Study to Collect Data for Neonatal Abstinence Syndrome (NAS) and Evaluate the Automated Data Collection Process
Public ClinicalTrials.gov record NCT06303986. Field values are reproduced from the official study page; the official ClinicalTrials.gov record remains the source of truth for eligibility, enrollment, and contact information.
Data is sourced from official ClinicalTrials.gov public API records. Always review the official ClinicalTrials.gov record for the latest information.
Official title
A Multi-center Study to Collect Data for Basic Physiological Research in Neonatal Abstinence Syndrome (NAS) and Evaluate the Automated Finnegan/ESC Data Collection Process
Brief summary
Reproduced verbatim from the official ClinicalTrials.gov record. Not medical advice.
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.
Study identification
- NCT ID
- NCT06303986
- Recruitment status
- Enrolling by invitation
- Study type
- Observational
- Phase
- Not listed
- Enrollment
- 100 participants
Conditions and interventions
Conditions
Eligibility (public fields only)
- Age range
- Up to 4 Weeks
- Sex
- All
- Healthy volunteers
- Accepts healthy volunteers
This page does not interpret eligibility. Detailed inclusion and exclusion criteria are on the official ClinicalTrials.gov record.
Study timeline
- Start date
- Mar 17, 2024
- Primary completion
- Jun 29, 2026
- Completion
- Jun 29, 2026
- Last update posted
- Sep 8, 2025
2024 – 2026
United States locations
- U.S. sites
- 1
- U.S. states
- 1
- U.S. cities
- 1
| Facility | City | State | ZIP | Site status |
|---|---|---|---|---|
| University of New Mexico | Albuquerque | New Mexico | 87106 | — |
Site contact phone numbers, emails, and investigator names are intentionally not displayed here. Open the official ClinicalTrials.gov record for site contact information.
About this trial record page
- What this page shows
- Public field values for ClinicalTrials.gov record NCT06303986, including study identification, conditions, interventions, eligibility (age, sex, healthy volunteer), timeline, and U.S. site list.
- What this page does not do
- No medical advice, eligibility judgments, treatment recommendations, study quality scoring, or AI-generated medical summaries. No site contact phone numbers, emails, or investigator names.
- Where the data comes from
- Sourced from the official ClinicalTrials.gov public API. The official record is the source of truth.
- Last refresh
- Last update posted Sep 8, 2025 · Synced Sep 6, 2026
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Open the official record
The complete protocol, eligibility criteria, and contact information for NCT06303986 live on ClinicalTrials.gov.