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Gestational diabetes: Development of an early risk prediction tool to facilitate opportunities for prevention

Authors

  • Helena J. TEEDE,

    1. Jean Hailes Foundation for Women’s Health, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria
    2. Diabetes Unit, Southern Health, Clayton, Victoria
    3. Department of Obstetrics and Gynaecology, Centre for Women’s Health, MIMR, Monash University, Clayton, Victoria, Australia
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  • Cheryce L. HARRISON,

    1. Jean Hailes Foundation for Women’s Health, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria
    2. Diabetes Unit, Southern Health, Clayton, Victoria
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  • Wan T. TEH,

    1. Department of Obstetrics and Gynaecology, Centre for Women’s Health, MIMR, Monash University, Clayton, Victoria, Australia
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  • Eldho PAUL,

    1. Jean Hailes Foundation for Women’s Health, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria
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  • Carolyn A. ALLAN

    1. Diabetes Unit, Southern Health, Clayton, Victoria
    2. Department of Obstetrics and Gynaecology, Centre for Women’s Health, MIMR, Monash University, Clayton, Victoria, Australia
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Prof Helena J. Teede, Jean Hailes Foundation for Women’s Health and Monash University School of Public Health and Preventive Medicine, Locked Bag 29, Monash Medical Centre, Clayton, Vic. 3168, Australia. Email: Helena.Teede@monash.edu

Abstract

Background and aim:  In the setting of advancing maternal age, escalating obesity and increasing Gestational Diabetes Mellitus (GDM) rates, we aimed to develop a novel risk prediction tool to identify high-risk women in early pregnancy, specifically to facilitate targeted antenatal prevention of GDM.

Methods:  In this retrospective, observational study, first-trimester data collected routinely by midwifery staff in 4276 women attending a large tertiary hospital in 2007/2008 was analysed to examine predictive factors for GDM. GDM was diagnosed with a 28-week oral glucose tolerance test. The data set included a derivation group (n = 2880, from 2007 deliveries) and a validation group (n = 1396, from 2008). Multivariate analysis generated a scoring system.

Results:  GDM was significantly correlated with a number of factors: past history of GDM, increasing maternal age and body mass index, Asian descent and family history of diabetes. Validation group clinical scores achieved a sensitivity of 61.3% and specificity of 71.4% for differentiating women according to their risk of developing GDM.

Conclusions:  Risk factors for GDM are easily identified at the first-trimester midwifery hospital booking visit. A risk prediction tool, derived from risk factors in early pregnancy, identifies women at high risk of GDM. This represents a novel approach to facilitate targeted early intervention with the potential to prevent development of or ameliorate, GDM.

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