In International journal of endocrinology and metabolism
BACKGROUND : The current study aimed to compare fetal myocardial function and ventricular thickness in diabetic and normal pregnancies.
METHODS : Women with singleton pregnancies in the second or third trimester who were referred for routine prenatal or anomaly ultrasounds within March 2020 to February 2021 were enrolled in the study. Women with a positive history of overt or gestational diabetes mellitus (GDM) were considered the case group (n = 50), and women without GDM were considered the control group (n = 50). The study did not include women with multifetal pregnancy, hypertension, intrauterine growth retardation, and polyhydramnios. A complete fetal Doppler echocardiography was performed to measure isovolumic relaxation time (IVRT), left myocardial performance index (MPI), E/A ratio, right and left ventricular wall thickness, and end-diastolic interventricular septal thickness (IVST). The data were analyzed using three types of decision tree (DT) algorithms, and the performance of each DT was measured on the testing dataset.
RESULTS : The frequency of IVRT > 41 milliseconds was significantly higher in the case group than in the control group. The mean MPI values were 0.53 ± 0.15 and 0.43 ± 0.09 (P < 0.05), respectively, and the mean IVST values were 3.3 ± 1.11 and 2.49 ± 0.55 mm (P < 0.05) in the case and control groups, respectively, but not different between the subjects with overt or GDM (P > 0.05). Additionally, in the case group, the mean left MPI values were 0.57 ± 0.18 and 0.49 ± 0.12 in participants with poor and good glycemic control, respectively (P = 0.12).
CONCLUSIONS : Complete prenatal echocardiography performed in the second or third trimester is an appropriate tool for the diagnosis of fetal cardiac dysfunction in diabetic mothers and is suggested to perform for diabetic mothers, even those with good glycemic control.
Pooransari Parichehr, Mehrabi Sahar, Mirzamoradi Masoumeh, Salehgargari Soraya, Afrakhteh Maryam
2022-Oct
Echocardiography, Fetal Heart, Gestational Diabetes Mellitus, Machine Learning
