Validation of proposed DSM-5 criteria for autism spectrum disorder

Abstract

Objective: The primary aim of the present study was to evaluate the validity of proposed DSM-5 criteria for autism spectrum disorder (ASD).

Method: We analyzed symptoms from 14,744 siblings (8,911 ASD and 5,863 non-ASD) included in a national registry, the Interactive Autism Network. Youth 2 through 18 years of age were included if at least one child in the family was diagnosed with ASD. Caregivers reported symptoms using the Social Responsiveness Scale and the Social Communication Questionnaire. The structure of autism symptoms was examined using latent variable models that included categories, dimensions, or hybrid models specifying categories and subdimensions. Diagnostic efficiency statistics evaluated the proposed DSM-5 algorithm in identifying ASD.

Results: A hybrid model that included both a category (ASD versus non-ASD) and two symptom dimensions (social communication/interaction and restricted/repetitive behaviors) was more parsimonious than all other models and replicated across measures and subsamples. Empirical classifications from this hybrid model closely mirrored clinical ASD diagnoses (90% overlap), implying a broad ASD category distinct from non-ASD. DSM-5 criteria had superior specificity relative to DSM-IV-TR criteria (0.97 versus 0.86); however sensitivity was lower (0.81 versus 0.95). Relaxing DSM-5 criteria by requiring one less symptom criterion increased sensitivity (0.93 versus 0.81), with minimal reduction in specificity (0.95 versus 0.97).

Conclusions: Results supported the validity of proposed DSM-5 criteria for ASD as provided in Phase I Field Trials criteria. Increased specificity of DSM-5 relative to DSM-IV-TR may reduce false positive diagnoses, a particularly relevant consideration for low base rate clinical settings. Phase II testing of DSM-5 should consider a relaxed algorithm, without which as many as 12% of ASD-affected individuals, particularly females, will be missed. Relaxed DSM-5 criteria may improve identification of ASD, decreasing societal costs through appropriate early diagnosis and maximizing intervention resources.

Figure 1

Two-factor/two-class model using the Social Responsiveness Scale in the total available sample (N=6,949) and non-autism spectrum disorder (ASD) sibling sub-sample (N=2,701). Note: The ASD class had substantially higher symptom levels (t-scores) than the non-ASD class in both the total sample and non-ASD sibling sub-sample (p<.001). Non-ASD sibs designate model results for the non-ASD sibling sub-sample. SC 1–4=social communication/interaction scales from the Social Responsiveness Scale. AM 1–4=autism mannerisms item packets from the Social Responsiveness Scale. Horizontal black line separates results for the empirically-derived ASD and non-ASD classes. Curved double-headed arrows represent factor correlations in the ASD and non-ASD classes.

Figure 2

Social Responsiveness Scale (SRS) social and autism mannerism T-scores cluster within autism spectrum disorder (ASD) and non-ASD classes in the total available sample (N=6,949; left panel) and non-ASD sibling sub-sample (N=2,701; right panel). Note: SRS social T-scores are the average of the four social sub-scales. Higher scores indicate greater impairment. Each figure is based upon a random sub-sample (n=500).

Figure 3

Age of reported developmental milestones (M +/− 95% CI) for non-autism spectrum disorder (ASD) siblings empirically classified into the autism spectrum disorder and non-autism spectrum disorder classes.