Framework for Multistakeholder Patient Registries in the Field of Rare Diseases: Focus on Neurogenetic Diseases

Abstract

Progress in genetic diagnosis and orphan drug legislation has opened doors to new therapies in rare neurogenetic diseases (RNDs). Innovative therapies such as gene therapy can improve patients' quality of life but come with academic, regulatory, and financial challenges. Registries can play a pivotal role in generating evidence to tackle these, but their development requires multidisciplinary knowledge and expertise. This study aims to develop a practical framework for creating and implementing patient registries addressing common challenges and maximizing their impact on care, research, drug development, and regulatory decision making with a focus on RNDs. A comprehensive 3-step literature and qualitative research approach was used to develop the framework. A qualitative systematic literature review was conducted, extracting guidance and practices leading to the draft framework. Subsequently, we interviewed representatives of 5 established international RND registries to add learnings from hands-on experiences to the framework. Expert input on the draft framework was sought in digital multistakeholder focus groups to refine the framework. The literature search; interviews with 5 registries; and focus groups with patient representatives (n = 4), clinicians (n = 6), regulators, health technology assessment (HTA) bodies and payers (n = 7), industry representatives (n = 7), and data/information technology (IT) specialists (n = 5) informed development of the framework. It covers the interests of different stakeholders, purposes for data utilization, data aspects, IT infrastructure, governance, and financing of rare disease registries. Key principles include that data should be rapidly accessible, independent, and trustworthy. Governance should involve multiple stakeholders. In addition, data should be highly descriptive, machine-readable, and accessible through a shared infrastructure and not spread over multiple isolated repositories. Sustainable and independent financing of registries is deemed important but remains challenging because of a lack of widely supported funding models. The proposed framework will guide stakeholders in establishing or improving rare disease registries that fulfill requirements of academics and patients as well as regulators, HTA bodies, and commercial parties. There is a need for more clarity regarding quality requirements for registries in regulatory and HTA context. In addition, independent financing models for registries should be developed, as well as well-defined policies on technical uniformity in health data.

Figure 1. Methodological Flowchart

The framework was drafted based on a qualitative literature review and authors' experiences (stage 1), followed by semistructured interviews with existing registries (stage 2). Multistakeholder expert focus groups were done to refine the framework. HD = Huntington disease; INPDR = International Niemann-Pick Disease Registry; IT = information technology; SCA = spinocerebellar ataxias; VWM = Vanishing White Matter.

Figure 2. Schematic Summary of the Framework for Rare Disease Registries

PROM = patient-reported outcome measure.

Figure 3. Schematic Visualization of the Gradient of Stakeholders' Influence on Collected Data

All stakeholders can be involved in the selection of the collected data in a registry. Their degree of influence depends on the layer of the data. Patients and their advocates should be a central interlocutor in establishing the registry's purposes and data. Physicians with medical and academic expertise can translate patient wishes into relevant outcome measurements/tools and research questions. Regulatory and HTA bodies can formulate research questions and express their data needs. Data stewards and data managers can define data elements for the design of the database. Involving IT specialists at the time of data element establishment is crucial, and for the ontological modelling and computer readability of data elements, input from semantic modelers is needed. HTA = health technology assessment; IT = information technology.