Development and validation of a measure of dysmorphology: Useful for autism subgroup classification

• Published in: American journal of medical genetics. Part A 2008-05, Vol.146A (9), p.1101-1116
• Main Authors: Miles, Judith H., Takahashi, T. Nicole, Hong, Julie, Munden, Nicole, Flournoy, Nancy, Braddock, Stephen R., Martin, Rick A., Spence, M. Anne, Hillman, Richard E., Farmer, Janet E.
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Algorithms; autism; Autistic Disorder - classification; Autistic Disorder - genetics; Autistic Disorder - pathology; Biological and medical sciences; CART; Child; Child clinical studies; Child, Preschool; complex autism; Congenital Abnormalities - classification; Congenital Abnormalities - genetics; Congenital Abnormalities - pathology; congenital anomalies; Developmental disorders; dysmorphology; Female; Head; Humans; Infant; Infantile autism; Male; Medical genetics; Medical sciences; Middle Aged; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; screening; Somatotypes
• ISSN: 1552-4825; 1552-4833
• DOI: 10.1002/ajmg.a.32244

Autism spectrum disorders (ASD) comprise a class of neurodevelopmental disorders that can originate from a variety of genetic and environmental causes. To delineate autism's heterogeneity we have looked for biologically‐based phenotypes found in consistent proportions of ASD individuals. One informative phenotype is that of generalized dysmorphology, based on whole body examinations by medical geneticists trained in the nuances of anomalous embryologic development. We identified a need for a dysmorphology measure that could be completed by medical clinicians not extensively trained in dysmorphology that would still retain the level of sensitivity and specificity of the comprehensive dysmorphology examination. Based on expert‐derived consensus dysmorphology designation of 222 autism patients and a classification validation study of 30 subjects by four dysmorphologists, we determined that dysmorphology designations based on body areas provided superior inter‐rater reliability. Using 34 body area designations, we performed a classification and regression tree (CART) analysis to construct a scoring algorithm. Compared to the consensus classification, the model performed with 81% sensitivity and 99% specificity, and classification of a replication dataset of 31 ASD individuals performed well, with 82% sensitivity and 95% specificity. The autism dysmorphology measure (ADM) directs the clinician to score 12 body areas sequentially to arrive at a determination of “dysmorphic” or “nondysmorphic.” We anticipate the ADM will permit clinicians to differentiate accurately between dysmorphic and nondysmorphic individuals—allowing better diagnostic classification, prognostication, recurrence risk assessment, and laboratory analysis decisions—and research scientists to better define more homogeneous autism subtypes. © 2008 Wiley‐Liss, Inc.