PedialyDx Inc. proposes to replicate previous research findings and develop a commercial product that can assist in the diagnosis of Neonatal Opioid Withdrawal Syndrome (NOWS), the withdrawal that infants experience shortly after birth due to the abrupt discontinuation of fetal exposure to opioids. NOWS has become a significant public health problem, increasing from 1.6 per 1000 births in 2004 to 8.8 per 1000 births in 2016, with associated hospital charges increasing from $190M in 2000 to $720M in 2009. Accurate diagnosis of NOWS is important because false-positives can result in babies unnecessarily receiving pharmacological treatment with corresponding prolonged hospitalization and substantial cost, or false- negatives result in babies not being treated when they should, potentially leading to serious medical problems. Unfortunately, current methods to diagnose NOWS, whether pharmacologically based such as the "gold standard" Finnegan scoring system, or non-pharmacologically based such as "East, Sleep and Console" are limited because of their subjectivity. Using proprietary acoustic analysis software and Machine Learning (ML) algorithms, our team has identified a preliminary "cry signature" relating baby cry acoustics to the Finnegan with .81 accuracy. PedialyDx's NOWS-app will deliver improved care for children with NOWS through 2 innovations: 1) more objective assessment using acoustic cry analysis will lead to more accurate diagnosis and treatment (fewer false positives, fewer false negatives, and more precise hospitalization stays) and 2) save time, leading to cost savings, in the management of babies with NOWS. Our preliminary data has been collected in a single controlled clinical setting, which has demonstrated proof of concept that our algorithm can objectively assess baby cries. Next, we will advance the commercialization of our NOWS-app by expanding upon our existing dataset by collecting data from real-world settings to optimize our ML algorithm for NOWS. In Aim 1, we will conduct a multi-center study (7 sites) to extend the research of baby cry as an indicator of NOWS. In Aim 2, we will complete the development of the NOWS-app. Successful completion of this project will result in our NOWS-App product ready for submission to the FDA. The work proposed in this application will also allow PedialyDx to take important steps toward advancing its baby cry technology platform to enable products for other conditions, such as screening for autism and other developmental disorders, colic and pain.
Public Health Relevance Statement: Narrative: PedialyDx Inc. proposes to replicate previous research findings and develop a commercial product that can assist in the diagnosis of Neonatal Opioid Withdrawal Syndrome (NOWS), the withdrawal that infants experience shortly after birth due to the abrupt discontinuation of fetal exposure to opioids. Current methods to diagnose NOWS, whether pharmacologically based such as the widely used Finnegan scoring system, or non-pharmacologically based such as "East, Sleep and Console" are limited because of their subjectivity. Our NOWS-App will be an objective, repeatable, low cost system to support diagnosis of NOWS.
Project Terms: Acoustics; Acoustic; Adoption; Algorithms; Birth; Parturition; Child Care; Puericulture; Diagnosis; Eating; Food Intake; Family; Feedback; Focus Groups; Freezing; Gold; Health; Recording of previous events; History; Hospitalization; Hospital Admission; Hospitals; Infant; Methods; NIH; National Institutes of Health; United States National Institutes of Health; Neonatal Opioid Withdrawal Syndrome; Neonatal Substance Withdrawal; Neonatal Withdrawal Syndrome; newborn abstinence syndrome; Neonatal Abstinence Syndrome; Noise; nurse; Nurses; Painful; Pain; Parents; Patients; Pharmacology; Public Health; Research; Investigators; Researchers; Research Personnel; Savings; Sleep; Software; Computer software; Technology; Time; Universities; Woman; Work; Cost Savings; Treatment Cost; Data Set; Dataset; base; improved; Site; Clinical; Phase; Medical; Hospital Charges; Colic; Colicky Pain; Abdominal colic; Withdrawal; Opiates; Opioid; Measurement; Funding; Multi-center studies; Multicenter Studies; Exposure to; machine learned; Machine Learning; System; Location; experience; HIPAA; Kennedy Kassebaum Act; PL 104-191; PL104-191; Public Law 104-191; United States Health Insurance Portability and Accountability Act; Health Insurance Portability and Accountability Act; Sampling; exposed in utero; fetal exposure; in utero exposure; intra-uterine environmental exposure; intrauterine environmental exposure; prenatally exposed; prenatal exposure; develop software; developing computer software; software development; developmental disease; developmental disorder; Cell Phone; Cellular Telephone; iPhone; smart phone; smartphone; Cellular Phone; Autism; Autistic Disorder; Early Infantile Autism; Infantile Autism; Kanner's Syndrome; autistic spectrum disorder; autism spectrum disorder; Data; Pharmacological Treatment; Small Business Innovation Research Grant; SBIR; Small Business Innovation Research; Modification; Development; developmental; cost; design; designing; innovation; innovate; innovative; commercialization; product development; screening; cloud based; accurate diagnosis; machine learning algorithm; machine learned algorithm; machine learning based algorithm; automated analysis; algorithm development; algorithm training
