Results
We included 203 women with an HbA1c measure at 1 year postpartum; of whom 71 (35%) had impaired glucose tolerance. Nine women (4%) had HbA1c ≥ 6.5%, consistent with type 2 diabetes at 1 year postpartum. Overall, our cohort was diverse (31% Hispanic and 10% non-Hispanic Black), women were overweight (median BMI 29.6; IQR 25.7, 34.9), over half required insulin or medication for GDM management during pregnancy, and 12% were diagnosed with GDM at <24 weeks’ gestation (Table 1). Hispanic women were over represented among those who developed impaired glucose tolerance (48% with impaired glucose tolerance versus 21% without), while white women were under-represented (32% with impaired glucose tolerance vs 57% without). Women who developed impaired glucose tolerance were more likely to report a family history of type 2 diabetes (64% vs 48%), be obese pre-pregnancy (70% vs 36%), be diagnosed with GDM at <24 weeks’ gestation (23% vs 6%), have experienced GDM in a previous pregnancy (34% vs 17%), and require insulin or medication for GDM management in their most recent pregnancy (67% vs 52%).
Eight candidate predictors were selected for inclusion into the final multivariable model. They included continuous pre-pregnancy weight, continuous pre-pregnancy BMI, pre-pregnancy obesity (BMI ≥ 30 kg/m2, versus not), GDM in a previous pregnancy (yes versus no or first pregnancy), Hispanic ethnicity (yes versus no), GDM diagnosis before 24 weeks’ gestation (yes versus no), and continuous measures of fasting and 2-hour plasma glucose at 2 days postpartum. Wea priori specified that candidate predicators would be included into the final model if they were selected by a Lasso model in >60% of imputed datasets; however, all but one of the predictors included in the final model were selected in 100% of imputed data sets (Supplemental Figure S3). The final multivariable model had an AUC of 0.81 (95% CI 0.74, 0.87; Table 2).
In sensitivity analyses, we examined the robustness of the final multivariable model in several ways. First, we excluded 2-day postpartum glucose data because glucose testing at this time is not standard of care. Second, we removed each of the weight and BMI variables individually to evaluate the impact of having highly correlated variables (e.g. weight and BMI) in the final model. Third, we removed the indicator for Hispanic ethnicity since ethnicity is not a reliable indicator of genetic differences and this variable likely captures a complex mix of ethnicity and social processes which may influence Hispanic women’s risk of impaired glucose tolerance.25Finally, we replaced Hispanic ethnicity in the final model with family history of type 2 diabetes to evaluate if a more direct measure of genetic risk (e.g. family history) influenced model results. All sensitivity analyses resulted in comparable or slightly reduced AUC compared to the final multivariable model from the primary analysis (Supplemental Table S2).
To identify women with impaired glucose metabolism at 1 year postpartum, we examined several possible cut-points of predicted probabilities from the final multivariable model. A cut-point of 0.13 resulted in a sensitivity of 96% and specificity of 35%, 44% PPV and 94% NPV to identify women with impaired glucose tolerance at 1 year postpartum (Table 3; Supplemental Figure S4). This cut point would identify 76% of the population for potential postpartum intervention, miss predicting impaired glucose tolerance in 2% of women, and incorrectly predict impaired glucose tolerance in 42% of women. A cut-point of 0.65 resulted in specificity of 96%, sensitivity of 39%, PPV 8% and NPV 75%. This cut-point would identify 17% of the population for additional intervention, miss predicting impaired glucose tolerance in 21% of women, and incorrectly predict impaired glucose tolerance in 3% of women.
To assess calibration of the final multivariable model, we compared observed and predicted probabilities of the outcome. Observed and predicted probabilities were similar for women in the lower two quartiles of risk, but were overestimated for women in the highest two quartile or risk, which may indicate model overfitting because of the small number of events in this sample (Supplemental Figure S5).22