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

基本信息

批准号：
10218273
负责人：
Yu Sun
金额：
$ 21.88万
依托单位：
UNIVERSITY OF SOUTH FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-16 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10218273
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

项目摘要

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.

大多数住院婴儿在护理过程中都会经历痛苦的过程反复或长时间接触。生命早期的疼痛与大脑结构和功能的永久性变化有关，并可能导致护理人员认识到治疗婴儿疼痛的必要性，但可能是这样。由于潜在的短期和长期副作用，不愿意使用镇痛药。弱势患者群体是困难的，因为他们的反应是非特异性的，并且根据发育情况而变化这种困难可能会导致疼痛治疗过度或不足，使婴儿面临永久性的风险。我们建议开发模型和计算机算法来自动连续地治疗。根据行为和生理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- 持续同步记录新生儿重症监护病房 (NICU) 中的新生儿在经历长时间的护理时，会收到环境、行为和生理信号急性疼痛；目标 2 - 开发评估新生儿术后疼痛的多模式系统。系统将生成连续的标准化疼痛评分，与传统方法获得的评分相当护士得出的疼痛评分将通过增强疼痛评估来改善护理结果。它还可以减轻与疼痛记录相关的评估者间可靠性问题。传统的新生儿疼痛评估是根据我们的数据设计的。初步结果和统计分析将使用标准性能进行评估。指标，例如准确度、精确度、召回率、科恩卡帕系数或其变体。这个跨学科团队包括新生儿科医生 Ho 博士，他是 USF 学院和她在坦帕医学院实践并监督临床研究项目。综合医院新生儿重症监护室（USF Health 的教学医院）她确保团队能够进入新生儿重症监护室。她负责监督研究的实施情况，包括护理疼痛。该团队还包括一名护理专家教授。在新生儿阿片类药物依赖和疼痛方面，一名经验丰富的研究护士、两名计算机科学教授、和一位统计学教授提出的工作与 NINR 的使命“促进和促进”非常吻合。 “改善个人、家庭和社区的健康。”提议的新生儿疼痛评估方法。将为护士提供持续且更准确地评估疼痛的工具。疼痛是关键因素之一。影响患有慢性疾病的新生儿的大脑发育，尤其是早产儿。足月新生儿暴露于疼痛的发生率较高，疼痛评估和管理仍然是首要任务新生儿护理研究的重点针对新生儿重症监护病房(NICU)中婴儿应对疼痛的情况，拟议的护理研究将进行适当的疼痛治疗，这也将改善他们的长期结果和发展。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Automatic Multimodal Assessment of Neonatal Pain

新生儿疼痛的自动多模式评估

DOI：
10.1109/jbhi.2015.2458965
发表时间：
2024-09-14
期刊：
IEEE Journal of Biomedical and Health Informatics
影响因子：
7.7
作者：
Ghada Zamzmi
通讯作者：
Ghada Zamzmi

Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation.

用于新生儿术后疼痛估计的注意力生成多模态网络。

DOI：
发表时间：
2022-09
期刊：
影响因子：
0
作者：
Salekin, Md Sirajus;Zamzmi, Ghada;Goldgof, Dmitry;Mouton, Peter R;Anand, Kanwaljeet J S;Ashmeade, Terri;Prescott, Stephanie;Huang, Yangxin;Sun, Yu
通讯作者：
Sun, Yu

Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment.

用于新生儿术后疼痛评估的多模态时空深度学习方法。