澳大利亚妊娠35周死产风险预测模型的开发和验证方案。

Jessica K Sexton, Michael Coory, Sailesh Kumar, Gordon Smith, Adrienne Gordon, Georgina Chambers, Gavin Pereira, Camille Raynes-Greenow, Lisa Hilder, Philippa Middleton, Anneka Bowman, Scott N Lieske, Kara Warrilow, Jonathan Morris, David Ellwood, Vicki Flenady
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引用次数: 5

摘要

背景:尽管过去一个世纪在妇女及其婴儿护理方面取得了进展,但全世界每年仍有大约170万婴儿出生。在资源丰富的澳大利亚,需要一种可靠的方法来估计孕妇妊娠晚期死产的个体化风险,从而为分娩时间的决策提供信息,以降低妊娠35周后死产的风险。方法:这是一项横断面研究方案,研究对象为澳大利亚(2005-2015)妊娠35周后的所有晚期妊娠分娩,包括310万例分娩中的5188例死产,估计每1000例分娩中有1.7例死产。将根据目前的透明报告个体预后或诊断多变量预测模型(TRIPOD)指南开发多变量逻辑回归模型,以估计具有预测间隔的妊娠特异性死产概率。候选预测因子是从系统评价和临床咨询中确定的,并将通过单变量回归分析进行描述。为了产生最终的模型,将进行反向逐步多变量逻辑回归的消除。该模型将使用1000次重复的bootstrapping进行内部验证,并使用临时唯一的数据集进行外部验证。总体模型性能将通过R2、校准和判别来评估。将使用具有95%置信区间(α = 0.05)的校准图报告校准。判别将通过c统计量和接收器-操作员曲线下的面积来测量。临床有用性将报告为阳性和阴性预测值,并将考虑决策曲线分析。讨论:需要一种可靠的方法来预测孕妇妊娠晚期死产的个体化风险,以便及时提供适当的护理以减少死产。在为产科使用设计的现有预测模型中,很少有模型经过内部和外部验证,许多模型未能达到建议的报告标准。在开发妊娠晚期死产的风险预测模型时,考虑到提供者和孕妇,我们努力开发一个经过验证的模型,以供澳大利亚临床使用,符合当前的报告标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia.

Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia.

Background: Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman's individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting.

Methods: This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005-2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered.

Discussion: A robust method to predict a pregnant woman's individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards.

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