EDRM for Autism

Overseen By Allison Schwartz, PhD

Age: < 18

Sex: Any

Trial Phase: Academic

Sponsor: Emory University

Disqualifiers: Self-referrals, Over 33 months, Non-English speakers

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 1 Jurisdiction This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 1

300 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new method for early autism diagnosis in young children at high risk. The focus is on developing and evaluating a plan to identify autism sooner, using tools like parent surveys and clinician assessments. This approach is called the Early Diagnostic Response Model (EDRM), also known as the Telehealth-based Autism Diagnostic Protocol or Autism Early Detection Model. Suitable participants are children aged 16-30 months who have been flagged as high-risk for autism through the M-CHAT-R screening and are referred by specific early intervention programs in Georgia. Parents must have basic English skills, internet access, and their child should regularly hear English at home or in care settings. As an unphased trial, this study offers families the chance to contribute to groundbreaking research that could lead to earlier autism diagnosis and intervention.

Will I have to stop taking my current medications?

The trial information does not specify whether participants need to stop taking their current medications.

What prior data suggests that this diagnostic protocol is safe for children?

Research has shown that the Early Diagnostic Response Model (EDRM) aims to simplify diagnosing autism in young children. As EDRM is a diagnostic tool rather than a treatment or medication, specific safety data is limited.

Studies have found that using machine learning and telehealth for autism diagnosis is generally safe, with no reports of negative side effects. The process typically involves answering questions and completing assessments online.

Since EDRM focuses on early autism diagnosis, it does not involve drugs or physical treatments. Safety concerns are minimal, as it primarily involves gathering information through surveys and assessments. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the Early Diagnostic Response Model (EDRM) for autism because it aims to streamline the diagnostic process for young children, potentially leading to earlier intervention. Unlike traditional methods that can be lengthy and fragmented, EDRM integrates a comprehensive survey and targeted clinician assessment to deliver a faster diagnosis. This approach could help address the critical need for timely identification of autism, allowing families to access support and resources sooner.

What evidence suggests that the Early Diagnostic Response Model (EDRM) is effective for autism?

Research shows that the Early Diagnostic Response Model (EDRM) for autism, which participants in this trial will receive, aims to improve autism diagnosis in young children. Studies have found that advanced tools like automated machine learning can make diagnosing autism spectrum disorder (ASD) faster and more accurate. Early detection models using electronic health records have been tested and effectively predict autism in high-risk toddlers. Initial findings suggest that this approach can help identify autism sooner, allowing for quicker interventions. Online assessment tools are also being tested to support accurate and timely diagnoses. ^{1 6 7 8 9}

Who Is on the Research Team?

Allison Schwartz, PhD

Principal Investigator

Emory University

Are You a Good Fit for This Trial?

This trial is for families with children aged 16-30 months at risk for autism, referred by specific BCW districts. Participants need internet access, exposure to English, and basic English skills. Children must score ≥8 on the MCHAT-R and be referred by 33 months of age.

Inclusion Criteria

My child is 16-30 months old, scored ≥8 on the MCHAT-R, and was referred to the clinic by 33 months.

Referring BCW provider must be one of the three targeted BCW districts participating in this pilot study.

Parent/Guardian needs to have basic English proficiency

Exclusion Criteria

Non-English speakers will be excluded from participation due to the high correlation between verbal language abilities and social communication and social interaction (SCI) abilities as part of the DSM-5-TR autism criteria.

Families making self-referrals to the EAC or referrals outside of the targeted BCW districts will be excluded from recruitment for this study.

My child is not older than 33 months.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Assessment

Families with children between 16-33 months complete developmental history surveys and questionnaires, followed by an autism assessment and feedback session.

Up to 12 weeks

1 in-person visit, telehealth sessions

Follow-up

Participants are monitored for satisfaction and effectiveness of the EDRM assessment.

3 months

Satisfaction surveys administered

Evaluation Completion

Final summary report sent to families and analysis of the number of families who received ASD evaluation.

Until study completion (approximately 1 year)

What Are the Treatments Tested in This Trial?

Interventions

• Early Diagnostic Response Model (EDRM)

Trial Overview The EDRM protocol is being tested to see if it's effective in diagnosing autism early in kids identified as high-risk in a public health service program. The study will collect data before and after implementing EDRM to assess its success.

How Is the Trial Designed?

Group I: Families with children enrolled in BCW with suspected diagnosis of autism Experimental Treatment 1 Intervention

Post-consent, families with children between 16-33 months will receive a link to complete the EAC Developmental History Survey on REDCap and a link to complete parent/caregiver questionnaires about developmental and/or adaptive information. Then, the family will be assigned to a clinician for the autism assessment and scheduled their assessment and feedback session (one week later).

Early Diagnostic Response Model (EDRM) is already approved in United States for the following indications:

Approved in United States as Early Diagnostic Response Model (EDRM) for:

• Autism Spectrum Disorder Diagnosis

Who Is Running the Clinical Trial?

Emory University

Lead Sponsor

Trials

1,735

Recruited

2,605,000+

Georgia Department of Public Health

Collaborator

Trials

2

Recruited

1,400+

Published Research Related to This Trial

Telemedicine shows promise as an effective alternative for screening and diagnosing Autism Spectrum Disorder (ASD), potentially reducing the long wait times parents currently face for assessments.

A systematic review of 26 studies revealed that telemedicine applications for ASD had varying sensitivity (0.70 to 1) and specificity (0.38 to 1), indicating that while some tools are effective, more rigorous studies with larger sample sizes are needed to fully evaluate their reliability and effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A systematic review of telehealth screening, assessment, and diagnosis of autism spectrum disorder. Liu, M., Ma, Z.[2022]

A study using electronic health records from 45,080 children found that an automated model for early autism detection achieved 45.5% sensitivity and 23.0% positive predictive value (PPV) at 30 days old, indicating a promising start for early identification.

By age 1 year, the model's sensitivity improved to 59.8% with a PPV of 17.6%, suggesting that integrating this model with caregiver surveys could enhance early screening accuracy for autism.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year. Engelhard, MM., Henao, R., Berchuck, SI., et al.[2023]

Telehealth methods, including real-time video conferencing and the Store-and-Forward approach, show promise in diagnosing Autism Spectrum Disorder (ASD) with high agreement compared to traditional face-to-face methods, based on a review of 10 studies.

Families and clinicians reported high satisfaction with telehealth assessments, indicating that these methods could improve access to ASD diagnosis, especially for individuals with clear autism traits, though further research with larger sample sizes is needed.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Use of telehealth for facilitating the diagnostic assessment of Autism Spectrum Disorder (ASD): A scoping review. Alfuraydan, M., Croxall, J., Hurt, L., et al.[2020]

Telehealth is highly satisfactory for engaging with patients with Autism Spectrum Disorder (ASD) and is effective for education, training, and symptom management, especially for those living far from specialists.

However, its effectiveness for diagnosing ASD using standard tools like the ADOS and ADI via videoconferencing is limited, indicating that while telehealth is beneficial for ongoing care, it may not be suitable for initial diagnostic assessments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Bridging Care Gaps: Using Tele-health to Provide Care for People With Autism Spectrum Disorder. Goldstein, FP., Klaiman, C., Willliams, S.[2022]

Telemedicine models for diagnosing and intervening in children with autism spectrum disorder (ASD) have been developed, focusing on both synchronous and asynchronous methods to meet patient needs during the COVID-19 pandemic.

These models aim to provide preliminary assessments and support for families, enhancing parent involvement and improving children's social communication skills, while not replacing in-person evaluations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Phase 2 and Later of COVID-19 Lockdown: Is it Possible to Perform Remote Diagnosis and Intervention for Autism Spectrum Disorder? An Online-Mediated Approach. Narzisi, A.[2020]

The Autism Detection in Early Childhood-Virtual (ADEC-V) assessment tool demonstrated good sensitivity (0.82) and specificity (0.78) for identifying autism in a study of 121 children aged 18-47 months, indicating it is a reliable method for telehealth evaluations.

The ADEC-V showed significant correlations with established autism symptom measures, suggesting it effectively captures key characteristics of autism spectrum disorder (ASD) in young children, although further validation in a larger sample is needed.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Preliminary Validation and Feasibility of the Autism Detection in Early Childhood-Virtual (ADEC-V) for Autism Telehealth Evaluations in a Hospital Setting. Kryszak, EM., Albright, CM., Stephenson, KG., et al.[2022]

Telehealth has been successfully adapted for autism spectrum disorder (ASD) diagnostic services during the COVID-19 pandemic, allowing continued access to essential evaluations for patients.

The case series highlights different telehealth approaches used for ASD diagnosis, emphasizing the need for future research to explore the long-term benefits of telehealth in this field beyond the pandemic.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Telehealth diagnosis of autism spectrum disorder through clinical cases. Ludwig, NN., Child, AE., Jashar, DT., et al.[2023]

A study involving 16 adult clients and 56 parent/carers found that most participants felt comfortable with telehealth assessments for autism and were open to future assessments using this method.

Clinicians expressed slightly less satisfaction with tele-assessments compared to in-person evaluations but maintained high confidence in their diagnostic decisions, suggesting that telehealth can be a viable option for autism assessments under certain conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Autism assessment via telehealth during the Covid 19 pandemic: Experiences and perspectives of autistic adults, parents/carers and clinicians. Gibbs, V., Cai, RY., Aldridge, F., et al.[2022]

Citations

1. clinicaltrials.gov clinicaltrials.gov/study/NCT05419895

NCT05419895 | Early Diagnostic Response Model (EDRM) The objective of this study is to develop, pilot, and evaluate a diagnostic protocol to assess high-risk toddlers for autism spectrum disorder (ASD) diagnosis ...

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12346167/

Early Detection of Autism Spectrum Disorder Through ... This research proposes leveraging automated machine learning (AUTOML) to streamline the diagnostic process and enhance its accuracy. Methods: In ...

3. withpower.com withpower.com/trial/phase-autism-early-infantile-5-2022-7eeb7

EDRM for Autism · Recruiting Participants for Clinical Trial ... This trial is testing a new method to diagnose autism in young children using online tools. It focuses on families with high-risk toddlers.

4. jamanetwork.com jamanetwork.com/journals/jamanetworkopen/fullarticle/2800898

Predictive Value of Early Autism Detection Models Based ... This diagnostic study evaluates the accuracy of early autism detection models based on electronic health record data collected before age 1 ...

5. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC9760512/

Tele-assessment of young children referred for autism ... The present study examined clinical characteristics and diagnostic outcome for young children receiving autism spectrum disorder tele-assessment during the ...

6. mdpi.com mdpi.com/2075-4418/15/15/1859

Early Detection of Autism Spectrum Disorder Through ... This research proposes leveraging automated machine learning (AUTOML) to streamline the diagnostic process and enhance its accuracy. Methods: In this study, by ...

7. sciencedirect.com sciencedirect.com/science/article/pii/S1876201825001418

Accelerating autism spectrum disorder care: A rapid review ... Data-driven methods enable early, personalized, and accurate ASD diagnosis and interventions. •. Ethical use, privacy, and equitable access are vital for ...

8. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC10091235/

Telehealth diagnosis of autism spectrum disorder through ... The purpose of this case series is to provide examples of three different approaches to ASD differential diagnostic services via telehealth that we have taken ...

9. frontiersin.org frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1507922/full

Early diagnosis of autism across developmental stages ... This study introduces a robust and interpretable machine learning framework to diagnose ASD using questionnaire data.