# An innovative model of early ASD diagnosis in the primary care setting: Integrating clinical evaluation and biomarkers to improve diagnostic accuracy

> **NIH NIH R21** · INDIANA UNIVERSITY INDIANAPOLIS · 2020 · $435,069

## Abstract

Project Summary / Abstract
Early identification of autism spectrum disorder (ASD) and subsequent engagement in evidence-based
interventions is associated with substantial developmental gains and lower lifetime costs. Despite core
symptoms emerging in the first year of life, the national average age of ASD diagnosis is not until 4 to 5 years,
with diagnosis of children from lower income, minority, and rural families lagging further behind. Thus, there is
a critical public health need to develop and test models of accurate, streamlined community-based ASD
diagnosis. Much of the research to date has focused on independent examination of community-based models
of early ASD diagnosis and measures of underlying biological processes as alternative approaches to
identifying children with ASD. However, given the heterogeneous nature of the ASD phenotype as well as
limitations in standard diagnostic tools, multi-method approaches that integrate clinical and biobehavioral
measures are likely to have the most impact on advancing the accuracy of ASD diagnosis in the community
setting. Our objective is to test an innovative method of ASD diagnosis that integrates clinical evaluation and
assessment of biobehavioral markers in a large high-risk community-referral sample of children in the primary
care setting. We propose three specific aims: 1) Evaluate the diagnostic accuracy of the Early Evaluation (EE)
Hub model of ASD diagnosis in the community primary care setting, 2) Determine whether biobehavioral
markers can reliably differentiate young children with and without ASD in a high-risk community referred
sample, and 3) Determine whether a combination of clinical and biobehavioral measures can be used to
accurately predict ASD diagnostic outcome in a high-risk sample of young children evaluated in the primary
care setting. EE Hubs across the state of Indiana will refer a consecutive sample of 120 children, ages 16-30
months, for diagnostic confirmation by an expert ASD-specialist using a standardized protocol including the
Autism Diagnostic Observation Schedule – 2 as well as measures of developmental level and adaptive skills. A
series of eye-tracking measures (pupil dilation, pupillary light reflex, blink rate, saccadic latency, and looking
time) will provide indirect measures of neuromodulator activity (i.e., norepinephrine, acetylcholine, and
dopamine, respectively) and non-social attentional disengagement efficiency and preferences for social
compared to non-social stimuli. Our approach demonstrates a high level of scientific innovation because it
integrates both clinical evaluation and assays of biobehavioral markers to develop and test a model of early
ASD diagnosis in local primary care settings. The proposed research is significant because it has the potential
to decrease wait times for initial ASD diagnosis and allow for earlier entry into evidence-based interventions,
thereby improving child outcomes and reducing societal costs associated with the d...

## Key facts

- **NIH application ID:** 10057793
- **Project number:** 1R21MH121747-01A1
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Brandon Keehn
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $435,069
- **Award type:** 1
- **Project period:** 2020-08-05 → 2023-08-04

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10057793

## Citation

> US National Institutes of Health, RePORTER application 10057793, An innovative model of early ASD diagnosis in the primary care setting: Integrating clinical evaluation and biomarkers to improve diagnostic accuracy (1R21MH121747-01A1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10057793. Licensed CC0.

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