需求存疑或不适当的临床情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25c0/4090181/78f96de15ae2/1471-2393-14-215-1.jpg

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