A Multimodal Approach for Monitoring Prolonged Acute Pain in Neonates

监测新生儿长期急性疼痛的多模式方法

• 批准号: 9979265
• 负责人: Yu Sun
• 金额: $ 18.18万
• 依托单位: UNIVERSITY OF SOUTH FLORIDA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-16 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9979265
• 关键词: Acute Pain; Affect; Agitation; Analgesics; Appearance; Behavioral; Belief; Brain; Caregivers; Caring; Chronic Disease; Classification; Clinical Research; Communities; Computational algorithm; Computer Assisted; Computer Vision Systems; Crying; Data Collection; Descriptor; Development; Developmental Disabilities; Diagnosis; Discipline of Nursing; Documentation; Emotions; Enrollment; Ensure; Evaluation; Expert Systems; Exposure to; Face; Facial Expression; Faculty; Failure; Family; Frequencies; General Hospitals; Goals; Health; Hospitalization; Human; Incidence; Individual; Infant; Infant Care; Lead; Life; Machine Learning; Medicine; Methods; Mission; Modality; Modeling; Monitor; Motion; Neonatal; Neonatal Intensive Care Units; Neonatal Nursing; Nervous System Trauma; Notification; Nurses; Nursing Research; Opiate Addiction; Optics; Outcome; Output; Pain; Pain Assessment Tool; Pain Measurement; Pain management; Paper; Pattern; Performance; Physiological; Postoperative Pain; Procedures; Psychometrics; Reader; Research Personnel; Research Project Grants; Risk; Sedation procedure; Signal Transduction; Standardization; Statistical Data Interpretation; Structure; Supervision; Surveys; System; Teaching Hospitals; Testing; Time; Training; Variant; Video Recording; Visual; Vulnerable Populations; Work; algorithm development; base; care burden; care outcomes; care providers; cerebral hemodynamics; college; computer science; convolutional neural network; coping; design; experience; improved; improved outcome; innovation; learning classifier; multimodality; neonatal care; neonate; novel; pain score; patient population; preterm newborn; procedural pain; professor; recurrent neural network; response; side effect; sound; statistics; tool

Most hospitalized infants experience painful procedures as a part of their care. Repeated or prolonged exposure to pain during early life is associated with permanent changes in brain structure and function and may lead to behavioral and developmental disabilities. Caregivers recognize the need to treat pain in infants but may be reluctant to administer analgesics because of potential short- and long-term side effects. Assessing pain in this vulnerable patient population is difficult as their responses are nonspecific and vary based on developmental stage. This difficulty may lead to both over- and under-treatment of pain, placing infants at risk for permanent neurologic injury. We propose to develop models and computer algorithms to automatically and continuously assess pain in neonates based on behavioral and physiological pain indicators. To achieve this goal, the proposal has two aims: aim 1- Continuously and synchronously record contextual, behavioral and physiological signals from neonates in the NICU while they are undergoing prolonged acute pain; aim 2 - developing a multimodal system for assessing postoperative pain in neonates. The proposed system would generate continuous and standardized pain scores comparable to those obtained by conventional nurse-derived pain scores. It would improve care outcomes by enhancing the assessment of pain while decreasing the burden of pain documentation. It can also eliminate issues of inter-rater reliability associated with conventional neonatal pain assessment. The proposed data collection plan was designed based on our preliminary results and statistical analysis. The proposed system will be evaluated using standard performance metrics, such as accuracy, precision, recall, Cohen’s kappa coefficient, or their variations. The interdisciplinary team includes a neonatologist, Dr. Ho, who is a faculty of both USF College of Medicine and College of Nursing. She practices and oversees the clinical research projects at the Tampa General Hospital NICU (the teaching hospital for USF Health). She ensures the team’s access to the NICU patient population for enrollment. She supervises the performance of the study including nursing pain assessment, video recording, and data collection. The team also includes a professor in nursing who specializes in neonatal opioids dependence and pain, an experienced research nurse, two computer science professors, and one statistics professor. The proposed work aligns very well with the NINR’s mission “to promote and improve the health of individuals, families, and communities.” The proposed neonatal pain assessment approach will provide a tool for nurses to assess pain continuously and more accurately. Pain is one of the critical factors affecting the brain development in neonates, especially in preterm neonates, with chronic illnesses. The incidence of perterm neonates exposed to pain in high and pain assessment and management remain a primary focus of neonatal nursing research. For infants in the NICU coping with pain, the proposed nursing research will enable proper pain treatment that will also improve their long-term outcomes and development.

大多数住院的婴儿经历了痛苦的程序，作为他们护理的一部分。重复或长时间暴露 早年疼痛与大脑结构和功能的永久变化有关，可能导致 行为和发育障碍。看护人认识到需要治疗婴儿疼痛，但可能是 由于潜在的短期和长期副作用，不愿给予镇痛药。评估疼痛 脆弱的患者人数很难，因为他们的反应是非特异性的，并且根据发育而有所不同 阶段。这种困难可能导致疼痛过度和治疗不足，使婴儿有永久性的风险 神经损伤。我们建议开发模型和计算机算法以自动，连续 根据行为和身体疼痛指标评估新生儿的疼痛。 为了实现这一目标，该提案有两个目标：目标1-连续和同步记录 NIC的上下文，行为和物理信号在延长的情况下。 急性疼痛；目标2-开发一种多模式系统，用于评估新生儿术后疼痛。提议 系统将产生连续和标准化的疼痛评分，可与常规的疼痛评分相当 护士衍生的疼痛评分。通过增强疼痛的评估，可以改善护理结果 减少疼痛文档的燃烧。它还可以消除与评估者间可靠性相关的问题 常规的新生儿疼痛评估。拟议的数据收集计划是根据我们的 初步结果和统计分析。建议的系统将使用标准性能进行评估 指标，例如准确性，精确度，召回，Cohen's Kappa Financial或它们的变化。 跨学科团队包括新生儿学家Ho博士，他是USF学院的学院 医学和护理学院。她练习并监督坦帕的临床研究项目 NICU综合医院（USF Health教学医院）。她确保团队访问NICU 患者人数入学。她监督研究的表现，包括护士疼痛 评估，视频记录和数据收集。该团队还包括一位专门的护士教授 在新生儿阿片类药物依赖和疼痛中，一位经验丰富的研究护士，两位计算机科学教授， 和一位统计教授。拟议的工作与Ninr的使命非常吻合，以促进和 改善个人，家庭和社区的健康。”拟议的新生儿疼痛评估方法 将为护士提供一种工具，以不断，更准确地评估疼痛。疼痛是关键因素之一 影响新生儿的大脑发育，尤其是在早产新生儿，患有慢性疾病。 在高疼痛和疼痛评估和管理中暴露于疼痛的per骨新生儿的发病率仍然是主要的 新生儿护士研究的重点。对于NICU应对疼痛的婴儿，拟议的护士研究将 启用适当的疼痛治疗，还将改善其长期结局和发展。