Background: Congenital heart disease/defect (CHD) is one of the most common congenital disabilities. Early diagnosis of CHD can improve the prognosis of newborns with CHD. The aim of this study was to evaluate the relationship between the factors and the onset of fetal congenital heart disease by measuring fetal umbilical artery (UA) Doppler index, maternal HCY, and Cys C levels during pregnancy. Methods: This retrospective study analyzed 202 fetuses with CHD, including 77 cases (39.1%) of simple CHD and 120 cases (60.9%) of complex CHD. Singleton pregnant women who were examined at the same time and whose malformation screening did not suggest any structural abnormalities in the fetus were assigned to the control group (n = 400). The UA Doppler index, plasma HCY, and Cys C levels were compared among the pregnant women across the three groups, and logistic regression analysis was performed on statistically significant markers. The ROC of UA S/D, PI, RI, HCY, and Cys C were plotted, and the area under the ROC (AUC) was calculated. Results: The UA S/D, PI, and RI in the complex CHD group were significantly higher than those in the control group (p < 0.05). The levels of HCY and Cys C in the CHD group were significantly higher than those in the control group (p < 0.05). HCY and S/D revealed a positive correlation (r = 0.157), and the difference was statistically significant (p < 0.001). Cys C and S/D were positively correlated (r = 0.131), and the difference was statistically significant (p < 0.05). The levels of UA Doppler indices, maternal plasma HCY, and Cys C were elevated in fetuses with CHD. The AUC of the combined test of the UA index, HCY, and Cys C was higher than that of each individual test. Conclusions: Elevated levels of the UA doppler indices, HCY, and Cys C during pregnancy are positively associated with the development of congenital heart disease in offspring. The combination of HCY and Cys C was the most efficient test for the diagnosis of CHD. We are the first to report that plasma Cys C levels of women pregnant with fetuses with CHD were higher than those of women pregnant with normal fetuses.

Objective: This study aimed to explore genetic etiologies of conotruncal defects (CTDs) in fetuses by analyzing the results of different genetic tests and to assess pregnancy outcomes of fetuses with CTD in a Chinese prenatal cohort.Methods: A total of 146 fetuses that underwent invasive prenatal genetic testing for CTD at the prenatal diagnosis center of the International Peace Maternity and Child Health Hospital between January 2018 and December 2022 were retrospectively analyzed.All of them underwent chromosomal microarray analysis (CMA) and karyotype analysis, but only 27 underwent whole‐exome sequencing (WES). The results of these different genetic testing methods were collected and compared. Data on pregnancy outcomes and neonate prognosis were obtained from the electronic medical records and postpartum telephone follow‐up.Results: Among the 146 fetuses with CTD, the chromosome abnormality rate was 22.6% (33/146), and the detection rate of abnormal chromosomes in CMA and karyotype was 15.8% (23/146). Among the 27 pregnant women who accepted WES, five had pathogenic variants. The detection rate of abnormal chromosomes by CMA was higher in infants with an interrupted aortic arch (IAA) than in the other infant groups (60.0%, χ² = 11.661, P = 0.045, c²‐test or Fisher's exact test).Conclusion: Congenital heart disease is a complex congenital heart disease with an etiology closely related to genetic abnormalities. Therefore, invasive genetic testing and CMA are recommended as first‐line tests for all fetuses with CTD detected by prenatal ultrasound, especially IAA. WES should be recommended when necessary.

Objectives: Timely identification of endometrial nonbenign lesions led to improved outcomes, but there was a lack of effective predictive models for asymptomatic endometrial thickening. The aim of this study was to develop a strong machine learning (ML) model for assessing the risk of endometrial malignancy in asymptomatic patients after menopause. Methods: This retrospective study was designed to collect data from 971 postmenopausal asymptomatic women with endometrial thickening. The bootstrap resampling method was used for model training, internal validation, and external validation. With 41 easily accessible characteristics, multifactor regression and least absolute shrinkage and selection operator regression were performed for feature selection. Nine ML algorithms were applied to build a model. To explain the final model and rank feature importance, the SHapley Additive exPlanation (SHAP) method was utilized. Meanwhile, a nomogram was developed to facilitate model interpretation. Results: The comprehensive methodologies identified parity, Doppler flow signals, endometrial thickness, cancer antigen 125, and D-dimer as significant predictors. The logistic regression (LR) model demonstrated superior performance compared with other ML algorithms, achieving an accuracy of 88%, a sensitivity of 78%, a specificity of 98%, and an area under the receiver operating characteristic curve of 0.81. Furthermore, individualized predictions of endometrial malignancy were visualized through a force plot generated by SHAP analysis. A nomogram based on the LR model was subsequently constructed, showing area under the receiver operating characteristic curve values of 0.82, 0.82, and 0.81 for the training, internal validation, and external validation cohorts, respectively. The calibration curve demonstrated excellent consistency. Conclusions: We developed an LR-based nomogram model and interpreted using the SHAP method, which provided visual insights for detecting endometrial nonbenign lesions in asymptomatic postmenopausal women. This approach would aid clinicians in providing individualized treatment and help avoid unnecessary invasive surgeries.