Diabetes after pregnancy: a study protocol for the derivation and validation of a risk prediction model for 5-year risk of diabetes following pregnancy.

Stephanie H Read, Laura C Rosella, Howard Berger, Denice S Feig, Karen Fleming, Padma Kaul, Joel G Ray, Baiju R Shah, Lorraine L Lipscombe
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Abstract

Background: Pregnancy offers a unique opportunity to identify women at higher future risk of type 2 diabetes mellitus (DM). In pregnancy, a woman has greater engagement with the healthcare system, and certain conditions are more apt to manifest, such as gestational DM (GDM) that are important markers for future DM risk. This study protocol describes the development and validation of a risk prediction model (RPM) for estimating a woman's 5-year risk of developing type 2 DM after pregnancy.

Methods: Data will be obtained from existing Ontario population-based administrative datasets. The derivation cohort will consist of all women who gave birth in Ontario, Canada between April 2006 and March 2014. Pre-specified predictors will include socio-demographic factors (age at delivery, ethnicity), maternal clinical factors (e.g., body mass index), pregnancy-related events (gestational DM, hypertensive disorders of pregnancy), and newborn factors (birthweight percentile). Incident type 2 DM will be identified by linkage to the Ontario Diabetes Database. Weibull accelerated failure time models will be developed to predict 5-year risk of type 2 DM. Measures of predictive accuracy (Nagelkerke's R2), discrimination (C-statistics), and calibration plots will be generated. Internal validation will be conducted using a bootstrapping approach in 500 samples with replacement, and an optimism-corrected C-statistic will be calculated. External validation of the RPM will be conducted by applying the model in a large population-based pregnancy cohort in Alberta, and estimating the above measures of model performance. The model will be re-calibrated by adjusting baseline hazards and coefficients where appropriate.

Discussion: The derived RPM may help identify women at high risk of developing DM in a 5-year period after pregnancy, thus facilitate lifestyle changes for women at higher risk, as well as more frequent screening for type 2 DM after pregnancy.

妊娠后糖尿病:推导和验证妊娠后 5 年糖尿病风险预测模型的研究方案。
背景:妊娠为识别未来罹患 2 型糖尿病(DM)风险较高的妇女提供了一个独特的机会。在怀孕期间,妇女与医疗保健系统的接触更多,某些情况更容易显现,如妊娠糖尿病(GDM),这是未来糖尿病风险的重要标志。本研究方案介绍了风险预测模型(RPM)的开发和验证,该模型用于估算妇女怀孕后5年罹患2型糖尿病的风险:方法:将从安大略省现有的基于人口的行政数据集中获取数据。推导队列将包括 2006 年 4 月至 2014 年 3 月期间在加拿大安大略省分娩的所有妇女。预先确定的预测因素包括社会人口因素(分娩年龄、种族)、产妇临床因素(如体重指数)、妊娠相关事件(妊娠糖尿病、妊娠高血压疾病)和新生儿因素(出生体重百分位数)。将通过与安大略省糖尿病数据库的连接来确定 2 型糖尿病的发病情况。将建立 Weibull 加速失败时间模型来预测 2 型糖尿病的 5 年风险。将生成预测准确性(Nagelkerke's R2)、区分度(C 统计量)和校准图。将在 500 个样本中使用自举法进行内部验证,并计算乐观校正 C 统计量。RPM 的外部验证将通过在艾伯塔省基于人口的大型妊娠队列中应用该模型来进行,并估算上述模型性能指标。在适当的情况下,将通过调整基线危险度和系数对模型进行重新校准:讨论:推导出的 RPM 可能有助于识别妊娠后 5 年内罹患糖尿病的高风险妇女,从而促进高风险妇女改变生活方式,并在妊娠后更频繁地筛查 2 型糖尿病。
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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