Risk stratification in early pregnancy for women at increased risk of gestational diabetes

https://doi.org/10.1016/j.diabres.2014.09.006Get rights and content

Highlights

  • A two-step GDM risk prediction strategy is explored in high-risk women.

  • When applied with a validated risk prediction tool, fasting glucose predicts GDM.

  • A 10 fold increased risk is demonstrated on IADPSG criteria.

  • Results present an accurate, feasible strategy for early pregnancy risk detection.

Summary

Aim

To evaluate the addition of fasting glucose and lipids to a simple, validated risk prediction tool for gestational diabetes (GDM) applied in early pregnancy.

Methods

Women at risk of developing GDM on a validated risk prediction tool were recruited in early pregnancy into a large randomised controlled trial. Outcome measures included fasting biochemical markers (glucose, lipids) at 12–15 weeks gestation and GDM diagnosis (28 weeks gestation). Multivariable logistic regression was used to identify additional predictive biochemical variables for GDM, with corresponding receiver operator characteristic (ROC) curves generated. Unadjusted and adjusted models were derived for both the Australasian Diabetes in Pregnancy (ADIPS) and the International Association for Diabetes in Pregnancy Study Group (IADPSG) GDM diagnostic criteria.

Results

51 (23%) Women were diagnosed with GDM based on ADIPS criteria, with 60 (30%) diagnosed based on IADPSG criteria. In all four regression models, fasting glucose was the strongest predictor for GDM development with an odds ratio range of 4.7–6.3 (ADIPS) and 8.8–10 (IADPSG). ROC curves revealed an area under the curve of 0.79 (95% CI: 0.72–0.86) for ADIPS criteria and 0.83 (95% CI: 0.77–0.90) for IADPSG criteria for adjusted models.

Conclusions

In a two-step approach, when applied with a validated risk prediction tool, fasting glucose in early pregnancy was predictive of GDM and incrementally improved risk identification, presenting potential for an early pregnancy, GDM risk screening strategy for streamlining of pregnancy care and opportunity for preventive intervention.

Introduction

In the setting of increasing gestational diabetes (GDM) prevalence, adoption of the International Association of Diabetes in Pregnancy Study Group (IADPSG) diagnostic criteria is pending, with endorsement from several key international bodies, including the American Diabetes Association (ADA) [1]. Along with rising maternal obesity rates and advancing maternal age, adoption of IADPSG criteria is anticipated to increase GDM prevalence ∼2 fold to ∼15 to 20% of pregnancies [2], increasing service burden and associated costs, with many health care providers likely under-resourced to provide adequate multi-disciplinary GDM care [3]. For these reasons, disparity remains between endorsement and implementation from a clinical practice perspective.

Whilst GDM diagnosis and treatment improves clinical outcomes for mothers and babies and is cost effective [4], [5], little effort has focused earlier in pregnancy on those women at high risk of GDM, despite clinical rationale for early risk identification. Risk identification and stratification is common in maternity care, enabling streamlined services and improved clinical outcomes, as is seen with multidisciplinary care in GDM. Continuity of pregnancy care improves obstetric outcomes [6], [7], yet women diagnosed with GDM are often transferred late in pregnancy to high risk services, disrupting continuity and leaving limited opportunity for preventive intervention. Early lifestyle intervention in pregnancy limits gestational weight gain (GWG), assists in managing obesity and may also prevent GDM, reducing prevalence by 40–80% [8], [9], [10], [11]. Longer term lifestyle intervention also reduces Type 2 diabetes (Type 2 DM) [12]. Therefore, early detection of women at risk of GDM may facilitate (i) early streamlined antenatal care, (ii) enhanced continuity of care, (iii) targeted lifestyle interventions to reduce GWG, and potentially reduce GDM and Type 2 DM, (iv) timely universal GDM screening and prompt GDM management and (v) improved patient experience and clinical outcomes in the short and long term. With rising GDM prevalence and opportunities for potential prevention of GDM and its complications, there is rationale for early pregnancy GDM risk screening to identify those at high GDM risk.

Three GDM risk prediction tools have been published internationally, although these tools have not been applied using the IADPSG GDM diagnostic criteria (summarised in [13]). Our Monash GDM risk tool was developed and validated based on Australian data [14]. It was then internationally validated [13] and has also been successfully applied in a clinical setting for a targeted lifestyle randomised controlled trial (RCT) intervention [11]. The Monash tool, based on a score generated from simple GDM risk factors performs well, but could be improved further to increase clinical utility [14]. Additionally, while the role of biochemical markers in GDM risk predication have been studied with some success [15], [16], [17], clinical application is limited by cost and patient inconvenience [18]. Given the rationale for early identification of those at high GDM risk, here we undertook a novel study aiming to improve risk prediction by testing a two-step approach involving combining a validated epidemiological risk prediction tool and in those at risk only, the addition of simple biochemical markers to improve prediction of GDM risk on IADPSG diagnostic criteria.

Section snippets

Prior GDM risk prediction tool development and validation

As previously described, we undertook a retrospective cohort study at a tertiary hospital in Victoria, Australia in 2008. Routine pregnancy data in 4276 women was analysed to identify clinical risk factors for GDM [14]. Women were divided in to a derivation group (n = 2880) and a validation group (n = 1396). Derivation group data was used to develop a simple risk predictor scoring tool, with scores derived from rounded odds ratios of clinical risk factors with a possible score range of zero to

Enhancing risk prediction—Current study

In this current analysis, we explore the impact of the addition of biochemical measures to the Monash risk prediction tool in the context of the HeLP-her HRP study. Specifically, we focus on the impact of a fasting blood glucose and lipid measurement taken in early pregnancy on GDM risk prediction, in women already identified at high risk based on the Monash validated GDM risk prediction tool.

Measures

All outcome measures were completed at baseline (12–15 weeks) and 26–28 weeks gestation. Basic

Results

Of the 238 women that were recruited into the RCT and completed baseline data collection, 224 women were included in final data analysis here (Fig. 2). Of the 224, 51 (23%) were diagnosed with GDM based on the ADIPS criteria, confirming a high-risk cohort. Applying the IADPSG criteria on the 202 women that completed an OGTT would have resulted in 60 women diagnosed with GDM (30%). Baseline demographics (age, BMI, fat mass [kg], country of birth, previous pregnancies) between these subgroups

Discussion

This study builds on the application of the simple, internationally validated Monash GDM risk prediction tool, applied in early pregnancy, to identify women at high risk of GDM in a clinical pregnancy care setting. We demonstrate that the addition of early pregnancy fasting glucose, found to be the strongest predictor of GDM, strengthened the Monash GDM risk prediction tool yielding an 80% and 83% probability that we can identify women early in pregnancy who will develop GDM, on ADIPS and

Disclosure of interests

The authors have nothing to disclose. No financial disclosures were reported by the authors of this paper.

Ethics approval

The Southern Health Research Advisory and Ethics Committee approved the study and all participants gave written informed consent (Southern Health known as Monash Health as of 2013). Approval date 1/4/2008; project number 07216C.

Funding sources

This project is supported by a BRIDGES grant from the International Diabetes Federation. BRIDGES, an International Diabetes Federation project is supported by an educational grant from Lilly Diabetes (Project Number: LT07-121). The Jack Brockhoff Foundation also provided funding for this study.

Conflict of interest statement

The authors declare they have no competing interests.

Clinical trial registration

Australian New Zealand Clinical Trial Registry Number: ACTRN12608000233325. Registered 7/5/2008. [www.anzctr.org.au].

Acknowledgements

The authors would like to acknowledge Carolyn Allan, Eldho Paul and Wan Teh for input in to the original Monash GDM risk prediction tool development. In addition, we acknowledge Boyd Strauss for body composition provision, Amanda Hulley, Lauren Snell and Melanie Gibson-Helm for recruitment and data collation, Deborah Thompson and Nicole Ng for data entry. Cheryce Harrison is supported by a Postdoctoral Fellowship (100168) from the National Heart Foundation. Helena Teede is an NHMRC research

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