ALD Healthy Planet Project

X连锁肾上腺脑白质营养不良(ALD)是一种致命的过氧素体疾病，其特征是有三种不同的 表型：脑性阿尔茨海默病(CALD)、肾上腺髓神经病(AMN)和单纯Addison病(肾上腺功能不全或 AI)。2016年，ALD被添加到建议的统一筛查小组(RUSP)，并已 迄今已在36个州和华盛顿特区实施。ALD在新生儿筛查中构成了独特的挑战 继续跟进。虽然Cald和AI的治疗是有效的和挽救生命的，但它们只在那里实施一次 是与疾病有关的证据。在Cald的案例中，有一个有限的干预窗口 直到疾病的最早阶段。这造成了一个重大的挑战--通过新生儿筛查来识别男孩 在整个童年时期，必须通过连续的脑成像和内分泌学测试来密切跟踪。 迫切需要一个严格的自动化系统来跟踪建议的合规性 后续测试。我们将利用我们现有的全球罕见疾病临床研究网络(RDCRN) 脑白质营养不良主动临床试验网络(GILA-CTN；U54TR002823)部署该系统和测量 它的功效。在《目标1》中，CHOP Healthy Planets EPIC编程团队最初开发了ALD 与CHOP脑白质营养不良卓越中心合作的监测仪表板将与当地 亚特兰大儿童医院、斯坦福儿童健康和肯尼迪·克里格研究所的EPIC程序员， 共享代码并促进CHOP系统适应本地EPIC环境。预期中的 成果是一个有效的ALD监测仪表板和一本关于这种方法如何实现的开放获取手册 更广泛地实施到更多的地点。在目标2中，我们将建立数据收集管道，以建立 数据驱动的监控指南。利用Glia内现有的电子健康记录计划- CTN，我们将建立ALD监测集中的数据管道。我们将收集和整理这些数据 跨越GILA-CTN实施地点。预期的结果是一个经过策划的数据管道和生成 了解现有监测指南是否适合新生儿后筛查ALD的试点数据 人口。预期结果是产生建立以证据为基础的必要的数据收集工具 后新生儿筛查指南。 虽然ALD领域已经通过早期发现和开创性的治疗而改变，但有一种 迫切的、未得到满足的系统需求，以监测长期监测需求的遵守情况。这条管道可以支持 症状前自然病史研究和建立数据驱动的护理监测标准指南。 拟议的工作在临床上是必要的，并将有助于支持未来以假说为导向的工作。这 战略有可能改善卫生公平，确保减少获得护理的机会不等于 减少了安全和监控。此外，通用方法也可适用于其他新生儿筛查。 项目和具有复杂纵向监测需求的罕见疾病。

X-linked adrenoleukodystrophy (ALD) is a fatal peroxisomal disorder characterized by three distinct phenotypes: cerebral ALD (cALD), adrenomyeloneuropathy (AMN), and Addison’s only (adrenal insufficiency or AI). In 2016, ALD was added to the Recommended Uniform Screening Panel (RUSP), and it has been implemented to date in 36 states and Washington DC. ALD poses a unique challenge in newborn screening follow up. While treatments for cALD and AI are effective and life-saving, they are only implemented once there is evidence of disease involvement. In the case of cALD, there is a narrow window for intervention that is limited to the earliest phase of disease. This has created a major challenge—boys identified via newborn screening must be intensely followed throughout childhood by serial brain imaging and by endocrinologic testing. There is an urgent unmet need for a rigorous automated system to track compliance with recommended follow up testing. We will leverage our existing Rare Diseases Clinical Research Network (RDCRN) Global Leukodystrophy Initiative Clinical Trials Network (GLIA-CTN; U54TR002823) to deploy this system and measure its efficacy. In Aim 1, the CHOP Healthy Planets EPIC programming team, who originally developed the ALD monitoring dashboard in collaboration with the CHOP Leukodystrophy Center of Excellence will work with local EPIC programmers at Children’s Hospital of Atlanta, Stanford Children’s Health, and Kennedy Krieger Institute, sharing code and facilitating the adaptation of the CHOP system to local EPIC environments. The expected outcome is a functional ALD monitoring dashboard and an open-access manual on how this approach can be more broadly implemented to additional sites. In Aim 2, we will establish the data collection pipeline to establish data-driven monitoring guidelines. Leveraging the existing electronic health records program within the GLIA- CTN, we will establish the data pipeline for centralization of ALD-monitoring. We will collect and curate the data across the GLIA-CTN implementation sites. The expected outcome is a curated data pipeline and generation of pilot data to understand if existing monitoring guidelines are appropriate for a post newborn screening ALD population. The expected outcome is generation of data collection tool necessary to establish evidence-based post newborn screening guidelines. While the ALD field has been transformed by early detection and ground-breaking therapies, there is an urgent, unmet need for system to monitor compliance with long term monitoring needs. This pipeline can support presymptomatic natural history studies and establish standard of care monitoring guidelines that are data-driven. The proposed work is both clinically necessary and will help to support future hypothesis-driven work. This strategy has the potential to improve health equity, ensuring that decreased access to care does not equal decreased safety and monitoring. Additionally, the general approach can be adapted to other newborn screening programs and rare disorders with complex longitudinal monitoring needs.