time point (Fig. 1D). A significant temporal decrease in eGFR with gestation was observed only in DM+PE+ women (p=0.002). The presence of urinary leukocytes did not affect the eGFR results. A New Model to Predict PE To explore the significance of first trimester uNGALcc and eGFR in pregnant women with T1DM, we developed a mathematical model to predict PE risk before 15 weeks of gestation using readily available clinical information. To prevent confounding by NGAL derived from leukocytes, only participants with leukocyte-negative urine were included. A fixed group of covariates was selected for the model, considering group comparisons at baseline, known risk factors, and availability (i.e. clinical convenience): BMI, HbA1c, and daily total insulin dose (all continuous). After adjustment for these factors, we found that the odds of developing PE were increased seven-fold for every two-fold increase in first trimester uNGALcc. ROC analysis was used to assess the utility of uNGALcc to improve PE prediction based on the three clinical factors at V1 (Fig. 2, Table 2). A model with only the clinical risk factors performed better than one using uNGALcc alone (AUC= 0.75 vs. 0.714, respectively). However, the addition of uNGALcc to the clinical risk factor model improved the predictive value (AUC=0.849). Although this improvement did not reach statistical significance (p=0.157), IDI and NRI results did indicate significant utility: adding uNGALcc improved discrimination between (p=0.016) and reclassification of (p=0.046) of women according to subsequent PE status. The final model resulted in a sensitivity of 75% and specificity of 79%. Adjusting to account for the overall cohort prevalence (~21%), the positive predicted value is 60% and
time point (Fig. 1D). A significant temporal decrease in eGFR with gestation was observed only in DM+PE+ women (p=0.002). The presence of urinary leukocytes did not affect the eGFR results. A New Model to Predict PE To explore the significance of first trimester uNGALcc and eGFR in pregnant women with T1DM, we developed a mathematical model to predict PE risk before 15 weeks of gestation using readily available clinical information. To prevent confounding by NGAL derived from leukocytes, only participants with leukocyte-negative urine were included. A fixed group of covariates was selected for the model, considering group comparisons at baseline, known risk factors, and availability (i.e. clinical convenience): BMI, HbA1c, and daily total insulin dose (all continuous). After adjustment for these factors, we found that the odds of developing PE were increased seven-fold for every two-fold increase in first trimester uNGALcc. ROC analysis was used to assess the utility of uNGALcc to improve PE prediction based on the three clinical factors at V1 (Fig. 2, Table 2). A model with only the clinical risk factors performed better than one using uNGALcc alone (AUC= 0.75 vs. 0.714, respectively). However, the addition of uNGALcc to the clinical risk factor model improved the predictive value (AUC=0.849). Although this improvement did not reach statistical significance (p=0.157), IDI and NRI results did indicate significant utility: adding uNGALcc improved discrimination between (p=0.016) and reclassification of (p=0.046) of women according to subsequent PE status. The final model resulted in a sensitivity of 75% and specificity of 79%. Adjusting to account for the overall cohort prevalence (~21%), the positive predicted value is 60% and