Data-driven characterization of individuals with delayed autism diagnosis

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