Danielle A J M Schoenaker1, Yvonne Vergouwe2, Sabita S Soedamah-Muthu3, Leonie K Callaway4, Gita D Mishra5. 1. School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia; Centre for Behavioural Research in Cancer, Cancer Council Victoria, Melbourne, Australia; Discipline of Obstetrics and Gynaecology, Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia. Electronic address: danielle.schoenaker@uq.net.au. 2. Department of Public Health, Centre for Medical Decision Sciences, Erasmus MC, Rotterdam, the Netherlands. 3. Center of Research on Psychology in Somatic Diseases (CORPS), Department of Medical and Clinical Psychology, Tilburg University, Tilburg, the Netherlands; Institute for Food, Nutrition and Health, University of Reading, Reading, United Kingdom. 4. UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia; Obstetric Medicine, Royal Brisbane and Women's Hospital, Brisbane, Australia. 5. School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
Abstract
AIM: To develop a prediction model for preconception identification of women at risk of gestational diabetes mellitus (GDM). METHODS: Data from a prospective cohort, the Australian Longitudinal Study on Women's Health, were used. Nulliparous women aged 18-23 who reported a pregnancy up to age 37-42 were included. Preconception predictors of GDM during a first pregnancy were selected using logistic regression. Regression coefficients were multiplied by a shrinkage factor estimated with bootstrapping to improve prediction in external populations. RESULTS: Among 6504 women, 314 (4.8%) developed GDM during their first pregnancy. The final prediction model included age at menarche, proposed age at future first pregnancy, ethnicity, body mass index, diet, physical activity, polycystic ovary syndrome, and family histories of type 1 or 2 diabetes and GDM. The model showed good discriminative ability with a C-statistic of 0.79 (95% CI 0.76, 0.83) after internal validation. More than half of the women (58%) were classified to be at risk of GDM (>2% predicted risk), with corresponding sensitivity and specificity values of 91% and 43%. CONCLUSIONS: Nulliparous women at risk of GDM in a future first pregnancy can be accurately identified based on preconception lifestyle and health-related characteristics. Further studies are needed to test our model in other populations.
AIM: To develop a prediction model for preconception identification of women at risk of gestational diabetes mellitus (GDM). METHODS: Data from a prospective cohort, the Australian Longitudinal Study on Women's Health, were used. Nulliparous women aged 18-23 who reported a pregnancy up to age 37-42 were included. Preconception predictors of GDM during a first pregnancy were selected using logistic regression. Regression coefficients were multiplied by a shrinkage factor estimated with bootstrapping to improve prediction in external populations. RESULTS: Among 6504 women, 314 (4.8%) developed GDM during their first pregnancy. The final prediction model included age at menarche, proposed age at future first pregnancy, ethnicity, body mass index, diet, physical activity, polycystic ovary syndrome, and family histories of type 1 or 2 diabetes and GDM. The model showed good discriminative ability with a C-statistic of 0.79 (95% CI 0.76, 0.83) after internal validation. More than half of the women (58%) were classified to be at risk of GDM (>2% predicted risk), with corresponding sensitivity and specificity values of 91% and 43%. CONCLUSIONS: Nulliparous women at risk of GDM in a future first pregnancy can be accurately identified based on preconception lifestyle and health-related characteristics. Further studies are needed to test our model in other populations.
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