Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06303986 |
| Other study ID # |
NAS-002 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
March 18, 2024 |
| Est. completion date |
June 30, 2024 |
Study information
| Verified date |
March 2024 |
| Source |
Rekovar Inc. |
| Contact |
Neema Onbirbak, BS |
| Phone |
9493741604 |
| Email |
neema.onbirbak[@]rekovar.com |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational [Patient Registry]
|
Clinical Trial Summary
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.
Description:
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.
