Poster Presentation Australasian Diabetes in Pregnancy Society Annual Scientific Meeting 2016

Does a validated GDM Insulin prediction model work with different treatment targets?   (#115)

Robyn A Barnes 1 , Tang Wong 1 2 , Glynis P Ross 1 3 , Bin B Jalaludin 4 5 , Jencia Wong 3 6 , Lynda Molyneaux 6 , Lesley MacDonald-Wicks 7 , Carmel Smart 7 , Clare Collins 7 , Jeff R Flack 1 2 8
  1. Diabetes Centre, Bankstown-Lidcombe Hospital, Bankstown, NSW, Australia
  2. Faculty of Medicine , University of NSW, Sydney, NSW, Australia
  3. Faculty of Medicine, University of Sydney, Sydney, NSW, Australia
  4. Epidemiology, Healthy People and Places Unit, South Western Sydney Local Health District, Sydney, NSW, Australia
  5. School of Public Health and Community Medicine, University of NSW, Sydney, NSW, Australia
  6. Diabetes Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
  7. Faculty of Health and Medicine, The University of Newcastle, Newcastle, NSW, Australia
  8. School of Medicine, Western Sydney University, Campbelltown, NSW, Australia

Background: A validated model was developed by Bankstown-Lidcombe Hospital (B-LH) for prediction of therapy type and adverse outcomes in women with Gestational Diabetes Mellitus (GDM) (1). The Model, using seven clinical items, identifies low and high risk women at GDM diagnosis for triage into different models of care.

Aim: To validate the model in a clinical population with a different ethnic mix, therapeutic targets and model of care.

Methods: De-identified, prospectively collected data were analysed from a major Sydney Teaching Hospital (Royal Prince Alfred Hospital (RPAH)) for women diagnosed from 1992-2010 by 1991 GDM Ad Hoc Working Party, thence 1998 ADIPS criteria (2,3). Treatment targets were 5.3mmol/L fasting, 6.7mmol/L for 2-hour post-prandial (to 1999) and thereafter 7.5mmol/L for 1-hour post-prandial glucose. Seven dichotomous variables were assessed against therapy type: Medical Nutrition Therapy (MNT) only or MNT plus insulin (MNT+I). A receiver operator curve (ROC) of sensitivity plotted against 1-specificity was constructed based on the number of predictors present (0-7) versus therapy outcome.

Results: Data were available for 1381 women, mean±SD age 33.1±5.1 years, GDM diagnosis at 26.4±5.7 weeks, pre-pregnancy BMI 23.9±5.1kg/m2, mean OGTT fasting BGL 4.6±0.7 mmol/L and HbA1c at GDM diagnosis 5.3±0.5%. Main ethnicities were South-East Asian 44.1%, European 32.5%, and South Asian 11.8%. Apart from gestation at diagnosis and HbA1c, all were significantly different to B-LH (p<0.05).The Table shows the number of predictors present and the corresponding percentage of women requiring MNT only versus MNT+I. The area under the ROC was 0.634 (95%CI 0.582–0.686).

576762e36dcdf-ADIPS+2016+RB+RPA+Data+Test2+TABLE.jpg

Conclusion: As previously found (1), the greater the number of predictors, the greater the likelihood of MNT+1. Conversely, the less predictors present, the greater likelihood of MNT only. These findings provide further validation of the model.

Acknowledgements: We thank all staff involved in data collection and maintenance of the B-LH and RPAH databases.

  1. Barnes R, Wong T, Ross G, Jalaludin B, Wong V, Collins C, MacDonald-Wicks L, Smart C, Flack J (2016) A Novel Validated Model For the Prediction of Insulin Therapy Initiation and Adverse Perinatal Outcomes in Women with Gestational Diabetes Mellitus. Diabetologia In Press.
  2. Martin FIR for the Ad Hoc Working Party. The diagnosis of gestational diabetes. Med J Aust 1991; 155: 112.
  3. Hoffman L, Nolan, C, Wilson, JD, Oats JJN, Simmons D. Gestational diabetes mellitus management guidelines. The Australasian Diabetes In Pregnancy Society. Med J Aust 1998; 169: 93-97.