Study to Collect Data for Neonatal Abstinence Syndrome (NAS) and Evaluate the Automated Data Collection Process

NCT ID: NCT06303986

Last Updated: 2025-09-09

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ENROLLING_BY_INVITATION

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-03-18

Study Completion Date

2026-06-30

Brief Summary

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Substance abuse during pregnancy is on the rise through both prescribed and illicit use of controlled substances, which has increased neonatal abstinence syndrome (NAS). The prevalence of opioid use during pregnancy has increased by 333% from 2013 to 2014 and continues to rise. Approximately 1 in 3 women were prescribed opioids during pregnancy from 2008 to 2012. In the US, NAS was diagnosed every 25 minutes in 2014. By 2019, it became every 15 minutes. Although there are medication-based interventions for the treatment of NAS, used in up to 80% of opioid-exposed infants, these treatments carry risks of toxicity and drug interactions. Despite the steep medical costs and the risks of treatment, current tools to assess the severity of NAS are subjective and suffer from examiner bias, resulting in poorer clinical outcomes, such as longer lengths of stay in the Neonatal Intensive Care Unit (NICU), for these babies. Studies have shown that continuous vital sign monitoring improves outcomes and decreases the length of stay in general practice. Preliminary machine learning models have been able to predict pharmacological treatment for Neonatal Opioid Withdrawal Syndrome (NOWS). This project will collect physiological and behavioral data of NAS patients to develop an AI algorithm and establish the advantages of continuous monitoring in NAS. The AI algorithm, processed by machine learning, will help predict NAS symptoms, automate scoring, and provide healthcare personnel with predictive analytics to guide suggested treatments.

Detailed Description

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The current diagnostic and assessment framework for NAS heavily relies on subjective methods, primarily the Finnegan Neonatal Abstinence Score (FNAS). FNAS helps providers evaluate pharmacological and non-pharmacological treatments and monitor the progress of infants with NAS. The Eat Sleep Console (ESC) approach has been implemented in some hospitals to emphasize non-pharmacological interventions as the primary method of managing and treating NAS. Despite the high prevalence of NAS and the significant resources allocated to its management, the healthcare system continues to grapple with an unmet clinical need for standardized diagnostic and treatment protocols.

The reliance on subjective assessments contributes to this challenge, as FNASS and ESC introduce variability in care that can affect outcomes. Developing objective, reliable tools for assessing NAS severity and guiding treatment decisions remains a critical need in neonatal care, promising to enhance the efficiency and effectiveness of interventions for these vulnerable patients. Recent studies have underscored the lack of consistency in diagnosing and treating NAS, revealing a broad spectrum of practices across different pediatric healthcare settings. This problematic inconsistency leads to varied patient outcomes and a lack of clarity on best practices.

This multicenter study will collect data that will be used to develop an AI-based tool that can automate scoring with predictive analytics. Additionally, the investigators aim to establish the advantages of continuous monitoring in NAS that should lead to decreased length of stay in the NICU and improved patient outcomes.

Conditions

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Neonatal Abstinence Syndrome

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Observational Group

These are the neonates enrolled in the study to collect data and aid in establishing continuous monitoring advantages.

NeoMonki

Intervention Type OTHER

Collection of Data for assessing the reliability of Neomonki and Realtime monitoring

Interventions

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NeoMonki

Collection of Data for assessing the reliability of Neomonki and Realtime monitoring

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* The subject selection criteria per site will be 80% term neonates affected by neonatal abstinence syndrome (NAS) and 20% term neonates without any health complications.
* The eligible screening process for the NAS neonates in the study will include neonates affected by NAS, newborn infants diagnosed with NAS, infants with confirmed history of prenatal exposure to opioids or other drugs that can cause NAS, and infants who meet specific diagnostic criteria for NAS based on standardized clinical assessments.
* The eligible screening process for neonates without any health complications include newborn infants without any known health complications or medical conditions, infants with a normal physical examination and absence of clinical signs or symptoms suggestive of NAS or other health issues.
* Given the simple nature of the study, wards of the courts will also be enrolled upon evaluating the appropriateness, identifying an advocate if required, and providing an approved consent for the ward subject.

Exclusion Criteria

* The investigators are NOT purposefully excluding or including any gender or race in our studies.
Maximum Eligible Age

4 Weeks

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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University of New Mexico

OTHER

Sponsor Role collaborator

The University of Texas Health Science Center at San Antonio

OTHER

Sponsor Role collaborator

Corewell Health East Dearborn Hospital and Royal Oak Hospital

UNKNOWN

Sponsor Role collaborator

Rekovar Inc.

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Nitin Chouthai, MD

Role: PRINCIPAL_INVESTIGATOR

Rekovar Inc.

Locations

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University of New Mexico

Albuquerque, New Mexico, United States

Site Status

Countries

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United States

Provided Documents

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Document Type: Informed Consent Form

View Document

Related Links

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Other Identifiers

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NAS-002

Identifier Type: -

Identifier Source: org_study_id

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