Objective To explore the clinical characteristics of missed diagnosis of fetal cardiac great vessel anomalies by ultrasound and to analyze the possible risk factors for missed diagnosis.Methods A total of 15 462 pregnant women who un-derwent prenatal fetal cardiac screening in our hospital from May 2013 to May 2023 were enrolled,including 386 true positive fe-tuses and 58 false negative fetuses,with a missed diagnosis rate of 13.06%.Among them,444 patients were randomly assigned in a 4∶ 1 ratio,with 352 assigned to the training set and 92 to the validation set.The training set was divided into a true positive group(n =308)and a false negative group(n =44).Additionally,60 normal fetuses were selected as the control group,and 183 true positive fetuses were included in the observation group.Multivariate logistic regression analysis and risk ratio modeling were used to analyze the independent risk factors affecting the false negative results of fetal cardiac great vessel anomaly by ultra-sound diagnosis.The consistency index was calculated and a predictive model was constructed.The performance of a model was evaluated using receiver operating characteristic(ROC)curves,cross-validation and Nagelkerke R2 index.Results The ab-sence of electrocardiographic monitoring,early pregnancy blood flow tracking,blood flow spectral monitoring,and non-4CV+LVOT+RVOT+3VV+3VT cardiac image slice type were identified as independent risk factors affecting false negative results of fetal cardiac great vessel anomaly by ultrasound diagnosis(P＜0.05,P＜0.01).The predictive model constructed using these four indicators had the highest C index in the training set and the validation set,with values of 0.792 and 0.789,respectively.The results of the model evaluation showed the area under the ROC curve(AUC)was 0.847(95%CI 0.812,0.879,P＜0.05)in the training set and 0.857(95%CI 0.832,0.891,P＜0.05)in the validation set,indicating good discrimination of the model.The results of the cross-validation experiment showed that the parameters for both the training and validation sets were very close,indicating a high stability of the model.The Nagelkerke R2 value was 0.627,indicating a goodness of fit and strong predictive capability of the model.Conclusion The absence of electrocardiographic monitoring,early pregnancy blood flow tracking,blood flow spectral monitoring,and non-4CV+LVOT+RVOT+3VV+3VT cardiac image slice type are independent risk factors affecting false negative results of fetal cardiac great vessel anomaly by ultrasound diagnosis.The predictive model constructed using these four indicators,cardiac slice type,early pregnancy blood flow tracking,blood flow spectral monitoring,and electrocardiogram monitoring,has good discrimination and accuracy.
维普
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