Data-driven characterization of individuals with delayed autism diagnosis

Abstract Importance Despite tremendous improvement in early identification of autism, ∼25% of children receive their diagnosis after the age of six. Since evidence-based practices are more effective when started early, delayed diagnosis prevents many children from receiving optimal support. Objective To identify and comparatively characterize groups of individuals diagnosed with Autism Spectrum Disorder (ASD) after the age of six. Design This cross-sectional study used various machine learning approaches to classify, characterize, and compare individuals from the Simons Foundation Powering Autism Research for Knowledge (SPARK) cohort, recruited between 2015-2020. Setting Analyses of medical histories and behavioral instruments. Participants 23,632 SPARK participants. Exposure ASD diagnosis upon registration to SPARK. Main Outcomes and Measures Clusters of individuals diagnosed after the age of six ( delayed ASD diagnosis ) and their defining characteristics, as compared to individuals diagnosed before the age of six ( timely ASD diagnosis ). Odds and mean ratios were used for feature comparisons. Shapley values were used to assess the predictive value of these features, and correlation-based cliques were used to understand their interconnectedness. Results Two robust subgroups of individuals with delayed ASD diagnosis were detected. The first, D1 , included 3,612 individuals with lower support needs as compared to 17,992 individuals with a timely diagnosis. The second subgroup, D2 , included 2,028 individuals with higher support needs, as consistently reflected by all commonly-used behavioral instruments, the greatest being repetitive and restrictive behaviors measured by the Repetitive Behavior Scale – Revised (RBS-R; D1: MR = 0.6854, 95% CI = 0.6848 – 0.686; D2: MR = 1.4223, 95% CI = 1.4210-1.4238, P = 3.54 × 10 −134 ). Moreover, individuals belonging to D1 had fewer comorbidities as compared to individuals with a timely ASD diagnosis, while D2 individuals had more (D1: mean = 3.47, t = 15.21; D2: mean = 8.12, t = 48.26, p< 2.23 × 10 −308 ). A Random Forest classifier trained on the groups’ characteristics achieved an AUC of 0.94. Further connectivity analysis of the groups’ most informative characteristics demonstrated their distinct topological differences. Conclusions and Relevance This analysis identified two opposite groups of individuals with delayed ASD diagnosis, thereby providing valuable insights for the development of targeted diagnostic strategies. Key Points Question Are there specific subgroups of individuals diagnosed with autism after school age? Findings In this data-driven analysis of a large cohort of autistic individuals, two distinct subgroups of individuals diagnosed with autism after the age of six were identified. The first included individuals requiring low levels of support, with modest comorbidity burdens; The second included individuals requiring high levels of support, with extremely high comorbidity burdens. Meaning The identification of opposite subgroups of individuals with delayed autism diagnosis improves our understanding of autism heterogeneity and moves us closer towards precision diagnosis of autism.