Stereology Resource Center, Inc — Department of Health and Human Services STTR Phase I: NICHD

Description

PROJECT SUMMARY Advances in technology and surgical procedures in the past decade have led to a remarkable increase in numbers of newborns subjected to lifesaving surgery. These postoperative neonates are customarily triaged to neonatal intensive care units (NICUs) for pain management with opioids, primarily morphine, fentanyl, and methadone. However, substantial evidence from in- vitro, animal, and human studies strongly suggests this severe pain-to-opioids regimen causes long-lasting and likely permanent traumatic harm to the developing neurological systems of neonates. We propose a novel machine learning and computer vision approach for early pain detection (EPD) with emphasis on postoperative neonates in NICU. By alerting NICU caregivers a minimum of ~ 30 minutes prior to pain onset, EPD will allow NICU staff to use fast-acting, opioid-sparing medications, e.g., intravenous paracetamol, ibuprofen, or ketorolac, in conjunction with non- pharmacological approaches, to “stay ahead of the pain” while avoiding opioid treatments, tolerance, withdrawal, and their associated side effects. Our team of NICU specialists, bioscientists, and computer experts will demonstrate proof-of- concept for reliable EPD in post-surgical neonates using the following aims: 1) Collect clinical information and multimodal data (facial expression, body movement,crying frequency, vital signs) for post-surgical pain prediction in neonates. We will collect and label multi-modal signals (facial expression, body movements, crying frequency, vital signs) from ~ 60 neonates in post-surgical pain at the NICU at Tampa General Hospital (TGH). We will combine these data with similar data from another cohort of ~60 neonates (total ~ 120 neonates) collected using the same system and approach at TGH from 2019 to 2022. 2) Show proof-of-concept for predicting the onset of post-surgical pain in neonates. The multimodal data collected from the training cases in Aim 1 will provide the ground truth for training a convolutional neural network to predict time-to-onset of pain for postoperative neonates in the testing cases. The performance target for the EPD in the test cases is pain prediction ~ 30 minutes prior to pain onset with a 90% confidence probability. Our intention is to disrupt the current standard [surgery; sedation; postoperative pain; opioid dependence, tolerance, withdrawal; discharge] in favor of a safer opioid-sparing approach [surgery; sedation; non-opioid treatment; discharge]. Our Phase 2 studies will add data from more diverse patient populations and examine the possible effects of EPD on stress biomarkers, e.g., cortisol, norepinephrine in hair, skin, blood, or urine. The major benefit to public health will be protection of perhaps the most vulnerable patient populations from unnecessary damage to their future health and well-being.PROJECT NARRATIVE Management of postoperative pain in NICUs worldwide follows the standard practice of surgery then sedation, severe pain, opioid analgesia, opioid dependence, and opioid withdrawal. Two decades of scientific evidence from in-vitro, animal, and human studies, argue this regimen is harmful to the newborn, i.e., severe pain and opioid dependence appear to cause long-lasting and most likely permanent damage to their normal anatomy, physiology, and behavior. We address this problem with a first-in-industry machine learning and computer vision solution that achieves early pain detection (EPD) of ~ 30 minutes prior to pain onset, which allows sufficient time for caregivers to “stay ahead of the pain” with opioid-sparing medications, e.g., acetaminophen, ketorolac, ibuprofen, and/or non-pharmacological interventions, and thereby avoid exposing vulnerable neonates to the harmful effects of postoperative pain, narcotic analgesia, and opioid withdrawal.