Ocular phenotypes associated with autism and atypical neurodevelopment: Insights from electronic health records

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Abstract

Atypical visual perception is often described in autism spectrum disorder (ASD); however, few studies have characterized ocular conditions in ASD using basic vision metrics such as those collected in routine eye exams. The current study uses electronic health record (EHR) codes to establish ocular phenotypes across individuals with and without neurodevelopmental diagnoses, including ASD. Using a population health approach, we assessed ocular conditions (identified based on medical codes from the EHR) in N = 7518 pediatric patients across 4 groups: n = 1196 with ASD, n = 156 with Intellectual Disability (ID), n = 347 with Language Disorder (LD), and n = 5819 matched controls (MC). We grouped and summarized ocular conditions across 5 ocular classes, including: (1) Visual impairment; (2) Refractive error, Accommodative & Vergence disorders; (3) Eye movements, Strabismus & Oculomotor Disorders; (4) Retinal disorders & Ocular disease; (5) Photosensitivity & Atypical Pupil response. We find an increased rate of ocular conditions in diagnostic groups compared to matched controls across classes 1 and 3. This study highlights the use of EHR data to curate ocular condition metrics collected in clinical care. The characterization of ocular anomalies across categories using EHR data offers a scalable method to improve our understanding of vision phenotypes that may be present in children with ASD and other neurodevelopmental differences.
Original languageEnglish
Article number108767
Pages (from-to)1-8
Number of pages8
JournalVision Research
Volume242
Early online date27 Jan 2026
DOIs
Publication statusPublished online - 27 Jan 2026

Bibliographical note

0042-6989/© 2026 Published by Elsevier Ltd.

Data Access Statement

Summary level data used and/or analyzed during the current study are available from the corresponding author upon reasonable request and the acquisition of appropriate permissions. Individual level data is unable to be shared due to the nature of the origin of the data from a health record.

Funding

This work was supported by the Autism Research Institute (PI: Troiani) and the Pennsylvania (PA) Department of Health- Common wealth Universal Research Enhancement (CURE) award (PI: Troiani).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Electronic health record (EHR)
  • Autism spectrum disorder (ASD)
  • Vision

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