An algorithm for predicting Robin sequence from fetal MRI

Background: Infants with Robin sequence (RS) may present with airway compromise at delivery. Prenatal diagnosis would improve preparation and postnatal care. The purpose of this study was to devise a predictive algorithm for RS based on fetal magnetic resonance imaging (MRI). Methods: Retrospective case-control study including fetal MRIs from 2002 to 2017. Inclusion criteria were (1) MRI of adequate quality, (2) live-born infant, and (3) postnatal evaluation. Subjects were grouped on the basis of postnatal diagnosis: (1) RS (micrognathia, glossoptosis, airway obstruction), (2) micrognathia without airway obstruction ("micrognathia"), (3) cleft lip and palate ("CLP"), and (4) gestational age-matched controls. A series of possible predictive variables were assessed on MRI. Receiver operator curves were applied to identify cut-off values, and a multivariable algorithm was developed. Results: A total of 162 subjects with mean gestational age at MRI of 25.6 ± 4.9 weeks were included: RS, n = 27 (17%); micrognathia, n = 35 (22%); CLP, n = 46 (28%); control, n = 54 (33%). Three variables were independent predictors of RS: (1) Veau I/II cleft palate (OR = 38.8), (2) tongue shape index (>80%; OR = 8.7), and (3) inferior facial angle (80% and IFA