protocolSection identificationModule nctId: NCT06303986, orgStudyIdInfo id: NAS-002, briefTitle: Study to Collect Data for Neonatal Abstinence Syndrome (NAS) and Evaluate the Automated Data Collection Process, statusModule overallStatus: RECRUITING, startDateStruct date: 2024-03-18, primaryCompletionDateStruct date: 2024-06-30, completionDateStruct date: 2024-06-30, studyFirstPostDateStruct date: 2024-03-12, lastUpdatePostDateStruct date: 2024-03-22, sponsorCollaboratorsModule leadSponsor name: Rekovar Inc., class: INDUSTRY, collaborators name: Johns Hopkins University, collaborators name: Children's Hospital of Orange County, collaborators name: Children's Hospital Los Angeles, collaborators name: University of New Mexico, descriptionModule briefSummary: 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., conditionsModule conditions: Neonatal Abstinence Syndrome, designModule studyType: OBSERVATIONAL, designInfo observationalModel: COHORT, timePerspective: PROSPECTIVE, enrollmentInfo count: 60, type: ESTIMATED, armsInterventionsModule interventions name: Neomonki, outcomesModule primaryOutcomes measure: Training/validation data collection, secondaryOutcomes measure: Continuous Monitoring, eligibilityModule sex: ALL, maximumAge: 4 Weeks, stdAges: CHILD, contactsLocationsModule locations facility: John Hopkins University, status: RECRUITING, city: Baltimore, state: Maryland, zip: 21224, country: United States, contacts name: Krystle McConnell, MPH, role: CONTACT, phone: 410-550-8779, email: kgreen39@jhmi.edu, contacts name: Lauren Jansson, MD, role: PRINCIPAL_INVESTIGATOR, geoPoint lat: 39.29038, lon: -76.61219, locations facility: University of New Mexico, status: RECRUITING, city: Albuquerque, state: New Mexico, zip: 87106, country: United States, contacts name: Sandra S Beauman, MSN, RNC-NIC, role: CONTACT, phone: 505-272-0363, email: sbeauman@salud.unm.edu, contacts name: Jessie R Maxwell, MD, MBA, role: PRINCIPAL_INVESTIGATOR, geoPoint lat: 35.08449, lon: -106.65114, documentSection largeDocumentModule largeDocs typeAbbrev: ICF, hasProtocol: False, hasSap: False, hasIcf: True, label: Informed Consent Form, date: 2023-03-23, uploadDate: 2024-02-26T15:29, filename: ICF_000.pdf, size: 230657, hasResults: False