DSM‐5 based algorithms for the Autism Diagnostic Interview‐Revised for children ages 4–17 years

• Published in: Journal of child psychology and psychiatry Vol. 66; no. 9; pp. 1403 - 1413
• Main Authors: Lampinen, Linnea A., Zheng, Shuting, Olson, Lindsay, Bal, Vanessa H., Thurm, Audrey E., Esler, Amy N., Kanne, Stephen M., Kim, So Hyun, Lord, Catherine, Parenteau, China, Nowell, Kerri P., Roberts, Jane E., Takahashi, Nicole, Bishop, Somer L.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.09.2025; John Wiley and Sons Inc
• Subjects: Adequacy; ADI‐R; Adolescent; Algorithms; Autism; autism spectrum disorder; Autism Spectrum Disorder - diagnosis; Autism Spectrum Disorders; Autistic children; Caregivers; Child; Child, Preschool; Clinical Diagnosis; Clinical research; Confirmatory factor analysis; Developmental disorders; diagnosis; Diagnostic and Statistical Manual of Mental Disorders; diagnostic instruments; Factor Analysis; Factor Structure; Female; Humans; Interview, Psychological - methods; Interview, Psychological - standards; Interviews; Male; Medical diagnosis; Original; Psychiatric Status Rating Scales - standards; sensitivity; Sensitivity and Specificity; specificity; Speech; Toddlers; Autism; specificity; diagnosis; diagnostic instruments; sensitivity; autism spectrum disorder; ADI‐R
• ISSN: 0021-9630; 1469-7610; 1469-7610
• DOI: 10.1111/jcpp.14159

Background The Autism Diagnostic Interview, Revised (ADI‐R) is a caregiver interview that is widely used as part of the diagnostic assessment for Autism Spectrum Disorder (ASD). Few large‐scale studies have reported the sensitivity and specificity of the ADI‐R algorithms, which are based on DSM‐IV Autistic Disorder criteria. Kim and Lord (Journal of Autism and Developmental Disorders, 2012, 42, 82) developed revised DSM‐5‐based toddler algorithms, which are only applicable to children under 4 years. The current study developed DSM‐5‐based algorithms for children ages 4–17 years and examined their performance compared to clinical diagnosis and to the original DSM‐IV‐based algorithms. Methods Participants included 2,905 cases (2,144 ASD, 761 non‐ASD) from clinical‐research databanks. Children were clinically referred for ASD‐related concerns or recruited for ASD‐focused research projects, and their caregivers completed the ADI‐R as part of a comprehensive diagnostic assessment. Items relevant to DSM‐5 ASD criteria were selected for the new algorithms primarily based on their ability to discriminate ASD from non‐ASD cases. Algorithms were created for individuals with and without reported use of phrase speech. Confirmatory factor analysis tested the fit of a DSM‐5‐based two‐factor structure. ROC curve analyses examined the diagnostic accuracy of the revised algorithms compared to clinical diagnosis. Results The two‐factor structure of the revised ADI‐R algorithms showed adequate fit. Sensitivity of the original ADI‐R algorithm ranged from 74% to 96%, and specificity ranged from 38% to 83%. The revised DSM‐5‐based algorithms performed similarly or better, with sensitivity ranging from 77% to 99% and specificity ranging from 71% to 92%. Conclusions In this large sample aggregated from US clinical‐research sites, the original ADI‐R algorithm showed adequate diagnostic validity, with poorer specificity among individuals without phrase speech. The revised DSM‐5‐based algorithms introduced here performed comparably to the original algorithms, with improved specificity in individuals without phrase speech. These revised algorithms offer an alternative method for summarizing ASD symptoms in a DSM‐5‐compatible manner.