Left Ventricular Strain, Arch Angulation, and Velocity-Time Integral Ratio Improve Performance of a Clinical Pathway for Fetal Diagnosis of Neonatal Coarctation of the Aorta.

INTRODUCTION

Neonatal presentation of coarctation of the aorta (CoA) is a potentially life-threatening condition that is difficult to diagnose in fetal life. We therefore sought to validate and compare novel metrics that may add diagnostic value for fetal CoA, including the diastolic to systolic aortic isthmus VTI ratio (VTId:VTIs), ascending aorta to descending aorta angle (AAo-DAo), transverse aorta to descending aorta angle (TAo-DAo), and LV longitudinal strain (LVS), then to evaluate whether these novel metrics improve specificity to identify fetuses at the highest risk for postnatal CoA without compromising sensitivity.

METHODS

Retrospective cohort study of fetuses followed a prospective clinical pathway and previously classified as mild, moderate, or high-risk for CoA based on standard fetal echo metrics. Novel metrics were retrospectively measured in a blinded manner.

RESULTS

Among fetuses with prenatal concern for CoA, VTId:VTIs, AAo-DAo angle, TAo-DAo angle, and LVS were significantly different between surgical and non-surgical cases (p < 0.01 for all variables). In the subgroup of moderate- and high-risk fetuses, the standard high-risk criteria (flow reversal at the foramen ovale or aortic arch) did not discriminate effectively between surgical and non-surgical cases. VTId:VTIs, AAo-Dao angle, Tao-DAo angle, and LVS all demonstrated greater discrimination than standard high-risk criteria, with specificity of 100% and PPV (positive predictive value) of 78-100%.

CONCLUSIONS

The incorporation of novel metrics added diagnostic value to our clinical pathway for fetal CoA with higher specificity than the previous high-risk criteria. The incorporation of these metrics into the evaluation of fetuses at moderate- or high-risk for surgical CoA may improve prenatal counseling, allow for more consistent surgical planning, and ultimately optimize hospital resource allocation.