Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Diabetes and Vascular Medicine Unit, Monash Health, Melbourne, Australia.
Syst Rev. 2019 Nov 11;8(1):270. doi: 10.1186/s13643-019-1151-0.
Gestational diabetes (GDM) is increasingly common and has significant implications during pregnancy and for the long-term health of the mother and offspring. However, it is a heterogeneous condition with inter-related factors including ethnicity, body mass index and gestational weight gain significantly modifying the absolute risk of complications at an individual level. Predicting the risk of pregnancy complications for an individual woman with GDM presents a useful adjunct to therapeutic decision-making and patient education. Diagnostic prediction models for GDM are prevalent. In contrast, prediction models for risk of complications in those with GDM are relatively novel. This study will systematically review published prognostic prediction models for pregnancy complications in women with GDM, describe their characteristics, compare performance and assess methodological quality and applicability.
Studies will be identified by searching MEDLINE and Embase electronic databases. Title and abstract screening, full-text review and data extraction will be completed independently by two reviewers. The included studies will be systematically assessed for risk of bias and applicability using appropriate tools designed for prediction modelling studies. Extracted data will be tabulated to facilitate qualitative comparison of published prediction models. Quantitative data on predictive performance of these models will be synthesised with meta-analyses if appropriate.
This review will identify and summarise all published prognostic prediction models for pregnancy complications in women with GDM. We will compare model performance across different settings and populations with meta-analysis if appropriate. This work will guide subsequent phases in the prognosis research framework: further model development, external validation and model updating, and impact assessment. The ultimate model will estimate the absolute risk of pregnancy complications for women with GDM and will be implemented into routine care as an evidence-based GDM complication risk prediction model. It is anticipated to offer value to women and their clinicians with individualised risk assessment and may assist decision-making. Ultimately, this systematic review is an important step towards a personalised risk-stratified model-of-care for GDM to allow preventative and therapeutic interventions for the maximal benefit to women and their offspring, whilst sparing expense and harm for those at low risk.
PROSPERO registration number CRD42019115223.
妊娠期糖尿病（GDM）越来越常见，对孕妇和母婴长期健康有重大影响。然而，它是一种具有异质性的疾病，相关因素包括种族、体重指数和妊娠期体重增加，这些因素显著改变了个体水平并发症的绝对风险。预测患有 GDM 的个体妇女的妊娠并发症风险是治疗决策和患者教育的有用辅助手段。用于 GDM 的诊断预测模型很普遍。相比之下，用于预测 GDM 患者并发症风险的预测模型相对较新。本研究将系统地综述已发表的用于预测 GDM 孕妇妊娠并发症的预后预测模型，描述其特征，比较其性能，并评估方法学质量和适用性。
通过搜索 MEDLINE 和 Embase 电子数据库来确定研究。标题和摘要筛选、全文审查和数据提取将由两名评审员独立完成。将使用专门为预测模型研究设计的适当工具对纳入的研究进行系统评估，以评估其偏倚风险和适用性。提取的数据将制表以方便对已发表的预测模型进行定性比较。如果合适，将对这些模型的预测性能的定量数据进行荟萃分析综合。
本综述将确定并总结所有已发表的用于预测 GDM 孕妇妊娠并发症的预后预测模型。如果合适，我们将通过荟萃分析比较不同环境和人群中模型的性能。这项工作将指导预后研究框架的后续阶段：进一步的模型开发、外部验证和模型更新以及影响评估。最终的模型将估计 GDM 妇女妊娠并发症的绝对风险，并将作为基于证据的 GDM 并发症风险预测模型纳入常规护理。它预计将为妇女及其临床医生提供个体化风险评估，并可能有助于决策。最终，本系统综述是迈向 GDM 个体化风险分层模型护理的重要一步，以便为妇女及其后代提供最大的预防和治疗干预，同时为低风险人群节省费用和危害。
PROSPERO 注册号 CRD42019115223。
