Project 1: Longitudinal Natural History Studies of Leukodystrophies

Summary/Abstract for Project 1: a Longitudinal Natural History Study in the Leukodystrophies The Longitudinal Natural History Study aims to understand the clinical course of leukodystrophy and to monitor treatment and outcome. Significant advances in therapies and available testing have altered the natural history population from one of symptomatic individuals to one of presymptomatic, symptomatic, and post-treatment individuals. These advances have created cohorts with unique research needs: natural history data for disorders without therapeutic options, better understanding of presymptomatic individuals needing intervention, and treatment-associated disease monitoring. Aim 1 will validate meaningful clinical outcome assessments (COA), in alignment with patient priorities. To identify COA, concepts of Interest for defined disorders will be assessed through both quantitative and qualitative work with patient support groups and clinician experts. Patient-centric COA will be administered remotely to validate alternate methods of administration for applicable COAs and decrease the limitations of in-person COA. Aim 2 will define determinates of health outcomes for presymptomatic children benefiting from newborn screening (NBS). An electronic health record (EHR) approach will be used to identify pre-symptomatic individuals and track disease-specific visits, labs, and procedures/imaging. Due to known compliance concerns in NBS, we will also use the EHR to measure core health events that may affect health outcomes and compliance. We expect these aims to allow us to deploy NBS monitoring approaches appropriate in a real-world setting. Aim 3 will assess health trajectories via EHR data to monitor leukodystrophies in real-world settings. The extraction of early disease data from medical records is currently limited to a time-consuming manual process which is impractical across many sites and disorders, thus an automated assessment of neurologic trajectories methods using EHR data will be applied. We will use control groups with manually-extracted data and apply automated EHR processes for both structured EHR fields as well as free-text fields through the use of functional scores. We expect these aims to refine our ability to enrich large data sets across a growing number of clinical sites. This project will prepare the leukodystrophy community for changes in the therapeutic landscape. We align each aim with clear dissemination approaches for our numerous stakeholders, including our patient partners, to create a dynamic environment to create therapeutic advances for these rare diseases.