External Validation of Risk Prediction Model for Gestational Diabetes: Individual Participant Data Meta-Analysis of Randomized Trials

Background An original validated risk prediction model with good discriminatory prognostic performance for predicting gestational diabetes (GDM) diagnosis, has been updated for recent international association of diabetes in pregnancy study group (IADPSG) diagnostic criteria. However, the updated model is yet to be externally validated on an international dataset. Aims To perform an external validation of the updated risk prediction model to evaluate model indices such as discrimination and calibration based on data from the International Weight Management in Pregnancy (i-WIP) Collaborative Group. Materials and Methods The i −WIP dataset was used to validate the GDM prediction tool across discrimination and model calibration. Results Overall 7689 individual patient data were included, with 17.4 % with GDM, however only 113 cases were available using IADPSG (International Association of Diabetes and Pregnancy Groups) criteria for 75 g OGTT glucose load and ACOG (American College of Obstetricians and Gynecologists) for 100 g glucose load and having the routine clinical risk factor data. The GDM model was moderately discriminatory (Area Under the Curve (AUC) of 0.67; 95 % CI 0.59 to 0.75), Sensitivity 81.0 % (95 % CI 66.7 % to 90.9 %), specificity 53 % (40.3 % to 65.4 %). The GDM score showed reasonable calibration for predicting GDM (slope = 0.84, CITL = 0.77). Imputation for missing data increased the sample to n = 253, and vastly improved the discrimination and calibration of the model to AUC = 78 (95 % CI 72 to 85), sensitivity (81 %, 95 % CI 66.7 % to 90.9 %) and specificity (75 %, 95 % CI 68.8 % to 81 %). Conclusion The updated GDM model showed promising discrimination in predicting GDM in an internationally population sourced from RCT individual patient data. External validations are essential in order for the risk prediction area to advance, and we demonstrate the utility of using existing RCT data from different global settings. Despite limitation associated with harmonising the data to the variable types in the model, the validation model indices were reasonable, supporting generalizability across continents and populations.