Suppr超能文献

胎儿适合分娩吗?引入快速节俭树(FFTrees)以简化对孕妇进行STAN监测的分诊:与传统分类的观察者间一致性比较。

Is the fetus fit for labor? Introducing fast-and-frugal trees (FFTrees) to simplify triage of women for STAN monitoring: An interobserver agreement comparison with traditional classification.

作者信息

Pereira Susana, Bakker Petra, Zaima Ahmed, Ghi Tullio, Kessler Jörg, Timonen Susanna, Vayssière Christoph, Löser Katrin, Holmberg Kaisa, Jacquemyn Yves, Chandraharan Edwin, Wertheim David, Olofsson Per

机构信息

Fetal Medicine Unit, The Royal London Hospital, Barts Health NHS Trust, London, UK.

Department of Obstetrics and Gynecology, Amsterdam UMC, Amsterdam, The Netherlands.

出版信息

Acta Obstet Gynecol Scand. 2024 Jan;103(1):68-76. doi: 10.1111/aogs.14680. Epub 2023 Oct 27.

Abstract

INTRODUCTION

It is a shortcoming of traditional cardiotocography (CTG) classification table formats that CTG traces are frequently classified differently by different users, resulting in poor interobserver agreements. A fast-and-frugal tree (FFTree) flow chart may help provide better concordance because it is straightforward and has clearly structured binary questions with understandable "yes" or "no" responses. The initial triage to determine whether a fetus is suitable for labor when utilizing fetal ECG ST analysis (STAN) is very important, since a fetus with restricted capacity to respond to hypoxic stress may not generate STAN events and therefore may become falsely negative. This study aimed to compare physiology-focused FFTree CTG interpretation with FIGO classification for assessing the suitability for STAN monitoring.

MATERIAL AND METHODS

A retrospective study of 36 CTG traces with a high proportion of adverse outcomes (17/36) selected from a European multicenter study database. Eight experienced European obstetricians evaluated the initial 40 minutes of the CTG recordings and judged whether STAN was a suitable fetal surveillance method and whether intervention was indicated. The experts rated the CTGs using the FFTree and FIGO classifications at least 6 weeks apart. Interobserver agreements were calculated using proportions of agreement and Fleiss' kappa (κ).

RESULTS

The proportions of agreement for "not suitable for STAN" were for FIGO 47% (95% confidence interval [CI] 42%-52%) and for FFTree 60% (95% CI 56-64), ie a significant difference; the corresponding figures for "yes, suitable" were 74% (95% CI 71-77) and 70% (95% CI 67-74). For "intervention needed" the figures were 52% (95% CI 47-56) vs 58% (95% CI 54-62) and for "expectant management" 74% (95% CI 71-77) vs 72% (95% CI 69-75). Fleiss' κ agreement on "suitability for STAN" was 0.50 (95% CI 0.44-0.56) for the FIGO classification and 0.57 (95% CI 0.51-0.63) for the FFTree classification; the corresponding figures for "intervention or expectancy" were 0.53 (95% CI 0.47-0.59) and 0.57 (95% CI 0.51-0.63).

CONCLUSIONS

The proportion of agreement among expert obstetricians using the FFTree physiological approach was significantly higher compared with the traditional FIGO classification system in rejecting cases not suitable for STAN monitoring. That might be of importance to avoid false negative STAN recordings. Other agreement figures were similar. It remains to be shown whether the FFTree simplicity will benefit less experienced users and how it will work in real-world clinical scenarios.

摘要

引言

传统的胎心监护(CTG)分类表格式存在一个缺点,即不同用户对CTG图形的分类常常不同,导致观察者间的一致性较差。快速节俭树(FFTree)流程图可能有助于提供更好的一致性,因为它简单明了,具有结构清晰的二元问题,答案为易懂的“是”或“否”。在利用胎儿心电图ST段分析(STAN)来确定胎儿是否适合分娩时,初始分诊非常重要,因为对缺氧应激反应能力受限的胎儿可能不会产生STAN事件,因此可能出现假阴性。本研究旨在比较以生理为重点的FFTree CTG解读与国际妇产科联盟(FIGO)分类在评估STAN监测适用性方面的差异。

材料与方法

一项回顾性研究,从欧洲多中心研究数据库中选取了36份不良结局比例较高(17/36)的CTG图形。八位经验丰富的欧洲产科医生评估了CTG记录的最初40分钟,并判断STAN是否是一种合适的胎儿监测方法以及是否需要干预。专家们至少间隔6周使用FFTree和FIGO分类对CTG进行评分。使用一致率和Fleiss卡方(κ)计算观察者间的一致性。

结果

对于“不适合STAN”,FIGO分类的一致率为47%(95%置信区间[CI] 42%-52%),FFTree分类的一致率为60%(95% CI 56-64),即存在显著差异;对于“是,适合”,相应数字分别为74%(95% CI 7

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa88/10755137/d1f2f379a37f/AOGS-103-68-g002.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验