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

Inge Kamp-Becker; Johannes Tauscher; Nicole Wolff; Charlotte Küpper; Luise Poustka; Stefan Roepke; Veit Roessner; Dominik Heider; Sanna Stroth

doi:10.3389/fpsyt.2021.727308

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

Front Psychiatry. 2021 Aug 24:12:727308. doi: 10.3389/fpsyt.2021.727308. eCollection 2021.

Authors

Inge Kamp-Becker¹, Johannes Tauscher², Nicole Wolff³, Charlotte Küpper⁴, Luise Poustka⁵, Stefan Roepke⁴, Veit Roessner³, Dominik Heider², Sanna Stroth¹

Affiliations

¹ Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Philipps University, Marburg, Germany.
² Department of Mathematics and Computer Science, Philipps University Marburg, Marburg, Germany.
³ Department of Child and Adolescent Psychiatry and Psychotherapy, Faculty of Medicine of the Technische Universität Dresden, Dresden, Germany.
⁴ Department of Psychiatry, Charité - Universitätsmedizin Berlin, Berlin, Germany.
⁵ Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany.

Abstract

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.

Keywords: ADI-R; ADOS; Goldstandard; autism spectrum disorder; clinical characteristics; differential diagnosis behavioral aspects; machine learning; random forest.