Neonatal & Pediatric Intensive Care Unit, Brest University Hospital, University of Western Brittany, Brest, France
Shamir Medical Center (Assaf Harofeh), Neonatal Intensive Care Unit, Tel Aviv University Sackler Faculty of Medicine, Tel Aviv, Israel.
BMJ Open. 2021 Jan 6;11(1):e039292. doi: 10.1136/bmjopen-2020-039292.
Objective pain assessment in non-verbal populations is clinically challenging due to their inability to express their pain via self-report. Repetitive exposures to acute or prolonged pain lead to clinical instability, with long-term behavioural and cognitive sequelae in newborn infants. Strong analgesics are also associated with medical complications, potential neurotoxicity and altered brain development. Pain scores performed by bedside nurses provide subjective, observer-dependent assessments rather than objective data for infant pain management; the required observations are labour intensive, difficult to perform by a nurse who is concurrently performing the procedure and increase the nursing workload. Multimodal pain assessment, using sensor-fusion and machine-learning algorithms, can provide a patient-centred, context-dependent, observer-independent and objective pain measure.
In newborns undergoing painful procedures, we use facial electromyography to record facial muscle activity-related infant pain, ECG to examine heart rate (HR) changes and HR variability, electrodermal activity (skin conductance) to measure catecholamine-induced palmar sweating, changes in oxygen saturations and skin perfusion, and electroencephalography using active electrodes to assess brain activity in real time. This multimodal approach has the potential to improve the accuracy of pain assessment in non-verbal infants and may even allow continuous pain monitoring at the bedside. The feasibility of this approach will be evaluated in an observational prospective study of clinically required painful procedures in 60 preterm and term newborns, and infants aged 6 months or less.
The Institutional Review Board of the Stanford University approved the protocol. Study findings will be published in peer-reviewed journals, presented at scientific meetings, taught via webinars, podcasts and video tutorials, and listed on academic/scientific websites. Future studies will validate and refine this approach using the minimum number of sensors required to assess neonatal/infant pain.
ClinicalTrials.gov Registry (NCT03330496).
由于无法通过自我报告来表达疼痛，因此对于无法言语的人群，客观的疼痛评估在临床上具有挑战性。新生儿反复暴露于急性或长期疼痛中会导致临床不稳定，继而出现长期的行为和认知后遗症。强效镇痛药也会引起医疗并发症、潜在神经毒性和大脑发育改变。床边护士进行的疼痛评分提供的是主观的、依赖观察者的评估，而不是婴儿疼痛管理的客观数据；所需的观察既费力又耗时，同时进行操作的护士难以执行，并且会增加护理工作量。使用传感器融合和机器学习算法的多模态疼痛评估可以提供以患者为中心、依赖于上下文、独立于观察者和客观的疼痛测量。
在接受有疼痛的操作的新生儿中，我们使用面部肌电图记录与婴儿疼痛相关的面部肌肉活动，使用心电图检查心率（HR）变化和 HR 变异性，用电极皮肤活动（皮肤传导）测量儿茶酚胺引起的手掌出汗，测量氧饱和度和皮肤灌注的变化，以及使用有源电极进行脑电图以实时评估大脑活动。这种多模态方法有可能提高无法言语的婴儿疼痛评估的准确性，甚至可能允许在床边进行连续的疼痛监测。我们将通过对 60 名早产儿和足月儿以及 6 个月或以下的婴儿进行的临床必需的有疼痛的操作的观察性前瞻性研究来评估这种方法的可行性。
斯坦福大学的机构审查委员会批准了该方案。研究结果将发表在同行评议的期刊上，在科学会议上展示，通过网络研讨会、播客和视频教程进行教学，并在学术/科学网站上列出。未来的研究将使用评估新生儿/婴儿疼痛所需的最少传感器数量来验证和改进这种方法。
ClinicalTrials.gov 注册（NCT03330496）。
