An algorithm for predicting Robin sequence from fetal MRI

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).

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.

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 (<48°; OR = 14.5).

MRI findings of cleft palate, TSI >80% and IFA <48° indicate a 98% probability of RS, whereas a lack of all 3 features denotes a likelihood of 1%.