Read Stephanie H, Rosella Laura C, Berger Howard, Feig Denice S, Fleming Karen, Kaul Padma, Ray Joel G, Shah Baiju R, Lipscombe Lorraine L
Women's College Research Institute, Women's College Hospital, 76 Grenville Street, Toronto, Ontario, M5S 1B2, Canada.
Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.
Diagn Progn Res. 2021 Mar 8;5(1):5. doi: 10.1186/s41512-021-00095-6.
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.
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 R), 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.
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.
妊娠为识别未来患2型糖尿病（DM）风险较高的女性提供了独特机会。在孕期，女性与医疗保健系统的接触更为密切，某些情况更易于显现，如妊娠期糖尿病（GDM），其是未来患糖尿病风险的重要标志物。本研究方案描述了一种风险预测模型（RPM）的开发与验证，该模型用于估计女性产后发生2型糖尿病的5年风险。
数据将从安大略省现有的基于人群的行政数据集中获取。推导队列将包括2006年4月至2014年3月在加拿大安大略省分娩的所有女性。预先指定的预测因素将包括社会人口学因素（分娩时年龄、种族）、孕产妇临床因素（如体重指数）、妊娠相关事件（妊娠期糖尿病、妊娠高血压疾病）和新生儿因素（出生体重百分位数）。通过与安大略省糖尿病数据库建立联系来识别2型糖尿病发病情况。将开发威布尔加速失效时间模型以预测2型糖尿病的5年风险。将生成预测准确性指标（Nagelkerke's R）、区分度指标（C统计量）和校准图。将采用有放回抽样的自举法在500个样本中进行内部验证，并计算校正乐观偏倚后的C统计量。将该RPM应用于艾伯塔省一个基于人群的大型妊娠队列，估计上述模型性能指标，从而进行外部验证。将在适当情况下通过调整基线风险和系数对模型进行重新校准。
推导得出的RPM可能有助于识别产后5年内患糖尿病风险较高的女性，从而促进高风险女性改变生活方式，并在产后更频繁地筛查2型糖尿病。
