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.