Use of routinely collected data to classify planned mode of delivery among pregnancies with a previous cesarean delivery: a validation study.

IF 4.4 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Epidemiology Pub Date : 2025-09-15 DOI:10.1097/EDE.0000000000001915

Mary M Brown, Ya-Hui Yu, Jennifer A Hutcheon, Christy G Woolcott, Victoria M Allen, John Fahey, Irene Gagnon, Azar Mehrabadi

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Abstract

Background: Counseling on the harms and benefits of a planned vaginal versus a planned repeat cesarean delivery often relies on observational studies using routinely collected (or administrative) data. However, the accuracy of planned (rather than actual) mode of delivery classifications in such data remains unknown. This study aimed to evaluate the validity of an administrative data-based algorithm to identify planned vaginal and planned cesarean deliveries among individuals with a previous cesarean.

Methods: An algorithm based on diagnostic and procedural codes was applied to records from the Nova Scotia Atlee Perinatal Database. Included were individuals with a previous cesarean eligible for a trial of labor between 2017 and 2019. We compared the classification of planned mode of delivery using the algorithm with that determined through review of a random sample of 200 medical charts. We estimated sensitivity, specificity, and predictive values with 95% confidence intervals (CI).

Results: Based on the chart review, 80 deliveries (40%) were planned vaginal deliveries. The algorithm had an estimated sensitivity of 99% (95% CI 93, 100%), specificity of 96% (95% CI 91, 99%), positive predictive value of 94% (95% CI 87, 98%), and negative predictive value of 99% (95% CI 95, 100%) for identifying planned vaginal deliveries.

Conclusions: An algorithm based on routinely collected data accurately classified planned vaginal and planned cesarean deliveries among individuals with a previous cesarean. These findings suggest that studies using similar algorithms to inform counseling on planned mode of delivery in this population are minimally impacted by misclassification of this data.

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使用常规收集的数据对既往剖宫产妊娠的计划分娩方式进行分类：一项验证研究。

背景：关于计划阴道分娩与计划重复剖宫产的利弊的咨询通常依赖于使用常规收集（或管理）数据的观察性研究。然而，这些数据中计划的（而不是实际的）交付方式分类的准确性仍然未知。本研究旨在评估一种基于管理数据的算法的有效性，该算法可在既往剖宫产的个体中识别计划阴道分娩和计划剖宫产。方法：应用基于诊断和程序代码的算法对新斯科舍省阿特利围产期数据库的记录进行分析。其中包括在2017年至2019年期间有资格进行剖宫产试验的患者。我们比较了使用该算法的计划分娩方式分类与通过审查200个医疗图表的随机样本确定的分类。我们以95%置信区间（CI）估计敏感性、特异性和预测值。结果：根据图表回顾，80例分娩（40%）计划阴道分娩。该算法在确定计划阴道分娩方面的估计灵敏度为99% (95% CI 93, 100%)，特异性为96% (95% CI 91, 99%)，阳性预测值为94% (95% CI 87, 98%)，阴性预测值为99% （95% CI 95, 100%）。结论：一种基于常规收集数据的算法可以准确地对有剖宫产史的患者进行阴道计划分娩和剖宫产计划分娩的分类。这些发现表明，在这一人群中使用类似算法为计划分娩方式提供咨询的研究受到数据错误分类的影响最小。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Epidemiology 医学-公共卫生、环境卫生与职业卫生

CiteScore

6.70

自引率

3.70%

发文量

177

审稿时长

6-12 weeks

期刊介绍： Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.