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Background: Early-life pain is associated with adverse neurodevelopmental consequences; and current pain assessment practices are discontinuous, inconsistent, and highly dependent on nurses' availability. Furthermore, facial expressions in commonly used pain assessment tools are not associated with brain-based evidence of pain., Purpose: To develop and validate a machine learning (ML) model to classify pain., Methods: In this retrospective validation study, using a human-centered design for Embedded Machine Learning Solutions approach and the Neonatal Facial Coding System (NFCS), 6 experienced neonatal intensive care unit (NICU) nurses labeled data from randomly assigned iCOPEvid (infant Classification Of Pain Expression video) sequences of 49 neonates undergoing heel lance. NFCS is the only observational pain assessment tool associated with brain-based evidence of pain. A standard 70% training and 30% testing split of the data was used to train and test several ML models. NICU nurses' interrater reliability was evaluated, and NICU nurses' area under the receiver operating characteristic curve (AUC) was compared with the ML models' AUC., Results: Nurses weighted mean interrater reliability was 68% (63%-79%) for NFCS tasks, 77.7% (74%-83%) for pain intensity, and 48.6% (15%-59%) for frame and 78.4% (64%-100%) for video pain classification, with AUC of 0.68. The best performing ML model had 97.7% precision, 98% accuracy, 98.5% recall, and AUC of 0.98., Implications for Practice and Research: The pain classification ML model AUC far exceeded that of NICU nurses for identifying neonatal pain. These findings will inform the development of a continuous, unbiased, brain-based, nurse-in-the-loop Pain Recognition Automated Monitoring System (PRAMS) for neonates and infants., (Copyright © 2024 by The National Association of Neonatal Nurses.)

Background: Neuropathic pain medications are included in multimodal postoperative analgesic strategies, but quality of perioperative pain is rarely assessed., Aims: The purpose of this study was to describe adolescents' pain experiences after thoracoscopic pectus excavatum repair (Nuss procedure) using the Adolescent Pediatric Pain Tool., Design: This prospective descriptive longitudinal study was designed to test the hypothesis that pain quality descriptors reported are consistent with neuropathic pain., Methods: A convenience sample of 23 adolescents aged 12 to 17 years from a single urban, university affiliated, nonprofit children's hospital consented to self-report pain using the Adolescent Pediatric Pain Tool before and during hospitalization, and up to 14 months after Nuss procedure. Visual analytic techniques were used to analyze reported pain intensity, location, and affective, evaluative, sensory, and temporal qualities., Results: Postoperative pain quality, intensity, number of sites, and surface area decreased over time. Word clouds illustrated that neuropathic sensory and temporal pain quality descriptors increased in frequency 2 to 6 weeks after surgery and were the predominant descriptors 2 to 4 months after surgery. Dot matrix charts illustrated an inconsistent relationship of pain quality and intensity with pain surface area., Conclusions: Pain quality should be assessed with valid, reliable, and developmentally appropriate tools. Visual analytics help illustrate pain quality at single points in time and longitudinally and may be helpful in guiding postoperative pain treatment., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2022 American Society for Pain Management Nursing. Published by Elsevier Inc. All rights reserved.)