Developing a Diagnostic Algorithm for the Music-Based Scale for Autism Diagnostics (MUSAD) Assessing Adults with...

Area of Science:

• Neurodevelopmental disorders
• Clinical psychology
• Psychometrics

Background:

• Autism spectrum disorder (ASD) diagnosis in adults with intellectual disability (ID) presents unique challenges.
• The Music-based Scale for Autism Diagnostics (MUSAD) is a DSM-5-based observational tool developed to aid in ASD identification.
• Previous research indicated MUSAD's potential, necessitating further validation in diverse clinical populations.

Purpose of the Study:

• To evaluate the clinical utility and diagnostic accuracy of the MUSAD in a new sample of adults with ID.
• To assess the inter-rater reliability and convergent validity of the MUSAD.
• To confirm MUSAD's effectiveness as a diagnostic aid for ASD in adults with ID.

Main Methods:

• A sample of 124 adults with ID was assessed, with 60.5% diagnosed with ASD.
• A diagnostic algorithm was derived from MUSAD scores to differentiate between individuals with and without ASD.
• Inter-rater reliability was examined using consensus ratings from four independent experts.
• Convergent validity was assessed by correlating MUSAD scores with other established ASD measures.

Main Results:

• The derived MUSAD algorithm demonstrated good diagnostic performance: sensitivity of 79%, specificity of 74%, and an area under the curve of 0.81.
• Inter-rater reliability for MUSAD scoring was excellent, with an intraclass correlation coefficient (ICC) of 0.92.
• Substantial correlations were found between MUSAD scores and other ASD-specific measures, supporting convergent validity.

Conclusions:

• The MUSAD provides accurate and reliable scores for identifying ASD in adults with intellectual disability.
• MUSAD is a valuable tool supporting comprehensive diagnostic assessments for ASD in this population.
• Further implementation of MUSAD can enhance diagnostic precision and clinical decision-making for adults with ID and ASD.