Signposting for diagnosis of Autism Spectrum Disorder using the Diagnostic Interview for Social and Communication Disorders (DISCO). (January 2015)

• Record Type: Journal Article
• Authors: Carrington, Sarah; Leekam, Susan; Kent, Rachel; Maljaars, Jarymke; Gould, Judith; Wing, Lorna; Le Couteur, Ann; Van Berckelaer-Onnes, Ina; Noens, Ilse
• Is Part Of: Research in autism spectrum disorders. Volume 9(2015:Jan.)
• Page Start: 45
• Page End: 52
• Publication Date: 2015-01
• Subjects: Autism Spectrum Disorder -- Diagnosis -- DSM-5 -- ICD-10 -- Screening; Autism spectrum disorders -- Periodicals; 616.85882005
• DOI: 10.1016/j.rasd.2014.10.003
• Languages: English
• ISSNs: 1750-9467

Abstract

Highlights: A set of 14, mostly social-communication items has been identified from the DISCO . An algorithm applied to this set discriminated ASD from non-ASD cases. Excellent agreement with outcome on DISCO algorithms for both ICD-10 and DSM-5. Good sensitivity for children, adolescents, and adults. An interview based on these items could highlight cases for full diagnostic assessment. Abstract: Recent research has investigated the capability of the Diagnostic and Statistical Manual for Mental Disorders ( DSM-5 ) descriptions to identify individuals who should receive a diagnosis of Autism Spectrum Disorder (ASD) using standardised diagnostic instruments. Building on previous research investigating behaviours essential for the diagnosis of DSM-5 ASD, the current study investigated the sensitivity and specificity of a set of 14 items derived from the Diagnostic Interview for Social and Communication Disorders ( DISCO Signposting set) that have potential for signposting the diagnosis of autism according to both the new DSM-5 criteria for ASD and ICD-10 criteria for Childhood Autism. An algorithm threshold for the Signposting set was calculated in Sample 1 ( n = 67), tested in an independent validation sample (Sample 2; n = 78), and applied across age and ability sub-groups in Sample 3 ( n = 190). The algorithm had excellent predictive validity according to best estimate clinical diagnosis (Samples 1 and 2) and excellent agreement with established algorithms for both DSM-5 and ICD-10 (all samples). The signposting set has potential to inform our understanding of the profile of ASD in relation to other neurodevelopmental disorders and to form the basis of a Signposting Interview for use in clinical practice.