The APERT Severity Scale: A Quantitative Tool for Risk Stratification in Apert Syndrome.

Hu, Allison C; Tolley, Philip D; Han, Nicholas A; Ryan, Isabel A; Liao, Eric C; Swanson, Jordan W; Bartlett, Scott P; Taylor, Jesse A · Plast Reconstr Surg · 2025

retrospective_cohort · Level III

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Abstract

Apert syndrome is a rare congenital disorder caused by FGFR2 mutations, characterized by craniosynostosis, midface hypoplasia, and syndactyly. The condition's multisystem complexity and lack of standardized surgical guidelines complicate diagnosis and treatment. This study introduces a quantitative severity scale for Apert syndrome to support objective risk stratification and personalized care. We retrospectively reviewed children with Apert syndrome treated from 1988-2024. Inclusion criteria required age ≥11 years at last follow-up. A five-domain severity scale based on literature review and clinical data was developed. Scoring was performed using preoperative clinical photographs and documentation. Of 65 identified patients, 37 (56.9%) met inclusion criteria (48.6% male). Mean follow-up age was 17.0±5.0 years, with an average of 4.4±2.2 craniofacial surgeries. Patients were scored 1 (least severe) to 3 (most severe) in five domains: Airway compromise, Pressure (intracranial), Eyes, Retrusion of midface, and Turribrachycephaly (APERT), with total scores from 5-15. Severity categories were defined as mild (5-7), moderate (8-11), and severe (12-15). Fifteen patients (40.5%) were mild, 20 (54.1%) moderate, and 2 (5.4%) severe, with a mean score of 8.1±1.8. Higher APERT scores correlated with increased surgical frequency (r=0.851, p<0.001). Subscores in airway, pressure, eyes, and skull shape were also predictive. A score ≥9 predicted ≥6 surgeries with 100% sensitivity and 86.7% specificity. The APERT score is the first quantitative classification of Apert syndrome severity, correlating with phenotypic burden and surgical volume. It may improve clinical communication, institutional comparisons, and risk-based treatment planning.