Intrapartum Cesarean Delivery in Nulliparas

Risk Factors Compared by Two Analytical Approaches

MA Kominiarek; P VanVeldhuisen; K Gregory; M Fridman; H Kim; JU Hibbard

Disclosures

J Perinatol. 2015;35(3):167-172. 

In This Article

Abstract and Introduction

Abstract

Objective To determine risk factors for cesarean delivery in nulliparas at labor admission.

Study Design Nulliparas with live-born, singleton gestations ≥37 weeks in spontaneous or induced labor were analyzed from the Consortium on Safe Labor database in a retrospective observational study. Classification and regression tree (CART) and multivariate logistic regression analysis determined risk factors for cesarean delivery.

Result Of the 66 539 nulliparas, 22% had a cesarean delivery. In the CART analysis, the first cervical dilation exam was the first branch followed by body mass index (BMI). Cesarean deliveries occurred in 45%, 25%, 14% and 10% of deliveries at <1 , 1 to 3, 4 and ≥5 cm dilated, respectively. The BMI influence was most evident in the <1 cm dilation category with 26% of BMI <25 kg m−2 and 66% of BMI ≥40 kg m−2 having a cesarean delivery. The fewest cesarean deliveries (5%) occurred in those ≥5 cm and BMI <25 kg m−2. In the multivariate regression analysis, first cervical dilation exam <1 cm (odds ratio (OR) 5.1, 95% confidence interval (CI): 4.5 to 5.7; reference ≥5 cm) and BMI ≥40 kg m−2 (OR 5.1, 95% CI: 4.6 to 5.7; reference BMI <25.0 kg m−2) had the highest odds for cesarean delivery.

Conclusion Cervical dilation on admission followed by BMI were the two most important risk factors for cesarean delivery identified in both CART and multivariate regression analysis.

Introduction

A critical issue in obstetrics is the rising cesarean delivery rate, which reached a high at 32.9% in 2009 in the United States.[1] Nulliparas comprise ~22 to 26% of all cesarean deliveries.[1,2] In addition to the known short-term complications such as infectious morbidity and thromboembolic events, cesarean deliveries are associated with long-term complications such as abnormal placentation and hysterectomy.[3,4] The cesarean performed in a nullipara is a seminal event as it impacts future delivery route decisions and potentially contributes to maternal morbidity.

The ability to determine which women are at higher risk for cesarean delivery is important in obstetrical care as this may modify management protocols and lead to improved perinatal outcomes. This is especially true for nulliparas as labor course and outcome is more difficult to determine compared with multiparas. Logistic regression analyses are among the most common methods used to describe associations between risk factors and outcomes (e.g., trial of labor after cesarean delivery and delivery route), but they have limited ability to predict outcomes.[5,6,7,8,9] Classification and regression tree (CART) analysis not only accounts for complex relationships between variables but also presents the results in a clinically useful form. In this approach, progressive splitting of the study population into subgroups occurs according to the independent variables. The underlying mathematical algorithm chooses the variables to split by, the discriminatory values of the variables and the order in which splitting occurs, with the objective of maximizing outcome discrimination at each step. The process develops a hierarchical tree structure that allows for the visualization of complex interactions when multiple variables are analyzed simultaneously. CART analysis is a different approach to studying risk factors and outcomes and a few investigations in obstetrics (e.g., outcomes after emergent cerclage, perineal lacerations, postpartum hemorrhage and primary cesarean delivery in a non-laboring population) have used this methodology.[8,10–12] The objective of this study was to identify factors present upon labor admission that are associated with cesarean delivery in nulliparas from the Consortium on Safe Labor database using CART analysis and then compare these findings with multivariate logistic regression analysis.

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