Validation of early risk-prediction models for gestational diabetes based on clinical characteristics.
OBJECTIVE
Gestational diabetes (GDM) is generally diagnosed late in pregnancy, precluding early preventive interventions. This study aims to validate, in a large Caucasian population of pregnant women, models based on clinical characteristics proposed in the literature to identify, early in pregnancy, those at high risk of developing GDM in order to facilitate follow up and prevention.


METHODS
This is a cohort study including 7929 pregnant women recruited prospectively at their first prenatal visit. Clinical information was obtained by a self-administered questionnaire and extraction of data from the medical records. The performance of four proposed clinical risk-prediction models was evaluated for identifying women who developed GDM and those who required insulin therapy.


RESULTS
The four models yielded areas under the receiver operating characteristic curve (AUC) between 0.668 and 0.756 for the identification of women who developed GDM, a performance similar to those obtained in the original studies. The best performing model, based on ethnicity, body-mass index, family history of diabetes and past history of GDM, resulted in sensitivity, specificity and AUC of 73% (66-79), 81% (80-82) and 0.824 (0.793-0.855), respectively, for the identification of GDM cases requiring insulin therapy.


CONCLUSIONS
External validation of four risk-prediction models based exclusively on clinical characteristics yielded a performance similar to those observed in the original studies. In our cohort, the strategy seems particularly promising for the early prediction of GDM requiring insulin therapy. Addition of recently proposed biochemical markers to such models has the potential to reach a performance justifying clinical utilization.
CHU de Québec Research Center, 10 rue de l'Espinay, Quebec City, QC, Canada G1L 3L5, and Département de biologie moléculaire, biochimie médicale et pathologie, Faculté de médecine, Université Laval, 1050 avenue de la Médecine, Quebec City, QC, Canada G1V 0A6.
Sébastien
S
Thériault
CHU de Québec Research Center, 10 rue de l'Espinay, Quebec City, QC, Canada G1L 3L5, and Département de biologie moléculaire, biochimie médicale et pathologie, Faculté de médecine, Université Laval, 1050 avenue de la Médecine, Quebec City, QC, Canada G1V 0A6.
Jean-Claude
JC
Forest
CHU de Québec Research Center, 10 rue de l'Espinay, Quebec City, QC, Canada G1L 3L5, and Département de biologie moléculaire, biochimie médicale et pathologie, Faculté de médecine, Université Laval, 1050 avenue de la Médecine, Quebec City, QC, Canada G1V 0A6.
Jacques
J
Massé
CHU de Québec Research Center, 10 rue de l'Espinay, Quebec City, QC, Canada G1L 3L5, and Département de biologie moléculaire, biochimie médicale et pathologie, Faculté de médecine, Université Laval, 1050 avenue de la Médecine, Quebec City, QC, Canada G1V 0A6. Electronic address: yves.giguere@crsfa.ulaval.ca.
Yves
Y
Giguère
D000328:Adult; D015992:Body Mass Index; D003920:Diabetes Mellitus / Q000175:diagnosis* / Q000188:drug therapy; D016640:Diabetes, Gestational / Q000175:diagnosis* / Q000188:drug therapy; D005260:Female; D005500:Follow-Up Studies; D005951:Glucose Tolerance Test; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D007328:Insulin / Q000627:therapeutic use; D015233:Models, Statistical; D011247:Pregnancy; D011446:Prospective Studies; D012372:ROC Curve; D012307:Risk Factors
D007004:Hypoglycemic Agents; D007328:Insulin
Early pregnancy; External validation; Gestational diabetes mellitus; Prediction models; Risk factors