neonatal care
Help for Babies Born Dependent on Opioids
Posted on by Lawrence Tabak, D.D.S., Ph.D.
It’s been estimated that every 18 minutes in the United States, a newborn baby starts life with painful withdrawals from exposure to opioids in the womb. It’s called neonatal opioid withdrawal syndrome (NOWS), and it makes for a challenging start in life. These infants may show an array of withdrawal symptoms, including tremors, extreme irritability, and problems eating and sleeping.
Many of these infants experience long, difficult hospital stays to help them manage their withdrawal symptoms. But because hospital staff have no established evidence-based treatment standards to rely on, there is substantial variation in NOWS treatment around the country. There also are many open questions about the safest and most-effective way to support these babies and their families.
But answers are coming. The New England Journal of Medicine just published clinical trial results that evaluated care for infants with NOWS and which offer some much needed—and rather encouraging—data for families and practitioners [1]. The data are from the Eating, Sleeping, Consoling for Neonatal Opioid Withdrawal (ESC-NOW) trial, led by Leslie W. Young, The University of Vermont’s Larner College of Medicine, Burlington, and her colleagues Lori Devlin and Stephanie Merhar.
The ESC-NOW study is supported through the Advancing Clinical Trials in Neonatal Opioid Withdrawal (ACT NOW) Collaborative. ACT NOW is an essential part of the NIH Helping to End Addiction Long-term (HEAL) Initiative, an aggressive effort to speed scientific solutions to stem the national opioid public health crisis and improve lives.
The latest study puts to the test two different approaches to care for newborns with NOWS. The first approach relies on the Finnegan Neonatal Abstinence Scoring Tool. For almost 50 years, doctors primarily assessed NOWS using this tool. It is based on a scoring system of 21 signs of withdrawal, including disturbances in a baby’s nervous system, metabolism, breathing, digestion, and more. However, there have been concerns that this scoring tool has led to an overreliance on treating babies with opioid medications, including morphine and methadone.
The other approach is known as Eat, Sleep, Console (ESC) care [2]. First proposed in 2014, ESC care has been adopted in many hospitals around the world. Rather than focusing on a long list of physical signs of withdrawal, this approach relies on a simpler functional assessment of whether an infant can eat, sleep, and be consoled. It emphasizes treatments other than medication, such as skin-to-skin contact, breastfeeding, and care from their mothers or other caregivers in a calm and nurturing environment.
The ESC care approach places an emphasis on the use of supportive interventions and aims to empower families in the care and nurturing of their infants. While smaller quality improvement studies of ESC have been compelling, the question at issue is whether the Eat, Sleep, Console care approach can reduce the time until infants with NOWS are medically ready to go home from the hospital in a wide variety of hospital settings—and, most importantly, whether it can do so safely.
To find out, the ESC-NOW team enrolled 1,305 infants with NOWS who were born after at least 36 weeks gestation. The study’s young participants were largely representative of infants with NOWS in the U.S., although non-Hispanic Black and Hispanic infants were slightly overrepresented. The babies were born at one of 26 U.S. hospitals, and each hospital was randomly assigned to transition from usual care using the Finnegan tool to the ESC care approach at a designated time.
Each hospital had a three-month transition period between the usual care and the ESC to allow clinical teams time to train on the new approach. The trial primarily aimed to understand if there was a significant difference in how long newborns with NOWS spent in the hospital before being medically ready for discharge between those receiving usual care versus those receiving ESC care. Researchers also assessed infants for safety, tracking both safety events that occurred during the hospital stay and events that occurred after the baby left the hospital, such as non-accidental trauma or death during an infant’s first three months.
The reported results reflect 837 of the 1,305 infants, who met the study definition of being medically ready for discharge. Infants who were discharged before meeting the study criteria, which were informed by the 2012 American Academy of Pediatrics recommendations for monitoring of infants with NOWS, were not included in the primary analysis.
Among the 837 infants, those who received ESC care were medically ready for discharge significantly sooner than those who received usual care. On average, they were medically ready to go home after about eight days compared to almost 15 days for the usual care group.
Many fewer infants in the ESC care group were treated with opioids compared to the usual care group (19.5 percent versus 52.0 percent). In more good news for ESC care, there was no difference in safety outcomes through the first three months despite the shorter hospital stays and reduced opioid treatment in the hospital. Infants who were cared for using the ESC care approach were no more likely to visit the doctor’s office, emergency room, or hospital after being discharged from the hospital.
More long-term study is needed to evaluate these children over months and years as they continue to develop and grow. Many of the infants in this study will be evaluated for the first two years of life to assess the long-term impact of ESC care on development and other outcomes. These findings offer encouraging early evidence that the ESC care approach is safe and effective. Although there was some variability in the outcomes, this study also shows that this approach can work well across diverse hospitals and communities.
The ESC-NOW trial is just one portion of the NIH Heal Initiative’s ACT NOW program, focused on gathering scientific evidence on how to care for babies with NOWS. Other studies are evaluating how to safely wean babies who do receive treatment with medication off opioids more quickly. The ACT NOW Longitudinal Study also will enroll at least 200 babies with prenatal opioid exposure and another 100 who were not exposed to better understand the long-term implications of early opioid exposure.
I’ve been anxious to see the results of the ESC-NOW study for a few months. It’s been worth the wait. The results show that we’re headed in the right direction with learning how best to treat NOWS and help to improve the lives of these young children and their families in the months and years ahead.
References:
[1] Eat, Sleep, Console Approach versus usual care for neonatal opioid withdrawal. Young LW, Ounpraseuth ST, Merhar SL, Newman S, Snowden JN, Devlin LA, et al. NEJM, 2023 Apr 30 [Published online ahead of print]
[2] An initiative to improve the quality of care of infants with neonatal abstinence syndrome. Grossman MR, Berkwitt AK, Osborn RR, Xu Y, Esserman DA, Shapiro ED, Bizzarro MJ. Pediatrics. 2017 Jun;139(6):e20163360.
Links:
SAMHSA’s National Helpline (Substance Abuse and Mental Health Services Administration, Rockville, MD)
“Eat, Sleep, Console” reduces hospital stay and need for medication among opioid-exposed infants, NIH news release, May 1, 2023
Helping to End Addiction Long-term® (HEAL) Initiative (NIH)
Advancing Clinical Trials in Neonatal Opioid Withdrawal (ACT NOW)
Environmental Influences on Child Health Outcomes (ECHO) Program (NIH)
Leslie Young (The University of Vermont, Larner College of Medicine, Burlington)
NIH Support: The Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Center for Advancing Translational Sciences; Office of the Director
Artificial Intelligence Getting Smarter! Innovations from the Vision Field
Posted on by Michael F. Chiang, M.D., National Eye Institute
One of many health risks premature infants face is retinopathy of prematurity (ROP), a leading cause of childhood blindness worldwide. ROP causes abnormal blood vessel growth in the light-sensing eye tissue called the retina. Left untreated, ROP can lead to lead to scarring, retinal detachment, and blindness. It’s the disease that caused singer and songwriter Stevie Wonder to lose his vision.
Now, effective treatments are available—if the disease is diagnosed early and accurately. Advancements in neonatal care have led to the survival of extremely premature infants, who are at highest risk for severe ROP. Despite major advancements in diagnosis and treatment, tragically, about 600 infants in the U.S. still go blind each year from ROP. This disease is difficult to diagnose and manage, even for the most experienced ophthalmologists. And the challenges are much worse in remote corners of the world that have limited access to ophthalmic and neonatal care.
Artificial intelligence (AI) is helping bridge these gaps. Prior to my tenure as National Eye Institute (NEI) director, I helped develop a system called i-ROP Deep Learning (i-ROP DL), which automates the identification of ROP. In essence, we trained a computer to identify subtle abnormalities in retinal blood vessels from thousands of images of premature infant retinas. Strikingly, the i-ROP DL artificial intelligence system outperformed even international ROP experts [1]. This has enormous potential to improve the quality and delivery of eye care to premature infants worldwide.
Of course, the promise of medical artificial intelligence extends far beyond ROP. In 2018, the FDA approved the first autonomous AI-based diagnostic tool in any field of medicine [2]. Called IDx-DR, the system streamlines screening for diabetic retinopathy (DR), and its results require no interpretation by a doctor. DR occurs when blood vessels in the retina grow irregularly, bleed, and potentially cause blindness. About 34 million people in the U.S. have diabetes, and each is at risk for DR.
As with ROP, early diagnosis and intervention is crucial to preventing vision loss to DR. The American Diabetes Association recommends people with diabetes see an eye care provider annually to have their retinas examined for signs of DR. Yet fewer than 50 percent of Americans with diabetes receive these annual eye exams.
The IDx-DR system was conceived by Michael Abramoff, an ophthalmologist and AI expert at the University of Iowa, Iowa City. With NEI funding, Abramoff used deep learning to design a system for use in a primary-care medical setting. A technician with minimal ophthalmology training can use the IDx-DR system to scan a patient’s retinas and get results indicating whether a patient should be sent to an eye specialist for follow-up evaluation or to return for another scan in 12 months.
Many other methodological innovations in AI have occurred in ophthalmology. That’s because imaging is so crucial to disease diagnosis and clinical outcome data are so readily available. As a result, AI-based diagnostic systems are in development for many other eye diseases, including cataract, age-related macular degeneration (AMD), and glaucoma.
Rapid advances in AI are occurring in other medical fields, such as radiology, cardiology, and dermatology. But disease diagnosis is just one of many applications for AI. Neurobiologists are using AI to answer questions about retinal and brain circuitry, disease modeling, microsurgical devices, and drug discovery.
If it sounds too good to be true, it may be. There’s a lot of work that remains to be done. Significant challenges to AI utilization in science and medicine persist. For example, researchers from the University of Washington, Seattle, last year tested seven AI-based screening algorithms that were designed to detect DR. They found under real-world conditions that only one outperformed human screeners [3]. A key problem is these AI algorithms need to be trained with more diverse images and data, including a wider range of races, ethnicities, and populations—as well as different types of cameras.
How do we address these gaps in knowledge? We’ll need larger datasets, a collaborative culture of sharing data and software libraries, broader validation studies, and algorithms to address health inequities and to avoid bias. The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) project and NIH’s Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program project will be major steps toward addressing those gaps.
So, yes—AI is getting smarter. But harnessing its full power will rely on scientists and clinicians getting smarter, too.
References:
[1] Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. JAMA Ophthalmol. 2018 Jul 1;136(7):803-810.
[2] FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Food and Drug Administration. April 11, 2018.
[3] Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Lee AY, Yanagihara RT, Lee CS, Blazes M, Jung HC, Chee YE, Gencarella MD, Gee H, Maa AY, Cockerham GC, Lynch M, Boyko EJ. Diabetes Care. 2021 May;44(5):1168-1175.
Links:
Retinopathy of Prematurity (National Eye Institute/NIH)
Diabetic Eye Disease (NEI)
Michael Abramoff (University of Iowa, Iowa City)
Bridge to Artificial Intelligence (Common Fund/NIH)
[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s institutes and centers to contribute occasional guest posts to the blog as a way to highlight some of the cool science that they support and conduct. This is the second in the series of NIH institute and center guest posts that will run until a new permanent NIH director is in place.]
