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

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.

The prevalence of autism spectrum disorder traits and diagnosis in adults and young people with personality disorders: A systematic review

“…Despite a number of participants scoring above threshold scores for current ASD traits assessed by the ADOS instrument, these were considered ‘false positive’ scores due to the ADI-R (which assesses developmental symptoms) being used to confirm diagnosis in the study. Retrospective assessment using tools such as the ADI-R may feature recall bias and less reliably detect ASD in adult populations (Fusar-Poli et al, 2017; Kamp-Becker et al, 2021). Finally, there was inconsistent reporting of subdomain trait scores, which may be important given the fractionable-triad model of autism (Happé and Ronald, 2008).…”

Validation of the social communication questionnaire amongst Nigerian adolescents

“…However, resources did not allow for the ADI‐R or DISCO, so the ADOS‐2 was used alone as the gold standard. Utilizing the ADOS‐2 as a stand‐alone measure is supported in the literature (Kamp‐Becker et al, 2021). Additionally, no diagnosis was given during this study.…”

Blockwise Robust Singular Value Decomposition for MRI Brain Image Denoising

“…MRI scan produces different slices of an image. However, MRI scan image often contain outliers [1] and the presence of noise/outliers hampers the disease diagnosis [2]. Therefore, image preprocessing as well as image filtering can help minimize the effects of these degradations [3].…”