A clinical prediction model to estimate the risk for coarctation of the aorta: From fetal to newborn life

AIM: A prenatal diagnosis of coarctation of the aorta (CoA) is challenging. This study aimed to develop a coarctation probability model incorporating prenatal cardiac sonographic markers to estimate the probability of an antenatal diagnosis of CoA. METHODS: We reviewed 89 fetuses as an investigation cohort with prenatal suspicion for CoA and categorized them into three subgroups: severe CoA: symptomatic CoA and surgery within the first 3 months; mild CoA: surgery within 4 months to 1 year (29); and false‐positive CoA: not requiring surgery (45). Logistic regression was used to create a multiparametric model, and a validation cohort of 86 fetuses with suspected CoA was used to validate the model. RESULTS: The prediction model had an optimal criterion >0.25 (sensitivity of 97.7%; specificity of 59.1%), and the area under the receiver operator curve was 0.85. The parameters and their cut‐off values were as follows: left common carotid artery to left subclavian artery distance/distal transverse arch (LCCA‐LSCA)/DT Index >1.77 (sensitivity 62%, specificity 88%, 95% confidence interval [CI]: 0.6–0.8), and z‐score of AAo peak Doppler > −1.7 (sensitivity 77%, specificity 56%, 95% CI: 0.6–0.8). The risk assessment demonstrated that fetuses with a model probability >60% should have inpatient observation for a high risk of CoA, whereas fetuses with a model probability <15% should not undergo clinical follow‐up. CONCLUSION: The probability model performs well in predicting CoA outcomes postnatally and can also improve the accuracy of risk assessment. The objectivity of its parameters may allow its implementation in multicenter studies of fetal cardiology.