A clinical model and nomogram for early prediction of gestational diabetes based on common maternal demographics and routine clinical parameters

We aimed to develop a risk prediction model for gestational diabetes mellitus (GDM) based on the common maternal demographics and routine clinical variables in Chinese population.Individual information was collected from December 2018 to October 2019 by a pretested questionnaire on demographics, medical and family history, and lifestyle factors. Multivariable logistic regression was performed to establish a predictive model for GDM by variables in pre- and early pregnancy. The consistency and discriminative validity of the model were evaluated by Hosmer-Lemeshow goodness-of-fit testing and ROC curve analysis. Internal validation was appraised by fivefold cross-validation. Clinical utility was assessed by decision curve analysis.Total 3263 pregnant women were included with 17.2% prevalence of GDM. The model equation was: LogitP = -11.432 + 0.065 × maternal age (years) + 0.061 × pre-pregnancy BMI (kg/m2 ) + 0.055 × weight gain in early pregnancy (kg) + 0.872 × history of GDM + 0.336 × first-degree family history of diabetes +0.213 × sex hormone usages during pre- or early pregnancy + 1.089 × fasting glucose (mmol/L) + 0.409 × triglycerides (mmol/L) + 0.082 × white blood cell count (109/L) + 0.669 × positive urinary glucose. Homer-Lemeshow goodness-of-fit testing indicated a good consistency between predictive and actual data (p = 0.586). The area under the ROC curve (AUC) was 0.720 (95% CI: 0.697 ~ 0.744). Cross-validation suggested a good internal validity of the model. A nomogram has been made to establish an easy to use scoring system for clinical practice.The predictive model of GDM exhibited well acceptable predictive ability, discriminative performance, and clinical utilities. The project was registered in clinicaltrial.gov.com with identifier of NCT03922087.