Cooray Shamil D, Boyle Jacqueline A, Soldatos Georgia, Allotey John, Wang Holly, Fernandez-Felix Borja M, Zamora Javier, Thangaratinam Shakila, Teede Helena J
Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton VIC 3168, Australia.
Diabetes and Endocrinology Units, Monash Health, Clayton VIC 3168, Australia.
EClinicalMedicine. 2022 Sep 5;52:101637. doi: 10.1016/j.eclinm.2022.101637. eCollection 2022 Oct.
The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM.
A prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis.
The final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome.
The PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria.
This work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847.
能够计算出患有妊娠期糖尿病(GDM)的个体女性发生不良妊娠结局的绝对风险,将有助于针对高危女性进行预防和治疗干预,使低风险女性免受不必要的医疗护理。我们旨在开发、验证并评估一种用于预测GDM女性不良妊娠结局的模型的临床实用性。
基于一项观察性队列研究的数据进行预测模型的开发和验证研究。参与者包括来自澳大利亚墨尔本三家大都市三级教学医院的所有GDM女性。开发队列包括2017年7月1日至2018年6月30日期间分娩的女性,验证队列包括2018年7月1日至2018年12月31日期间分娩的女性。主要结局是一组极为重要的孕产妇和围产期并发症(妊娠高血压疾病、大于胎龄儿、需要静脉治疗的新生儿低血糖、肩难产、围产期死亡、新生儿骨折和神经麻痹)。通过区分度和校准来衡量模型性能,并使用决策曲线分析评估临床实用性。
最终的PeRSonal(GDM女性风险分层护理预测)模型包括体重指数、产妇年龄、空腹和1小时血糖值(75克口服葡萄糖耐量试验)、GDM诊断时的孕周、南亚和中亚族裔、东亚族裔、初产、既往分娩大于胎龄儿、既往子痫前期、GDM诊断前的孕期体重增加以及糖尿病家族史。在开发队列(1747名女性)中,27%(476/1747)的女性发生了复合不良妊娠结局,在验证队列(955名女性)中这一比例为26%(244/955)。经时间验证,该模型显示出良好的校准,斜率为0.99(95%CI 0.75至1.23),且具有可接受的区分度(统计量0.68;95%CI 0.64至0.72)。决策曲线分析表明,与将所有GDM女性都视为会或不会发生不良妊娠结局的替代方案相比,该模型在预测不良妊娠结局的概率阈值介于0.15至0.85之间时具有实用性。
由常规可用临床数据组成的PeRSonal GDM模型表现出色,具有时间可转移性,并且在一系列预测概率范围内具有临床实用性。现在需要对该模型在更不同的人群中进行进一步的外部验证,以评估其在不同中心、社区护理和低资源环境、其他医疗系统以及不同GDM诊断标准下的通用性。
本研究得到澳大利亚母亲与妊娠期糖尿病2(Mothers and Gestational Diabetes in Australia 2)项目的支持,该项目由澳大利亚国家健康与医学研究委员会(NHMRC)资助,项目编号#1170847。