Katrien Benhalima1, Paul Van Crombrugge2, Carolien Moyson3, Johan Verhaeghe4, Sofie Vandeginste5, Hilde Verlaenen5, Chris Vercammen6, Toon Maes6, Els Dufraimont7, Christophe De Block8, Yves Jacquemyn9,10, Farah Mekahli11, Katrien De Clippel12, Annick Van Den Bruel13, Anne Loccufier14, Annouschka Laenen15, Caro Minschart3, Roland Devlieger4, Chantal Mathieu3. 1. Department of Endocrinology, University Hospital Gasthuisberg, KU Leuven, Herestraat 49, 3000, Louvain, Belgium. katrien.benhalima@uzleuven.be. 2. Department of Endocrinology, OLV ziekenhuis Aalst-Asse-Ninove, Moorselbaan 164, 9300, Aalst, Belgium. 3. Department of Endocrinology, University Hospital Gasthuisberg, KU Leuven, Herestraat 49, 3000, Louvain, Belgium. 4. Department of Obstetrics and Gynecology, University Hospital Gasthuisberg, KU Leuven, Herestraat 49, 3000, Louvain, Belgium. 5. Department of Obstetrics and Gynecology, OLV ziekenhuis Aalst-Asse-Ninove, Moorselbaan 164, 9300, Aalst, Belgium. 6. Department of Endocrinology, Imelda ziekenhuis, Imeldalaan 9, 2820, Bonheiden, Belgium. 7. Department of Obstetrics and Gynecology, Imelda ziekenhuis, Imeldalaan 9, 2820, Bonheiden, Belgium. 8. Department of Endocrinology-Diabetology-Metabolism, Antwerp University Hospital, Wilrijkstraat 10, 2560, Edegem, Belgium. 9. Department of Obstetrics and Gynecology, Antwerp University Hospital, Wilrijkstraat 10, 2560, Edegem, Belgium. 10. ASTARC and Global Health Institute, Antwerp University, UA, Prinsstraat 13, 2000, Antwerp, Belgium. 11. Department of Endocrinology, Kliniek St-Jan Brussel, Kruidtuinlaan 32, 1000, Brussels, Belgium. 12. Department of Obstetrics and Gynecology, Kliniek St-Jan Brussel, Kruidtuinlaan 32, 1000, Brussels, Belgium. 13. Department of Endocrinology, AZ St Jan Brugge, Ruddershove 10, 8000, Brugge, Belgium. 14. Department of Obstetrics and Gynecology, AZ St Jan Brugge, Ruddershove 10, 8000, Brugge, Belgium. 15. Center of Biostatics and Statistical Bioinformatics, KU Leuven, Kapucijnenvoer 35 bloc d - box 7001, 3000, Louvain, Belgium.
Abstract
AIMS: We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. METHODS: A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). RESULTS: A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69-0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64-0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72-0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66-0.78), compared to an AUC of 0.67 (95% CI 0.63-0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62-0.70) using the Teede model. CONCLUSIONS: A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
AIMS: We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. METHODS: A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). RESULTS: A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69-0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64-0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72-0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66-0.78), compared to an AUC of 0.67 (95% CI 0.63-0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62-0.70) using the Teede model. CONCLUSIONS: A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
Entities:
Keywords:
2013 WHO criteria; Gestational diabetes mellitus; Prediction; Risk factors
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