利用超声波估算第二和第三孕期胎龄模型的外部验证:前瞻性多中心观察研究。

IF 5.4 3区 材料科学 Q2 CHEMISTRY, PHYSICAL
Alice Self, Michael Schlussel, Gary S. Collins, Ferdinand Dhombres, Nicolas Fries, Georges Haddad, Laurent J. Salomon, Mona Massoud, Aris T. Papageorghiou
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引用次数: 0

摘要

目的:准确评估孕龄(GA)对个人和人群都很重要。估算孕晚期妇女孕龄的最准确方法尚不清楚。本研究旨在从外部验证孕晚期孕龄估算公式的准确性,并确定最佳公式,以估算在妊娠第二或第三个月才接受超声波扫描的妇女的孕龄:设计:这是一项前瞻性、观察性横断面研究:地点:法国 57 家产前护理中心:在为期 8 周的时间里,招募了单胎妊娠且之前在 11-14 周的孕期扫描中观察到胎儿性别的妇女。她们在孕期的一个时间点(15-43周)接受了标准化超声波检查,测量了12个胎儿生物测量参数,这些参数之前已被确认为有助于估计胎儿性别:主要结果测量:共研究了 189 个基于胎儿生物测量的 GA 估算方程,并与基于胎儿 CRL 的 GA 估算进行了比较。使用 R2、校准斜率和截距对观察到的 GA 与估计的 GA 进行比较。还计算了 RMSE、平均差和 95% 的误差范围:共有 2741 名孕妇接受了检查。排除后,共纳入 2339 名参与者。在 20 个表现最好的方程中,截距范围从-0.22 到 0.30,校准斜率从 0.96 到 1.03,RSME 从 0.67 到 0.87。总体而言,多参数模型优于单参数模型。95% 误差范围和平均差都随着妊娠期的增加而增加。仅以测量头围为基础的常用模型并不是表现最好的模型,其 95% 误差和平均差也较高:我们提供了强有力的证据,证明基于多参数模型的GA特异性方程应用于估计孕晚期的GA。然而,由于妊娠晚期的所有 GA 评估方法都与较大的预测间期有关,因此,改善早期产前超声检查的可及性仍是当务之急:本研究的提案及相应的方法学综述已在 PROSPERO 国际系统综述注册中心注册(注册号:CRD4201913776)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

External validation of models to estimate gestational age in the second and third trimester using ultrasound: A prospective multicentre observational study

External validation of models to estimate gestational age in the second and third trimester using ultrasound: A prospective multicentre observational study

Objectives

Accurate assessment of gestational age (GA) is important at both individual and population levels. The most accurate way to estimate GA in women who book late in pregnancy is unknown. The aim of this study was to externally validate the accuracy of equations for GA estimation in late pregnancy and to identify the best equation for estimating GA in women who do not receive an ultrasound scan until the second or third trimester.

Design

This was a prospective, observational cross-sectional study.

Setting

57 prenatal care centres, France.

Participants

Women with a singleton pregnancy and a previous 11–14-week dating scan that gave the observed GA were recruited over an 8-week period. They underwent a standardised ultrasound examination at one time point during the pregnancy (15–43 weeks), measuring 12 foetal biometric parameters that have previously been identified as useful for GA estimation.

Main Outcome Measures

A total of 189 equations that estimate GA based on foetal biometry were examined and compared with GA estimation based on foetal CRL. Comparisons between the observed GA and the estimated GA were made using R2, calibration slope and intercept. RMSE, mean difference and 95% range of error were also calculated.

Results

A total of 2741 pregnant women were examined. After exclusions, 2339 participants were included. In the 20 best performing equations, the intercept ranged from −0.22 to 0.30, the calibration slope from 0.96 to 1.03 and the RSME from 0.67 to 0.87. Overall, multiparameter models outperformed single-parameter models. Both the 95% range of error and mean difference increased with gestation. Commonly used models based on measurement of the head circumference alone were not amongst the best performing models and were associated with higher 95% error and mean difference.

Conclusions

We provide strong evidence that GA-specific equations based on multiparameter models should be used to estimate GA in late pregnancy. However, as all methods of GA assessment in late pregnancy are associated with large prediction intervals, efforts to improve access to early antenatal ultrasound must remain a priority.

Trial Registration

The proposal for this study and the corresponding methodological review was registered on PROSPERO international register of systematic reviews (registration number: CRD4201913776).

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来源期刊
ACS Applied Energy Materials
ACS Applied Energy Materials Materials Science-Materials Chemistry
CiteScore
10.30
自引率
6.20%
发文量
1368
期刊介绍: ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.
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