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 [经常需要小儿专家来识别可能遭受神经发育不良的婴儿 结局，包括脑瘫（CP）。 CP是联合儿童中最常见的发育残疾 国家和来自几个因素的结果，包括胎龄，早产和中风的重量低。虽然 MRI和颅骨超声（CUS）在早产时提供了宝贵的结构信息，它们具有现代 对CP的敏感性，需要婴儿的运输。在过去的20年中，许多研究证实了 CP风险识别的一般运动评估（GMA）的临床潜力。在早期（23周） 到36周的胎龄），存在狭窄同步一般运动（CSGM）的存在 表现出对CP的敏感性和特异性非常高，共同点为80％-98％。评估CSGM 尽管早产儿仍在急性护理机构（NICU），并且可以独立通知临床，并且 结合CUS和MRI。尽管具有潜力，但GMA仅在几个临床中心就可以使用，作为采用和 常规申请取决于冗长，成本密集的观察和经过特殊培训的评估者的可用性。脑 提出了Palsy风险识别系统（CPRIS），该系统将自动化GMA，以进行两个早产的床边评估 和后期期。 CPRIS不仅是常规风险识别，而且还构成启用技术的关键，而且还构成 用于建立疾病轨迹并潜在地区分CP亚型并评估出现的效率 沿着早期发育连续体的治疗。 UC Irvine的初步研究表明，CSGM的GMA分析可以通过 使用高度微型的3轴无线加速度计和CSGMS分类来量化婴儿肢体运动 使用获得专利的马尔可夫型方法，该方法将特定于应用的Erlang-Cox状态过渡模型合并 动态的贝叶斯网络（“ EC-DBN”），将瞬时机器学习分类值视为 观测和明确对CSGM（和非CSGM）持续时间和间隔进行建模。在第一阶段，这种方法将是 用于对两种运动测量方式的比较评估，以确定哪个具有最佳总体 三个领先的NICU中心的性能和临床实用性。婴儿运动数据将同时获得 使用高级，第二代原型无线加速度计系统（CPRIS-A）和高清3D （红外）光相机（CPRIS-O）。光学方式具有重要的潜在优势，因为它不需要婴儿 接触并可以间歇性地监视无人看管的数周或数月。但是，它具有GMA自动化的潜力 必须进行系统评估。将两种方式的分类器结果与专家共识进行比较 在80位早产儿中。主要结果将是由ROC-AUC确定的CSGM识别精度 分析，成功的阈值为0.85。其他比较绩效指标包括可靠性和 NICU环境中的实践性。新生儿，儿科和 脑瘫将评估项目结果并就每种方式的临床潜力提供建议。