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Aim To evaluate the incidence of congenital malformations in children exposed prenatally to antiseizure medications (ASMs), to assess other perinatal and fetal complications, and to determine the potential predictors for these complications. Method A retrospective review of pregnancy outcomes of women with epilepsy. Patients were followed up at the King Faisal Specialist Hospital and Research Centre, Riyadh and Jeddah, Saudi Arabia, between Dec 1993 and Oct 2020. Results Of 162 pregnancies included, 10 (6.17%) congenital malformations were observed, 6.82% in ASM-exposed babies versus 3.33% in babies of epilepsy-untreated mothers (P = 0.69). The overall incidence of perinatal and fetal complications was 53%; most frequent were low birth weight (24%), preterm birth (19%), transfer to neonatal intensive care unit (18%) and abortion (8%). These complications were higher in the untreated group (66.67%) than in the ASM group (50%). The use of other non-antiseizure medications during pregnancy was the only factor that significantly increased the risk of complications. Conclusion Prenatal exposure to ASMs was associated with increased risk of congenital malformations. However, overall perinatal and fetal complications were higher in the untreated group than in the ASM group, which could be explained by maternal seizures. Therefore, taking ASMs to control epilepsy and prevent perinatal complications may outweigh the risks of teratogenicity.

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Background: Factors associated with hospital mortality are usually identified and their effects are quantified through statistical modeling. To guide the choice of the best statistical model, we first quantify the predictive ability of each model and then use the CIHI index to see if the hospital policy needs any change. Objectives: The main purpose of this study compared three statistical models in the evaluation of the association between hospital mortality and two risk factors, namely subject's age at admission and the length of stay, adjusting for the effect of Diagnostic Related Groups (DRG). Methods: We use several SAS procedures to quantify the effect of DRG on the variability in hospital mortality. These procedures are the Logistic Regression model (ignoring the DRG effect), the Generalized Estimating Equation (GEE) that takes into account the within DRG clustering effect (but the within cluster correlation is treated as nuisance parameter), and the Generalized Linear Mixed Model (GLIMMIX). We showed that the GLIMMIX is superior to other models as it properly accounts for the clustering effect of "Diagnostic Related Groups" denoted by DRG. Results: The GLM procedure showed that the proportional contribution of DRG is 16%. All three models showed significant and increasing trend in mortality (P < 0.0001) with respect to the two risk factors (age at admission, and hospital length of stay). It was also clear that the CIHI index was not different under the three models. We re-estimated the models parameters after dichotomizing the risk factors at the optimal cutoff points, using the ROC curve. The parameters estimates and their significance did not change.