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. ]

项目摘要和摘要 [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，包括脑性瘫痪(CP)。脑性瘫痪是美国儿童中最常见的发育障碍 状态和结果是由几个因素造成的，包括胎龄体重过低、早产和中风。虽然 MRI和头颅超声(Cus)在早产儿中提供了有价值的结构信息，它们有适度的 对CP敏感，需要运送婴儿。在过去的20年里，大量的研究证实了 一般运动评估(GMA)在CP风险识别中的临床潜力。在早期，(23周 胎龄到36周)，出现狭小的同步全身运动(CSGM)， CP具有很高的敏感性和特异性，联合检测的敏感度在80%-98%之间。对CSGM进行评估 虽然早产儿仍在急性护理机构(NICU)，并可以独立通知临床医生，但在 结合超声心动图和核磁共振。尽管GMA具有潜力，但只有几个临床中心可以使用，因为采用和 常规应用依赖于长时间、高成本的观察和受过专门培训的评分员的可用性。A大脑 瘫痪风险识别系统(CPRIS)被提出，将自动化GMA在两个早产儿的床边评估 和期后阶段。CPRIS不仅是常规风险识别的关键使能技术，而且也是 用于建立疾病轨迹、潜在地区分CP亚型和评估新出现的 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，CSGM的GMA分析可以通过以下方式自动进行 使用高度小型化的三轴无线加速度计量化婴儿肢体运动并对CSGM进行分类 使用获得专利的马尔可夫类型方法，该方法将特定于应用的Erlang-Cox状态转移模型与 一种动态贝叶斯网络(EC-DBN)，将瞬时机器学习分类值视为 并对CSGM(和非CSGM)持续时间和间隔进行明确建模。在第一阶段，这一方法将是 用于对两种运动测量模式进行比较评估，以确定哪种运动测量模式的整体效果最好 三个领先的NICU中心的性能和临床效用。将同时获取婴儿运动数据 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)。光学模式提供了显著的潜在优势，因为它不需要婴儿 联系，并可以监控无人值守，间歇性，在数周或数月。然而，它在GMA自动化方面的潜力 必须进行系统的评估。两种模式的分类器结果将与专家评分者的共识进行比较 在80名早产儿中。主要结果将是由ROC-AUC确定的CSGM识别准确性 分析，成功的门槛为0.85。其他可比较的业绩衡量标准包括可靠性和 在NICU环境中的实用性。新生儿科、儿科和儿科领域专家咨询委员会 脑瘫将评估项目结果，并就每种模式的临床潜力提出建议。]