Quantitative Parameters Analysis for Prenatally Echocardiographic Diagnosis of Atrioventricular Septal Defects

Background:Atrioventricular septal defects(AVSDs)are screened and diagnosed usually rely on the imaging characteristics of fetal echocardiography(FE).However,diagnosis on images is heavily depended on sonographers’experience and the quantitative data are rarely studied.Objective:This study aimed to realize the prenatal diagnosis of AVSDs by analyzing the quantitative data on FE.Methods:One hundred and thirteen cardiac quantitative data was analyzed in 370 normal and 49 AVSDs fetuses retrospectively.The top six with the highest diagnostic accuracy rate were acquired according to the area under the curve(AUC),and the diagnostic value of six variables was analyzed.Results:Six parameters obtained on the four-chamber view(4CHV),including the atrial to ventricular length ratio in end-diastole(AVLR-ED),AVLR-ED combined with the atrial to ventricular length ratio in end-systole(AVLR-ES),quantile score(Q score)of AVLR-ED,Q score of AVLR-ES,Q score of ventricle length in end-diastole(VL-ED),and AVLR-ES,were the top six with the highest diagnostic value,and the AUC was 0.99(95%CI 0.99–1.00),0.99(95%CI 0.99–1.00),0.99(95%CI 0.98–1.00),0.95(95%CI 0.91–0.99),0.93(95%CI 0.87–0.99),and 0.91(95%CI 0.83–1.00),respectively.And within the 20%false positive rate,the diagnostic sensitivity was greater than 100%,100%,100%,90%,90%,and 88%,respectively.Conclusions:Six variables could be used for prenatal diagnosis of AVSDs.Among them,AVLR-ED and Q score of AVLR-ED,obtained on the 4CHV,were more convenient to acquire and had higher diagnostic accuracy.

Noninherited Factors in Fetal Congenital Heart Diseases Based on Bayesian Network:A Large Multicenter Study

Background:Current studies have confirmed that fetal congenital heart diseases(CHDs)are caused by various factors.However,the quantitative risk of CHD is not clear given the combined effects of multiple factors.Objective:This cross-sectional study aimed to detect associated factors of fetal CHD using a Bayesian network in a large sample and quantitatively analyze relative risk ratios(RRs).Methods:Pregnant women who underwent fetal echocardiography(N=16,086 including 3,312 with CHD fetuses)were analyzed.Twenty-six maternal and fetal factors were obtained.A Bayesian network is constructed based on all variables through structural learning and parameter learning methods to find the environmental factors that directly and indirectly associated with outcome,and the probability of fetal CHD in the two groups is predicted through a junction tree reasoning algorithm,so as to obtain RR for fetal CHD under different exposure factor combinations.Taking into account the effect of gestational week on the accuracy of model prediction,we conducted sensitivity analysis on gestational week groups.Results:The single-factor analysis showed that the RRs for the numbers of births,spontaneous abortions,and parental smoking were 1.50,1.38,and 1.11(P<0.001),respectively.The risk gradually increased with the synergistic effect of ranging from one to more environmental factors above.The risk was higher among subjects with five synergistic factors,including the number of births,upper respiratory tract infection during early pregnancy,anemia,and mental stress as well as a history of spontaneous abortions or parental smoking,than in those with less than 5 factors(RR=2.62 or 2.28,P<0.001).This result was consistent across the participants grouped by GWs.Conclusion:We identified six factors that were directly associated with fetal CHD.A higher number of these factors led to a higher risk of CHD.These findings suggest that it is important to strengthen healthcare and prenatal counseling for women with these factors.