Replication study for ADOS‐2 cut‐offs to assist evaluation of autism spectrum disorder

Abstract The Autism Diagnostic Observation Schedule, Second Edition (ADOS‐2) has been widely used for ASD assessment. While prior studies investigated sensitivity and specificity of ADOS‐2 Modules 1–3, there has been limited research addressing algorithm cut‐off scores to optimize ADOS‐2 classification. The goal of this study was to assess algorithm cut‐off scores for diagnosing ASD with Modules 1–3, and to evaluate alignment of the ADOS‐2 classification with the best estimate clinical diagnosis. Participants included 3144 children aged 31 months or older who received ADOS‐2 Modules 1–3, as well as the best estimate clinical diagnosis. Five classification statistics were reported for each module: sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (i.e., Receiver Operator Classification Statistic), and these statistics were calculated for the optimal cut‐off score. Frequency tables were used to compare ADOS‐2 classification and the best estimate clinical diagnosis. Half of the sample received Module 3, 21% received Module 2, and 29% received Module 1. The overall prevalence of ASD was 60%; the male‐to‐female ratio was 4:1, and half of the sample was non‐White. Across all modules, the autism spectrum cut‐off score from the ADOS‐2 manual resulted in high sensitivity (95%+) and low specificity (63%–73%). The autism cut‐off score resulted in better specificity (76%–86%) with favorable sensitivity (81%–94%). The optimal cut‐off scores for all modules based on the current sample were within the autism spectrum classification range except Module 2 Algorithm 2. In the No ASD group, 29% had false positives (ADOS‐2 autism spectrum classification or autism classification). The ADOS‐2 autism spectrum classification did not indicate directionality for diagnostic outcome (ASD 56% vs. No ASD 44%). While cut‐off scores of ADOS‐2 Modules 1–3 in the manual yielded good clinical utility in ASD assessment, false positives and low predictability of the autism spectrum classification remain challenging for clinicians.