ALD Healthy Planet Project

X连锁肾上腺脑白质营养不良（ALD）是一种致命的过氧化物酶体疾病，其特征在于三个不同的 表型：脑ALD（cALD）、肾上腺脊髓神经病（AMN）和仅Addison's（肾上腺功能不全或 AI）。2016年，ALD被添加到推荐的统一筛选小组（RUSP），并已被 到目前为止已在36个州和华盛顿特区实施。ALD对新生儿筛查构成独特挑战 跟进虽然cALD和AI的治疗是有效和挽救生命的，但它们只有在那里才能实施。 是疾病的证据在cALD的情况下，干预的窗口很窄， 到疾病的最早阶段。这带来了一个重大挑战--通过新生儿筛查确定的男孩 在整个童年时期，必须通过一系列脑成像和内分泌测试来密切跟踪。 迫切需要一个严格的自动化系统来跟踪建议的遵守情况， 后续测试。我们将利用我们现有的全球罕见病临床研究网络（RDCRN） 脑白质营养不良倡议临床试验网络（GLIA-CTN; U 54 TR 002823）部署该系统并测量 其功效。在目标1中，CHOP健康星球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、斯坦福大学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 结果是一个功能性的ALD监测仪表板和一个开放获取的手册，说明如何使用这种方法， 更广泛地实施到其他网站。在目标2中，我们将建立数据收集管道， 数据驱动的监测准则。利用GLIA内现有的电子健康记录程序- CTN，我们将建立数据管道，以集中监测ALD。我们将收集和整理数据 在GLIA-CTN实施站点中。预期的结果是一个精心策划的数据管道和生成 试点数据，以了解现有的监测指南是否适用于新生儿筛查后ALD 人口预期成果是生成必要的数据收集工具，以建立基于证据的 新生儿筛查指南。 虽然ALD领域已经通过早期检测和突破性治疗而改变，但仍存在一个 对系统的迫切、未满足的需求，以监测长期监测需求的遵守情况。这条管道可以支持 症状前自然史研究，并建立数据驱动的护理标准监测指南。 拟议的工作是临床必要的，并将有助于支持未来的假设驱动的工作。这 战略有可能改善卫生公平，确保获得保健的机会减少不等于 降低安全性和监测。此外，一般的方法可以适用于其他新生儿筛查 需要复杂纵向监测的项目和罕见疾病。

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.