Cerebral Palsy Risk Identification System

脑瘫风险识别系统

• 批准号: 9769890
• 负责人: JAMES P O'HALLORAN
• 金额: $ 26.11万
• 依托单位: NEUROCOMP SYSTEMS, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9769890
• 关键词: 3-Dimensional; Accelerometer; Acute; Adoption; Advisory Committees; Algorithms; Architecture; Area; Automation; Bayesian Network; Biological Markers; Birth; Brain; Budgets; Cephalic; Cerebral Palsy; Child; Childhood; Classification; Clinical; Clinical assessments; Collaborations; Consensus; Country; Data; Data Compromising; Data Set; Databases; Development; Developmental Disabilities; Diagnosis; Diagnostic; Disease; Drops; Electronic Health Record; Enrollment; Environment; Equipment; Evaluation; Frequencies; Generations; Gestational Age; Goals; Health care facility; Healthcare Systems; Incidence; Income; Infant; Legal patent; Machine Learning; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Methods; Modality; Modeling; Monitor; Motor; Movement; Multicenter Trials; Muscle Cramp; National Institute of Child Health and Human Development; National Institute of Neurological Disorders and Stroke; Neonatology; Optics; Outcome; Patients; Pattern; Pediatrics; Performance; Phase; Physically Handicapped; Premature Birth; Premature Infant; Prevalence; Production; Progress Reports; Provider; Reporting; Research; Research Personnel; Resolution; Risk; Sample Size; Sampling; Sensitivity and Specificity; Signal Transduction; Specialist; Strategic Planning; Stroke; Structure; System; Technology; Time; Training; Transportation; Ultrasonography; United States; Validation; Video Recording; Weight; Wireless Technology; base; clinical practice; comparative; computerized; computerized data processing; cost; critical period; data modeling; data sharing; digital; experience; field study; follow-up; heuristics; high risk; improved; indexing; limb movement; miniaturize; new technology; off-patent; perinatal brain; postnatal period; primary outcome; prospective; prototype; software systems; success; tool; wireless communication

PROJECT SUMMARY AND ABSTRACT [ Pediatric specialists are often required to identify infants who are likely to suffer poor neurodevelopmental outcome, including Cerebral Palsy (CP). CP is the most common developmental disability among children in the United States and results from several factors, including low weight for gestational age, premature birth, and stroke. Although MRI and cranial ultrasound (cUS) provide valuable structural information in the preterm period, they have moderate sensitivity to CP and require transportation of the infant. Over the past 20 years, numerous studies have validated the clinical potential of General Movement Assessment (GMA) for CP risk identification. During the early period, (23 weeks to 36 weeks gestational age), the presence of Cramped Synchronized General Movements (CSGMs), has demonstrated very high sensitivity and specificity for CP, conjointly ranging from 80%-98%. CSGMs are assessed while preterm infants are still in an acute care facility (NICU) and can inform the clinician independently, and in combination with cUS and MRI. Despite its potential, GMA is available in only a few clinical centers, as adoption and routine application depend on lengthy, cost-intensive observation and availability of specially trained raters. A Cerebral Palsy Risk Identification System (CPRIS) is proposed that will automate GMA for bedside evaluations in both preterm and postterm periods. The CPRIS constitutes a key enabling technology not only for routine risk identification, but also for establishing disease trajectory and potentially differentiating CP subtypes and assessing efficacy of emerging treatments along the early developmental continuum. Preliminary studies at UC Irvine have demonstrated that GMA analysis for CSGMs can be automated by quantifying infant limb movement using highly miniaturized, 3-axis wireless accelerometers and classifying CSGMs using a patented Markov-type approach that merges an application-specific Erlang-Cox state transition model with a Dynamic Bayesian Network (“EC-DBN”), treating instantaneous machine learning classification values as observations and explicitly modeling CSGM (and non-CSGM) duration and interval. In Phase I, this approach will be utilized in a comparative evaluation of two movement measurement modalities to determine which has the best overall performance and clinical utility at three leading NICU centers. Infant movement data will be concurrently acquired using an advanced, second generation prototype wireless accelerometer system (CPRIS-A) and a high definition 3D (infrared) optical camera (CPRIS-O). The optical modality offers significant potential advantages as it requires no infant contact and can monitor unattended, intermittently, over weeks or months. However, its potential for GMA automation must be systematically evaluated. Classifier results from both modalities will be compared to expert rater consensus in 80 preterm infants. The primary outcome will be CSGM identification accuracy, as determined by ROC-AUC analyses, with a threshold for success of 0.85. Additional comparative performance measures include reliability and practicability in the NICU environment. An Advisory Committee of experts in the fields of neonatology, pediatrics and cerebral palsy will evaluate project results and advise on the clinical potential of each modality. ]

项目总结和摘要 [儿科专家经常被要求识别可能患有神经发育不良的婴儿， 结果，包括脑瘫（CP）。脑瘫是美国儿童中最常见的发育障碍。 国家和结果的几个因素，包括低体重胎龄，早产，中风。虽然 MRI和颅超声（cUS）提供了有价值的结构信息，在早产期，他们有中度 对CP敏感，需要运输婴儿。在过去的20年里，许多研究已经证实了 一般运动评估（GMA）用于CP风险识别的临床潜力。早期（23周） 至36周胎龄），出现痉挛性同步全身运动（CSGMs）， 显示出非常高的敏感性和特异性CP，联合范围从80%-98%。评估CSGM 而早产儿仍在急性护理设施（NICU）中，并且可以独立地通知临床医生， 结合cUS和MRI。尽管有潜力，但GMA仅在少数临床中心可用， 常规应用取决于长时间、高成本的观察和受过专门培训的评定员的可用性。脑 麻痹风险识别系统（CPRIS）的建议，将自动GMA的床边评估，在早产儿 和后期。CPRIS不仅是常规风险识别的关键技术， 用于建立疾病轨迹和潜在地区分CP亚型以及评估新出现的 沿着早期发育连续体的治疗。 加州大学欧文分校的初步研究表明，CSGMs的GMA分析可以通过以下方式自动化： 使用高度小型化的3轴无线加速度计量化婴儿肢体运动，并对CSGM进行分类 使用专利的马尔可夫型方法，将特定于应用的Erlang-Cox状态转换模型与 动态贝叶斯网络（“EC-DBN”），将瞬时机器学习分类值视为 观察和明确建模CSGM（和非CSGM）持续时间和间隔。在第一阶段，这种方法将 在两种运动测量模式的比较评估中使用，以确定哪一种具有最佳的整体性能 在三个领先的NICU中心的性能和临床效用。将同时采集婴儿运动数据 使用先进的第二代原型无线加速度计系统（CPRIS-A）和高清3D （红外）光学相机（CPRIS-O）。光学模态提供了显着的潜在优势，因为它不需要婴儿 联系，并可以监测无人值守，间歇性，超过几周或几个月。然而，它在全球海洋环境状况评估自动化方面的潜力 必须进行系统评估。将两种模式的分类器结果与专家评定者共识进行比较 80名早产儿。主要结局将是CSGM识别准确性，由ROC-AUC确定 分析，成功阈值为0.85。其他比较性能指标包括可靠性和 在NICU环境中的应用一个由儿科学、儿科学和医学领域专家组成的咨询委员会， 脑性麻痹将评估项目结果，并就每种模式的临床潜力提出建议。]