Favipiravir does not appear to be a major teratogen: Case series from Türkiye.

INTRODUCTION: Favipiravir has gained attention during the Coronavirus Disease-2019 pandemic due to its potential antiviral effect against Severe Acute Respiratory Syndrome Coronavirus-2. Favipiravir has been identified as a teratogen in animal studies, but there is limited human data. We aimed to evaluate the pregnancy outcomes of women exposed to favipiravir during the pandemic. MATERIAL AND METHODS: Pregnant women who were exposed to favipiravir and applied to Marmara University School of Medicine Medical Pharmacology Outpatient Clinic Teratology Information Service between December 2020-September 2021 are included in the study. The demographic information, medical and obstetric histories of patients were acquired during admission, the outcomes of the pregnancies and the characteristics of the infants were gathered by regular phone calls. The infants whose parents consented were evaluated by a pediatrician for general well-being and congenital anomalies. RESULTS: 22 pregnant women were included in this study. 81.8 % received the recommended favipiravir dose (8000 mg in 5 days), in the first trimester. Two patients were lost to follow-up, there was one elective termination and 19 live births. Congenital anomalies were found in 2 infants, one of whom had 9q34 duplication syndrome. Except for these, all newborns examined by the pediatrician were healthy. DISCUSSION: Within a limited case series, a subset of the infants exposed to favipiravir prenatally were followed up to 1 year of age. Two infants exhibited congenital malformations that cannot be directly linked to favipiravir due to confounding variables. Considering the limited data published, favipiravir does not appear to be a major teratogen.

Evaluation of Risk Factors Associated With Antihypertensive Treatment Success Employing Data Mining Techniques.

OBJECTIVE: This study aimed to evaluate the effects of potential risk factors on antihypertensive treatment success. METHODS: Patients with hypertension who were treated with antihypertensive medications were included in this study. Data from the last visit were analyzed retrospectively for each patient. To evaluate the predictive models for antihypertensive treatment success, data mining algorithms (logistic regression, decision tree, random forest, and artificial neural network) using 5-fold cross-validation were applied. Additionally, study parameters between patients with controlled and uncontrolled hypertension were statistically compared and multiple regression analyses were conducted for secondary endpoints. RESULTS: The data of 592 patients were included in the analysis. The overall blood pressure control rate was 44%. The performance of random forest algorithm (accuracy = 97.46%, precision = 97.08%, F1 score = 97.04%) was slightly higher than other data mining algorithms including logistic regression (accuracy = 87.31%, precision = 86.21%, F1 score = 85.74%), decision tree (accuracy = 76.94%, precision = 70.64%, F1 score = 76.54%), and artificial neural network (accuracy = 86.47%, precision = 83.85%, F1 score = 84.86%). The top 5 important categorical variables (predictive correlation value) contributed the most to the prediction of antihypertensive treatment success were use of calcium channel blocker (-0.18), number of antihypertensive medications (0.18), female gender (0.10), alcohol use (-0.09) and attendance at regular follow up visits (0.09), respectively. The top 5 numerical variables contributed the most to the prediction of antihypertensive treatment success were blood urea nitrogen (-0.12), glucose (-0.12), hemoglobin A1c (-0.12), uric acid (-0.09) and creatinine (-0.07), respectively. According to the decision tree model; age, gender, regular attendance at follow-up visits, and diabetes status were identified as the most critical patterns for stratifying the patients. CONCLUSION: Data mining algorithms have the potential to produce predictive models for screening the antihypertensive treatment success. Further research on larger populations and longitudinal datasets are required to improve the models.