Optimizing Accuracy of Autism Diagnostic Observation Schedule-2 in Very Young Children With Modifying the Effect of...

The current study investigated 1144 toddlers and preschoolers (ASD + GDD n = 592; ASD only n = 249; GDD only n = 89; no ASD or GDD n = 214) with the toddler module (38.6%), Module 1 (57.5%), and Module 2 (3.9%) as well as Mullen Scales of Early Learning. The calibrated severity score (CSS) was used to compare severity across modules. The study sample was stratified by GDD (Visual Reception Developmental Quotient < 75), and each stratum was investigated with descriptive statistics, ROC curves, and test statistics to identify the optimal cut-off CSS to differentiate ASD and non-ASD. ROC analysis indicated that the CSS scores showed excellent discrimination for ASD status for both the GDD (AUC = 0.86) and no GDD (AUC = 0.95) strata. In the no-GDD stratum, an ADOS-2 CSS of 5 was determined to be the optimal cut-off. In the GDD stratum, an ADOS-2 CSS of 6 was determined to be the optimal cutoff. While non-spectrum/little-to-no concern and autism/moderate-to-severe concern showed very high predictive accuracy for diagnostic outcomes, the autism spectrum/mild-to-moderate concern lacked clear diagnostic directionality, regardless of GDD status. This is the first study with a large sample of toddlers and preschoolers exploring optimal ADOS-2 CSS cut-off when stratified by GDD.