Is the Combination of ADOS and ADI-R Necessary to Classify ASD? Rethinking the “Gold Standard” in Diagnosing ASD

• Published in: Frontiers in psychiatry Vol. 12; p. 727308
• Main Authors: Kamp-Becker, Inge, Tauscher, Johannes, Wolff, Nicole, Küpper, Charlotte, Poustka, Luise, Roepke, Stefan, Roessner, Veit, Heider, Dominik, Stroth, Sanna
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 24.08.2021
• Subjects: ADOS; autism spectrum disorder; clinical characteristics; differential diagnosis behavioral aspects; machine learning; Psychiatry; random forest; ADI-R; ADOS; random forest; autism spectrum disorder; differential diagnosis behavioral aspects; machine learning; clinical characteristics; Goldstandard
• DOI: 10.3389/fpsyt.2021.727308
• ISSN: 1664-0640

Diagnosing autism spectrum disorder (ASD) requires extensive clinical expertise and training as well as a focus on differential diagnoses. The diagnostic process is particularly complex given symptom overlap with other mental disorders and high rates of co-occurring physical and mental health concerns. The aim of this study was to conduct a data-driven selection of the most relevant diagnostic information collected from a behavior observation and an anamnestic interview in two clinical samples of children/younger adolescents and adolescents/adults with suspected ASD. Via random forests, the present study discovered patterns of symptoms in the diagnostic data of 2310 participants (46% ASD, 54% non-ASD, age range 4–72 years) using data from the combined Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview—Revised (ADI-R) and ADOS data alone. Classifiers built on reduced subsets of diagnostic features yield satisfactory sensitivity and specificity values. For adolescents/adults specificity values were lower compared to those for children/younger adolescents. The models including ADOS and ADI-R data were mainly built on ADOS items and in the adolescent/adult sample the classifier including only ADOS items performed even better than the classifier including information from both instruments. Results suggest that reduced subsets of ADOS and ADI-R items may suffice to effectively differentiate ASD from other mental disorders. The imbalance of ADOS and ADI-R items included in the models leads to the assumption that, particularly in adolescents and adults, the ADI-R may play a lesser role than current behavior observations.