From Fragmented Evidence to a Unified GCC Demyelinating Disease Registry: A Strategic Framework for MS, NMOSD, MOGAD, Optic Neuritis, LETM, and Pediatric Acquired Demyelination

1. Introduction and regional rationale

The GCC region has reached a pivotal moment in neuroimmunology. Multiple sclerosis services have expanded, high-efficacy therapies are increasingly available, magnetic resonance imaging access has improved, and regional expertise in MS and related demyelinating disorders has grown substantially. Yet the clinical and research infrastructure required to distinguish, monitor and study the full spectrum of demyelinating disease remains incomplete. The central gap is no longer simply the absence of clinicians, hospitals or therapies. It is the absence of a unified, prospective, harmonized and interoperable regional registry capable of capturing the true burden, phenotype, diagnostic pathway, treatment exposure, outcomes and long-term safety profile of demyelinating diseases across the GCC.

This article is a strategic framework and implementation roadmap for a prospective, multicountry GCC Demyelinating Disease Registry. It does not report new patient-level data and should not be interpreted as a systematic review, scoping review or clinical trial. Instead, it synthesizes regional evidence gaps, international registry principles, real-world evidence standards, implementation-science concepts and digital-health architecture into a practical proposal for a unified GCC-wide demyelinating-disease infrastructure.[44–55]

The GCC setting is particularly suitable for a regional registry because the six member states share overlapping clinical networks, referral patterns, treatment challenges, population mobility, public-private healthcare interfaces and high-cost biologic decision pressures, while also differing in population size, insurance structure, laboratory access, registry maturity and national health-information systems. A unified registry would allow the region to preserve national data sovereignty while enabling multicountry learning, benchmarking, pharmacovigilance and policy-relevant analysis.

Saudi Arabia and Kuwait have contributed important epidemiological and registry-based MS data.[2–4] Qatar and Oman have also contributed hospital-based and population-relevant MS data.[5,6] Multinational and global MS epidemiological studies confirm the increasing importance of the region within the broader global MS burden.[7,8] However, the evidence base remains uneven across countries and disease categories, with particularly limited prospective data for NMOSD, MOGAD, recurrent optic neuritis, LETM, pediatric demyelination, expatriate populations, visual outcomes, antibody-testing pathways, treatment access and long-term safety.[9–12]

The experience of smaller GCC states is instructive because it illustrates both the value and the limits of centralized clinical ascertainment. Where a national population is small and tertiary neurology services are concentrated, near-complete clinical ascertainment of prevalent MS is achievable, and repeat ascertainment exercises in such settings may suggest substantially higher contemporary case numbers than foundational national prevalence studies of a decade ago.[1] The interpretation of that difference, however, is precisely the problem this proposal seeks to solve. Apparent increases across periods spanning revisions of diagnostic criteria, expanded MRI capacity, improved survival with accumulation of the prevalence pool and changing case-finding methods cannot be attributed to changing disease occurrence without a longitudinal data structure capable of separating these contributions. Static, criteria-heterogeneous, retrospectively derived prevalence estimates cannot support the service-planning and biologic-budgeting decisions that GCC ministries are now required to make. A living registry that records criteria version, ascertainment source, diagnosis date and follow-up status prospectively can.

The diagnostic framework has moved substantially, and a registry designed now must be built to the current standard rather than to the standard used when most regional cohorts were assembled. The 2024 revisions of the McDonald criteria establish a unified diagnostic approach across the lifespan, include the optic nerve as a fifth anatomical location for dissemination in space, and permit central vein sign, paramagnetic rim lesions and cerebrospinal fluid kappa free light chain measurement to provide supportive evidence in defined situations; in specific circumstances radiologically isolated syndrome may now fulfill diagnostic criteria.[13,15] The companion 2024 MAGNIMS-CMSC-NAIMS recommendations specify the imaging acquisition and interpretation standards on which those criteria depend.[14] The 2015 International Panel for NMO Diagnosis criteria remain the foundation for NMOSD diagnosis, and the 2026 MENACTRIMS consensus recommendations provide a regional practice anchor that emphasizes cell-based AQP4-IgG testing and addresses pediatric, pregnancy and treatment-access considerations relevant to this region.[16,17] The 2023 international MOGAD criteria define MOGAD as a distinct antibody-associated disorder requiring compatible clinical and radiological features together with appropriately interpreted MOG-IgG testing.[18,19]

Two consequences follow for registry design. First, diagnostic criteria must be operationalized as structured fields rather than as free-text labels. Second, the registry must record which version of which criteria was applied to each case and on what date. Prevalence and incidence estimates generated under different MS, NMOSD or MOGAD criteria are not directly comparable, and a registry that fails to capture criteria provenance will generate apparent temporal and cross-country trends that may reflect diagnostic reclassification rather than disease occurrence. Reclassification events between MS, NMOSD, MOGAD and other opticospinal syndromes should therefore be recorded with their date, basis and criteria version.

The regional need is sharpened by recurring clinical-system gaps. Demyelinating disease care remains institutionally fragmented: MS patients may be followed in neurology or MS clinics, optic neuritis may first present to ophthalmology, LETM may enter emergency, neurology, infectious, rheumatology or spinal pathways, and pediatric demyelination may be managed through pediatric neurology, general pediatrics or tertiary referral centers. NMOSD and MOGAD may therefore be missed not because clinicians are unaware of them, but because the health system does not force structured diagnostic convergence.[16–23]

Antibody testing is not uniformly documented. AQP4-IgG and MOG-IgG results are clinically decisive, but registry-level interpretation requires more than a binary positive or negative result. Assay type, laboratory, specimen type, date of testing, timing relative to relapse or immunotherapy, titer or qualitative strength, repeat testing, and live or fixed cell-based assay status all matter. Without these fields, apparent differences in NMOSD or MOGAD frequency may reflect testing variability rather than true disease differences.[19–22]

GCC demographic complexity adds another layer. Citizens and expatriates may differ in access pathways, continuity of care, insurance status, mobility and long-term follow-up. Expatriate patients may leave the country, change employers, change insurance, or move between GCC states, leading to fragmented longitudinal records. A registry that excludes expatriates would underestimate service burden and distort the clinical reality of GCC healthcare, while a registry that includes expatriates without mobility and follow-up fields would risk incomplete outcome data.[7,8]

Pediatric demyelination is also underdeveloped in the regional evidence base. Pediatric MOGAD, acute disseminated encephalomyelitis-like presentations, relapsing optic neuritis, school performance, cognition, growth, puberty, family burden and quality of life require variables that are not captured in adult MS registries.[30–35,42,43] A pediatric module must therefore be designed from the start rather than added later.

The proposed registry does not duplicate the NMOAG registry, which demonstrated that multicentre Gulf collaboration in NMOSD is feasible and scientifically productive; it extends that experience across the demyelinating spectrum.[11] It should also be positioned deliberately within the international MS data ecosystem rather than constructed in isolation. MSBase, the Big Multiple Sclerosis Data network and the Multiple Sclerosis Data Alliance catalogue illustrate how harmonized registries can support collaborative outcomes research, common-data-model development and discovery of real-world MS data sources.[56–58] Future alignment with such networks and catalogues should be explored so that the GCC platform becomes a Gulf node in global federated learning rather than an isolated regional database.

The therapeutic landscape also strengthens the case for timely registry infrastructure. Several targeted therapies are now established for AQP4-IgG-positive NMOSD, including complement inhibition, CD19 B-cell depletion and interleukin-6 pathway therapy.[24–28] Recent phase 3 trial data in relapsing MOGAD suggest that the therapeutic landscape may soon change, with satralizumab reported to reduce relapse risk compared with placebo in the METEOROID study presented at the 2026 American Academy of Neurology meeting.[29] These emerging data should be interpreted cautiously until full peer-reviewed publication and regulatory decisions are available, but they sharpen the regional need for denominator-based epidemiology, treatment-exposure tracking and pharmacovigilance infrastructure before high-cost biologic decisions become routine.

Novelty statement: To our knowledge, this is the first framework to propose a unified, multicountry, multisyndromic GCC demyelinating-disease registry that integrates MS, NMOSD, MOGAD, recurrent optic neuritis, LETM, and pediatric acquired demyelination within a single FAIR–OMOP-compatible, federated, criteria-versioned, and precision-ready architecture. Its novelty lies not simply in calling for a registry, but in translating fragmented Gulf evidence into an operational regional blueprint that links diagnostic harmonization, antibody-testing provenance, imaging standards, pediatric outcomes, expatriate mobility, data sovereignty, pharmacovigilance, real-world treatment evaluation, and ethically governed biomarker/genetic sub-studies into one learning health-system model.

Figure 1 presents the proposed conceptual architecture of the unified GCC Demyelinating Disease Registry, illustrating the relationships between regional governance, national coordinating hubs, local clinical nodes, disease-specific modules, and the bidirectional flow of de-identified clinical data and actionable feedback across participating stakeholders.

Figure 1illustrates a federated, modular, multicountry architecture linking national coordinating hubs with hospital nodes, MS centres, pediatric neurology units, ophthalmology clinics, radiology departments, immunology laboratories, rehabilitation services, pharmacy systems and optional biobanking or biomarker units. The revised figure intentionally avoids national flags and emphasizes functional national hubs, aggregate dashboards, core disease modules and optional extended precision-neuroimmunology modules.

2. Vision, strategic objectives and clinical value

The vision is to establish the Arabian Gulf as a regional leader in precision neuroimmunology by creating a unified, ethically governed, interoperable and clinically actionable registry for demyelinating diseases across Bahrain, Saudi Arabia, the United Arab Emirates, Kuwait, Qatar and Oman. The mission is to capture high-quality longitudinal data on MS, NMOSD, MOGAD, optic neuritis, LETM and pediatric acquired demyelination; harmonize diagnostic and treatment pathways; generate real-world evidence; support equitable care; enable research collaboration; and inform policy, pharmacovigilance and clinical-trial readiness across the GCC.

The registry should pursue ten strategic objectives:

Objective 1: Improve ascertainment. The registry should identify and enroll patients with MS, AQP4-IgG-positive NMOSD, seronegative NMOSD, MOGAD, recurrent optic neuritis, LETM, double-seronegative opticospinal syndromes and pediatric acquired demyelinating syndromes.[13–23,30–35]

Objective 2: Standardize diagnosis. The registry should embed the 2024 McDonald criteria, the 2015 IPND NMOSD criteria as endorsed for regional practice by the 2026 MENACTRIMS consensus, the 2023 international MOGAD criteria and current pediatric acquired demyelinating syndrome definitions into structured registry fields, together with mandatory criteria-version, criteria-date and structured reclassification fields on every case record.[13,16–18,30]

Objective 3: Harmonize serological documentation. The registry should capture AQP4-IgG and MOG-IgG testing, including assay type, laboratory, specimen, timing, titer or qualitative strength, repeat testing and interpretation.[19–22]

Objective 4: Integrate clinical pathways. The registry should connect neurology, pediatric neurology, ophthalmology, neuroradiology, immunology laboratories, emergency care, rehabilitation, pharmacy and primary care.[44–49]

Objective 5: Support longitudinal outcome monitoring. The registry should track relapses, disability, Neurostatus-certified EDSS where feasible, visual outcomes, MRI activity, treatment response, adverse events, quality of life, school and work participation, pregnancy and survival.[37–43]

Objective 6: Capture pediatric depth. The registry should include developmental milestones, cognition, academic outcomes, growth, puberty, family burden and child-specific quality-of-life measures.[30–35,42,43]

Objective 7: Enable research readiness. The registry should support prospective cohort studies, nested case-control analyses, biomarker studies, pharmacovigilance, health-economic studies, pragmatic trials and OHDSI-compatible real-world evidence generation.[44–58]

Objective 8: Support policy influence. The registry should provide ministries, payers and professional societies with reliable evidence for service planning, biologic access, treatment sequencing, equity monitoring and pharmacovigilance.[24–29,44–49]

Objective 9: Ensure interoperability and data quality. The registry should use a REDCap-compatible, OMOP-mappable, FAIR-aligned, privacy-preserving platform suitable for federated analytics, data-quality monitoring and artificial-intelligence-ready data structures.[50–55]

Objective 10: Establish sustainability. The registry should include long-term governance, funding, authorship, publication, data-access, data-quality auditing, training and conflict-of-interest policies.[44–49]

Beyond epidemiological surveillance, the registry should be designed as a platform for ethically governed precision neuroimmunology. GCC populations include shared and partially overlapping ancestral, familial and environmental backgrounds, while also showing substantial diversity through migration and expatriate communities. Extended-family structures, consanguinity patterns, regional lifestyle transition, vitamin D deficiency, obesity and metabolic risk, infection exposure and rapidly evolving healthcare systems may all influence susceptibility, phenotype, relapse biology, disability trajectory and therapeutic response. The registry should not promise genetic discovery from registry data alone; instead, it should create the consent, phenotyping and governance infrastructure for hypothesis generation, consortium participation and approved nested biomarker, pharmacogenomic and genetic sub-studies.

Clinically, the registry should not be viewed as a passive data repository. It should become a mechanism for generating actionable knowledge across the full demyelinating-disease pathway. By linking presenting syndrome, antibody status, MRI pattern, CSF profile, OCT findings, relapse recovery, treatment exposure, pediatric outcomes and long-term disability, the registry could help define regionally relevant diagnostic pathways, referral algorithms, relapse-escalation approaches, biologic-treatment eligibility frameworks and follow-up schedules. This would make the registry useful at the bedside, not only for research and policy.

Figure 2 summarizes the overall value proposition of the proposed registry by demonstrating how harmonized registry inputs are transformed into clinically meaningful outputs that support patient care, research, pharmacovigilance, policy development, and precision neuroimmunology.

Figure 2summarizes how registry inputs such as clinical phenotype, MRI and OCT findings, AQP4-IgG and MOG-IgG status, CSF data, treatment exposure, relapse data, disability, pediatric outcomes, patient-reported outcomes, biomarkers and equity indicators can be transformed into diagnostic, clinical, research, policy and precision-management outputs.

3. Governance, minimum dataset and registry modules

The registry should be governed as a regional public-good infrastructure rather than a single-center academic database. Its authority should derive from clinical credibility, national representation, transparent governance, patient trust and technical robustness. Multicenter registries require clear data-access policies, national ownership, data-quality oversight, stakeholder participation, authorship governance and transparent conflict-of-interest management.[44–49]

In the GCC context, governance must also account for cross-border professional networks, mixed public-private care, expatriate mobility, multilingual patient communication, variable national ethics processes and the need to preserve national data sovereignty while enabling regional learning. A registry that succeeds in the Gulf must therefore be scientifically rigorous, culturally sensitive, operationally pragmatic and compatible with both national health-system priorities and regional collaboration.

The governance model should include a GCC Steering Committee, national coordinating hubs, hospital-level registry nodes, a pediatric working group, laboratory and imaging panels, a FAIR-OMOP technical board, a data governance board, a publication and conflict-of-interest committee, a patient and family advisory board, and international advisory linkage. The GCC Steering Committee would provide strategic direction, protocol approval, data standards, publication oversight, ethics alignment, conflict resolution, international partnerships and sustainability planning. National hubs would coordinate local ethics approvals, site onboarding, quality assurance, training, data monitoring, regulatory compliance and national reporting. Hospital-level nodes would include public and private hospitals, MS centers, neurology clinics, pediatric neurology units, neuro-ophthalmology clinics, radiology departments and immunology laboratories. Figure 3 illustrates the multidisciplinary stakeholder ecosystem required for successful implementation of the registry, highlighting the coordinated roles of clinicians, patients, healthcare institutions, technical experts, regulatory authorities, researchers, and policymakers in ensuring long-term sustainability.

Figure 3shows the stakeholder ecosystem required for successful implementation, including patients and families, adult and pediatric neurologists, neuro-ophthalmologists, neuroradiologists, immunology laboratories, pharmacy and rehabilitation services, hospital administrators, ministries, payers, ethics committees, academic institutions, professional societies, patient groups, technical experts and international advisors.

Table 1. Proposed governance structure and responsibilities.

Governance layer	Composition	Core responsibilities	Key outputs
GCC Steering Committee	Senior representatives from all GCC countries, registry leads, neuroimmunology experts, epidemiologists	Strategic direction, protocol approval, publication policy, regional harmonization	Master protocol, annual report, research priorities
National Coordinating Hubs	Country principal investigator, registry manager, ethics liaison, data-quality officer	Site onboarding, ethics, national reporting, training	Country dashboards, data-quality reports
Hospital Registry Nodes	Neurology, pediatric neurology, ophthalmology, radiology, immunology, pharmacy	Patient enrollment, data entry, clinical validation, follow-up	Patient-level longitudinal data
Pediatric Working Group	Pediatric neurologists, neuropsychologists, school-health experts, parents	Pediatric module design, child-specific outcomes, family burden	Pediatric dataset, child follow-up schedule
Laboratory and Imaging Panel	Immunology laboratories, neuroradiologists, OCT/neuro-ophthalmology experts	AQP4/MOG assay standards, MRI/OCT protocols, kappa free light chain and biomarker-field definitions	Testing and imaging standard operating procedures
FAIR-OMOP Technical Board	Health informaticians, data scientists, OHDSI/OMOP advisors, cybersecurity experts	Data architecture, ETL, vocabulary mapping, OMOP backend, FAIR metadata, data quality	Technical architecture, mapping plan, data-quality reports
Data Governance Board	Legal, ethics, cybersecurity and health-informatics representatives	Privacy, access, interoperability, audit trails, AI governance, disclosure-control rules	Data-access policy, security framework
Publication and COI Committee	National leads, site investigators, ethics representative, independent scientific advisor	Authorship policy, writing groups, conflict-of-interest review, industry interaction	Publication charter, COI register
Patient and Family Advisory Board	Adult patients, parents, caregivers, advocacy representatives	Consent, acceptability, communication, equity	Patient-facing materials, registry feedback
International Advisory Group	External registry, OHDSI, MSBase/Big MS Data and neuroimmunology experts	Benchmarking, scientific advice, global linkage	External review, trial-readiness guidance

Note: Governance categories are adapted from principles of multicenter registry design, real-world evidence governance, FAIR-aligned data stewardship, implementation science,patient engagement, authorship governance, conflict-of-interest management and health-data governance.[44–58] Abbreviations: AI, artificial intelligence; AQP4, aquaporin-4; COI, conflict of interest; ETL, extract-transform-load; FAIR, Findable, Accessible, Interoperable, Reusable; GCC, Gulf Cooperation Council; MOG, myelin oligodendrocyte glycoprotein; MRI, magnetic resonance imaging; OCT, optical coherence tomography; OHDSI, Observational Health Data Sciences and Informatics; OMOP, Observational Medical Outcomes Partnership.

Table 1. Proposed governance structure and responsibilities.

Governance layer	Composition	Core responsibilities	Key outputs
GCC Steering Committee	Senior representatives from all GCC countries, registry leads, neuroimmunology experts, epidemiologists	Strategic direction, protocol approval, publication policy, regional harmonization	Master protocol, annual report, research priorities
National Coordinating Hubs	Country principal investigator, registry manager, ethics liaison, data-quality officer	Site onboarding, ethics, national reporting, training	Country dashboards, data-quality reports
Hospital Registry Nodes	Neurology, pediatric neurology, ophthalmology, radiology, immunology, pharmacy	Patient enrollment, data entry, clinical validation, follow-up	Patient-level longitudinal data
Pediatric Working Group	Pediatric neurologists, neuropsychologists, school-health experts, parents	Pediatric module design, child-specific outcomes, family burden	Pediatric dataset, child follow-up schedule
Laboratory and Imaging Panel	Immunology laboratories, neuroradiologists, OCT/neuro-ophthalmology experts	AQP4/MOG assay standards, MRI/OCT protocols, kappa free light chain and biomarker-field definitions	Testing and imaging standard operating procedures
FAIR-OMOP Technical Board	Health informaticians, data scientists, OHDSI/OMOP advisors, cybersecurity experts	Data architecture, ETL, vocabulary mapping, OMOP backend, FAIR metadata, data quality	Technical architecture, mapping plan, data-quality reports
Data Governance Board	Legal, ethics, cybersecurity and health-informatics representatives	Privacy, access, interoperability, audit trails, AI governance, disclosure-control rules	Data-access policy, security framework
Publication and COI Committee	National leads, site investigators, ethics representative, independent scientific advisor	Authorship policy, writing groups, conflict-of-interest review, industry interaction	Publication charter, COI register
Patient and Family Advisory Board	Adult patients, parents, caregivers, advocacy representatives	Consent, acceptability, communication, equity	Patient-facing materials, registry feedback
International Advisory Group	External registry, OHDSI, MSBase/Big MS Data and neuroimmunology experts	Benchmarking, scientific advice, global linkage	External review, trial-readiness guidance

Note: Governance categories are adapted from principles of multicenter registry design, real-world evidence governance, FAIR-aligned data stewardship, implementation science,patient engagement, authorship governance, conflict-of-interest management and health-data governance.[44–58] Abbreviations: AI, artificial intelligence; AQP4, aquaporin-4; COI, conflict of interest; ETL, extract-transform-load; FAIR, Findable, Accessible, Interoperable, Reusable; GCC, Gulf Cooperation Council; MOG, myelin oligodendrocyte glycoprotein; MRI, magnetic resonance imaging; OCT, optical coherence tomography; OHDSI, Observational Health Data Sciences and Informatics; OMOP, Observational Medical Outcomes Partnership.

The registry should use a modular dataset. Every patient should have a core minimum dataset, while disease-specific modules activate according to phenotype and diagnosis. This prevents unnecessary burden while allowing deep phenotyping. The core dataset should include administrative data, demographics, residence, citizenship, residency or expatriate status, ethically permissible ancestry category, family history, consanguinity, environmental risk factors, index event, diagnosis, diagnostic criteria, criteria version, reclassification events, relapse history, MRI, CSF, antibody testing, OCT/visual data, treatment, disability, outcomes, adverse events, patient-reported outcomes and follow-up status.

Disease-specific modules should include MS, NMOSD, MOGAD, recurrent optic neuritis, LETM, pediatric acquired demyelination, pregnancy, vaccination/infection, biologics safety, imaging, OCT, CSF, laboratory biomarkers, genetics, patient-reported outcomes and health economics. Disease modules should be activated according to clinical phenotype and diagnostic category, but cross-module visibility should be retained because patient classification may evolve over time.[13–23]

Table 2. Proposed core minimum dataset for the unified GCC registry.

Domain	Minimum variables	Purpose
Administrative	Country, center, patient registry ID, enrollment date, consent status, ethics pathway	Governance, auditability
Demographics	Date/year of birth, sex at birth, citizenship, residency/expatriate status, ethically permissible ancestry category, residence	Epidemiology, equity, mobility, approved precision sub-studies
Social determinants	Education, employment/school status, insurance, public/private care pathway	Access and outcome analysis
Family/genetic context	Consanguinity, family history of MS/NMOSD/MOGAD/autoimmunity; genetic sub-study consent	Familial risk and optional genetics
Diagnosis	MS, AQP4-IgG-positive NMOSD, seronegative NMOSD, MOGAD, recurrent ON, LETM, pediatric ADS, uncertain	Disease classification
Criteria provenance	Criteria applied, criteria version/year, criteria date, clinician/site, basis of diagnosis	Comparability across criteria revisions
Reclassification	Initial diagnosis, revised diagnosis, date, trigger, evidence, criteria version	Tracks MS/NMOSD/MOGAD reclassification
Index event	Date, phenotype, optic neuritis, myelitis, area postrema, ADEM-like, brainstem, cortical	Phenotype mapping
Antibody testing	AQP4-IgG, MOG-IgG, assay type, laboratory, date, titer/strength, repeat testing	Serological validity
MRI	Brain, orbit, optic nerve, spine, LETM segments, conus, enhancement, brainstem, cortical lesions, central vein sign, paramagnetic rim lesions	Radiological classification under current MRI standards
CSF	WBC, protein, OCB, IgG index, infectious exclusions, kappa free light chain if available	Differential diagnosis and criteria support
OCT/visual	Visual acuity, low-contrast acuity, color vision, visual fields, RNFL, GCL/IPL, VEP where available	Visual outcome tracking and optic nerve involvement
Relapses	Date, phenotype, severity, treatment, recovery	Disease activity
Disability	EDSS, Neurostatus certification field, ambulation, visual disability, bladder/bowel, cognition	Longitudinal outcome quality
Treatment	Acute treatment, maintenance therapy, rituximab and biosimilar exposure, biologics, IVIG, start/stop dates, reason for discontinuation	Effectiveness, access and safety
Safety	Infections, infusion reactions, malignancy, pregnancy outcomes, hospitalizations, vaccination interactions	Pharmacovigilance
Biomarker/precision fields	Biospecimen availability, vitamin D, EBV status where available, NfL/GFAP assay platform and units, genetic sub-study consent	Optional precision-neuroimmunology module
Patient-reported outcomes	Quality of life, fatigue, pain, work/school impact; Arabic/English validation status or validation plan	Patient-centered care
Follow-up	Last visit, lost to follow-up, moved country, death, transition of care	Longitudinal completeness

Table 2 footnote: The proposed minimum dataset integrates contemporary diagnostic criteria for MS, NMOSD and MOGAD; current MRI recommendations; disability and visual outcome measures; antibody-testing requirements; pediatric demyelination definitions; registry-methodology principles; OMOP-mappable variable structure; and optional precision-neuroimmunology fields.[13–23,30–58].Abbreviations: ADEM, acute disseminated encephalomyelitis; ADS, acquired demyelinating syndromes; AQP4-IgG, aquaporin-4 immunoglobulin G; CSF, cerebrospinal fluid; DMT, disease-modifying therapy; EBV, Epstein-Barr virus; EDSS, Expanded Disability Status Scale; GCL/IPL, ganglion-cell layer/inner plexiform layer; GFAP, glial fibrillary acidic protein; IgG, immunoglobulin G; LETM, longitudinally extensive transverse myelitis; MOGAD, myelin oligodendrocyte glycoprotein antibody-associated disease; MOG-IgG, myelin oligodendrocyte glycoprotein immunoglobulin G; MRI, magnetic resonance imaging; MS, multiple sclerosis; NfL, neurofilament light chain; NMOSD, neuromyelitis optica spectrum disorder; OCB, oligoclonal bands; OCT, optical coherence tomography; ON, optic neuritis; RNFL, retinal nerve fiber layer; VEP, visual evoked potential.

Table 2. Proposed core minimum dataset for the unified GCC registry.

Domain	Minimum variables	Purpose
Administrative	Country, center, patient registry ID, enrollment date, consent status, ethics pathway	Governance, auditability
Demographics	Date/year of birth, sex at birth, citizenship, residency/expatriate status, ethically permissible ancestry category, residence	Epidemiology, equity, mobility, approved precision sub-studies
Social determinants	Education, employment/school status, insurance, public/private care pathway	Access and outcome analysis
Family/genetic context	Consanguinity, family history of MS/NMOSD/MOGAD/autoimmunity; genetic sub-study consent	Familial risk and optional genetics
Diagnosis	MS, AQP4-IgG-positive NMOSD, seronegative NMOSD, MOGAD, recurrent ON, LETM, pediatric ADS, uncertain	Disease classification
Criteria provenance	Criteria applied, criteria version/year, criteria date, clinician/site, basis of diagnosis	Comparability across criteria revisions
Reclassification	Initial diagnosis, revised diagnosis, date, trigger, evidence, criteria version	Tracks MS/NMOSD/MOGAD reclassification
Index event	Date, phenotype, optic neuritis, myelitis, area postrema, ADEM-like, brainstem, cortical	Phenotype mapping
Antibody testing	AQP4-IgG, MOG-IgG, assay type, laboratory, date, titer/strength, repeat testing	Serological validity
MRI	Brain, orbit, optic nerve, spine, LETM segments, conus, enhancement, brainstem, cortical lesions, central vein sign, paramagnetic rim lesions	Radiological classification under current MRI standards
CSF	WBC, protein, OCB, IgG index, infectious exclusions, kappa free light chain if available	Differential diagnosis and criteria support
OCT/visual	Visual acuity, low-contrast acuity, color vision, visual fields, RNFL, GCL/IPL, VEP where available	Visual outcome tracking and optic nerve involvement
Relapses	Date, phenotype, severity, treatment, recovery	Disease activity
Disability	EDSS, Neurostatus certification field, ambulation, visual disability, bladder/bowel, cognition	Longitudinal outcome quality
Treatment	Acute treatment, maintenance therapy, rituximab and biosimilar exposure, biologics, IVIG, start/stop dates, reason for discontinuation	Effectiveness, access and safety
Safety	Infections, infusion reactions, malignancy, pregnancy outcomes, hospitalizations, vaccination interactions	Pharmacovigilance
Biomarker/precision fields	Biospecimen availability, vitamin D, EBV status where available, NfL/GFAP assay platform and units, genetic sub-study consent	Optional precision-neuroimmunology module
Patient-reported outcomes	Quality of life, fatigue, pain, work/school impact; Arabic/English validation status or validation plan	Patient-centered care
Follow-up	Last visit, lost to follow-up, moved country, death, transition of care	Longitudinal completeness

Table 2 footnote: The proposed minimum dataset integrates contemporary diagnostic criteria for MS, NMOSD and MOGAD; current MRI recommendations; disability and visual outcome measures; antibody-testing requirements; pediatric demyelination definitions; registry-methodology principles; OMOP-mappable variable structure; and optional precision-neuroimmunology fields.[13–23,30–58].Abbreviations: ADEM, acute disseminated encephalomyelitis; ADS, acquired demyelinating syndromes; AQP4-IgG, aquaporin-4 immunoglobulin G; CSF, cerebrospinal fluid; DMT, disease-modifying therapy; EBV, Epstein-Barr virus; EDSS, Expanded Disability Status Scale; GCL/IPL, ganglion-cell layer/inner plexiform layer; GFAP, glial fibrillary acidic protein; IgG, immunoglobulin G; LETM, longitudinally extensive transverse myelitis; MOGAD, myelin oligodendrocyte glycoprotein antibody-associated disease; MOG-IgG, myelin oligodendrocyte glycoprotein immunoglobulin G; MRI, magnetic resonance imaging; MS, multiple sclerosis; NfL, neurofilament light chain; NMOSD, neuromyelitis optica spectrum disorder; OCB, oligoclonal bands; OCT, optical coherence tomography; ON, optic neuritis; RNFL, retinal nerve fiber layer; VEP, visual evoked potential.

The pediatric module should not be a shortened adult module. It must include age at first symptom, perinatal history, developmental milestones, school performance, cognitive testing, behavior, fatigue, academic accommodations, growth, puberty, family burden, parent-reported quality of life, treatment impact, vaccination history, infection triggers and transition to adult care.[30–35,42,43] MRI fields should capture brain, orbit, optic nerve, spinal cord, LETM length, conus involvement, enhancement, brainstem lesions, cortical lesions, central vein sign, paramagnetic rim lesions and follow-up activity.[14,36] Visual outcomes should include high- and low-contrast visual acuity, color vision, visual fields, retinal nerve fiber layer thickness, ganglion-cell/inner plexiform layer thickness, VEP where available and final recovery category.[15,38,39] Treatment and safety fields should capture acute relapse therapy, time from relapse to treatment, steroid exposure, plasma exchange, IVIG, rituximab and biosimilars, maintenance biologics, start and stop dates, reason for discontinuation, access delay, funding source, adverse events, hospitalization, infection, pregnancy, vaccination, malignancy and death.[24–29]

The registry should include optional extended research modules for immunopathology, genetics, pharmacogenomics, biomarker discovery and precision management. These modules should not be mandatory for routine clinical enrollment, but should be available for approved sub-studies with separate consent and ethics approval. Candidate fields may include family history, consanguinity, ancestry category where ethically permissible, biospecimen availability, HLA and non-HLA genetic markers, vitamin D status, EBV serology where clinically available, cytokine or immune-cell profiling, NfL, GFAP, treatment exposure, relapse response, adverse events and long-term disability outcomes. The aim should be ethically governed hypothesis generation and consortium-compatible research readiness rather than overclaiming discovery power from routine registry data alone.

The minimum dataset should be designed from the beginning to support future mapping to OMOP Common Data Model tables. Demographic variables would map primarily to PERSON and OBSERVATION; diagnoses and phenotypes to CONDITION_OCCURRENCE; relapses, visits, hospitalizations and procedures to VISIT_OCCURRENCE and PROCEDURE_OCCURRENCE; drug exposures to DRUG_EXPOSURE; and antibody tests, laboratory values, MRI-derived coded variables, OCT measures, EDSS and visual outcomes to MEASUREMENT or OBSERVATION depending on variable type.[53,54] A REDCap-style data-dictionary excerpt, pediatric-specific fields, FAIR-OMOP alignment matrix and vocabulary mapping workflow are provided in Supplementary Table S1-S4.

Table 3. Clinically actionable information generated by the GCC Demyelinating Disease Registry.

Clinical domain	Key information generated	Why it matters clinically
Disease burden	Prevalence, incidence, age at onset, sex ratio, citizen/expatriate distribution	Defines real GCC disease burden and service needs
Presenting phenotype	Optic neuritis, LETM, brainstem syndrome, area postrema syndrome, ADEM-like illness, cortical encephalitis	Identifies early diagnostic clues and referral pathways
Diagnostic accuracy	Initial diagnosis, final diagnosis, criteria version, diagnostic delay, MS-to-NMOSD/MOGAD reclassification	Reduces misdiagnosis and inappropriate treatment
Antibody testing	AQP4-IgG/MOG-IgG result, assay type, timing, repeat testing, laboratory source	Standardizes serostatus interpretation
MRI phenotype	Optic nerve, central vein sign, paramagnetic rim lesions, brain, spinal cord, LETM length, conus and enhancing lesions	Improves clinicoradiological classification
CSF and biomarkers	OCB, IgG index, kappa free light chain, NfL/GFAP where available	Supports differential diagnosis and future prognostic studies
Visual outcomes	Visual acuity, color vision, visual fields, OCT RNFL/GCL-IPL, VEP, recovery after optic neuritis	Captures disability often missed by EDSS alone
Relapse biology	Relapse frequency, severity, phenotype, recovery, steroid response, PLEX/IVIG requirement	Identifies high-risk disease and escalation needs
Treatment effectiveness	Acute treatment, rituximab/biosimilar exposure, maintenance biologics, discontinuation, relapse on treatment	Generates real-world comparative-effectiveness evidence
Safety and pharmacovigilance	Serious infections, infusion reactions, pregnancy outcomes, malignancy, hospitalization	Supports safe biologic use and policy decisions
Pediatric outcomes	Development, cognition, school function, growth, puberty, quality of life, transition to adult care	Captures child-specific disease impact
Equity and access	MRI access, antibody testing access, biologic access, insurance/public-private pathway, expatriate follow-up	Identifies inequities and health-system gaps
Clinical decision support	Diagnostic algorithms, relapse-escalation pathways, biologic eligibility indicators, follow-up schedules	Converts registry data into practical tools
Precision management	Family history, consanguinity, consented genetics, biomarkers, treatment response predictors	Enables approved GCC precision-neuroimmunology sub-studies

Table 3 footnote: These domains should be captured in a modular manner to avoid excessive data-entry burden while preserving the ability to answer high-value clinical, epidemiological, pharmacovigilance, pediatric, equity, decision-support and precision-management questions.[13–58] Abbreviations: ADEM, acute disseminated encephalomyelitis; AQP4-IgG, aquaporin-4 immunoglobulin G; CSF, cerebrospinal fluid; EDSS, Expanded Disability Status Scale; GCL-IPL, ganglion-cell layer/inner plexiform layer; GFAP, glial fibrillary acidic protein; IgG, immunoglobulin G; IVIG, intravenous immunoglobulin; LETM, longitudinally extensive transverse myelitis; MOGAD, myelin oligodendrocyte glycoprotein antibody-associated disease; MOG-IgG, myelin oligodendrocyte glycoprotein immunoglobulin G; MRI, magnetic resonance imaging; MS, multiple sclerosis; NfL, neurofilament light chain; NMOSD, neuromyelitis optica spectrum disorder; OCB, oligoclonal bands; OCT, optical coherence tomography; PLEX, plasma exchange; RNFL, retinal nerve fiber layer; VEP, visual evoked potential.

Table 3. Clinically actionable information generated by the GCC Demyelinating Disease Registry.

Clinical domain	Key information generated	Why it matters clinically
Disease burden	Prevalence, incidence, age at onset, sex ratio, citizen/expatriate distribution	Defines real GCC disease burden and service needs
Presenting phenotype	Optic neuritis, LETM, brainstem syndrome, area postrema syndrome, ADEM-like illness, cortical encephalitis	Identifies early diagnostic clues and referral pathways
Diagnostic accuracy	Initial diagnosis, final diagnosis, criteria version, diagnostic delay, MS-to-NMOSD/MOGAD reclassification	Reduces misdiagnosis and inappropriate treatment
Antibody testing	AQP4-IgG/MOG-IgG result, assay type, timing, repeat testing, laboratory source	Standardizes serostatus interpretation
MRI phenotype	Optic nerve, central vein sign, paramagnetic rim lesions, brain, spinal cord, LETM length, conus and enhancing lesions	Improves clinicoradiological classification
CSF and biomarkers	OCB, IgG index, kappa free light chain, NfL/GFAP where available	Supports differential diagnosis and future prognostic studies
Visual outcomes	Visual acuity, color vision, visual fields, OCT RNFL/GCL-IPL, VEP, recovery after optic neuritis	Captures disability often missed by EDSS alone
Relapse biology	Relapse frequency, severity, phenotype, recovery, steroid response, PLEX/IVIG requirement	Identifies high-risk disease and escalation needs
Treatment effectiveness	Acute treatment, rituximab/biosimilar exposure, maintenance biologics, discontinuation, relapse on treatment	Generates real-world comparative-effectiveness evidence
Safety and pharmacovigilance	Serious infections, infusion reactions, pregnancy outcomes, malignancy, hospitalization	Supports safe biologic use and policy decisions
Pediatric outcomes	Development, cognition, school function, growth, puberty, quality of life, transition to adult care	Captures child-specific disease impact
Equity and access	MRI access, antibody testing access, biologic access, insurance/public-private pathway, expatriate follow-up	Identifies inequities and health-system gaps
Clinical decision support	Diagnostic algorithms, relapse-escalation pathways, biologic eligibility indicators, follow-up schedules	Converts registry data into practical tools
Precision management	Family history, consanguinity, consented genetics, biomarkers, treatment response predictors	Enables approved GCC precision-neuroimmunology sub-studies

Table 3 footnote: These domains should be captured in a modular manner to avoid excessive data-entry burden while preserving the ability to answer high-value clinical, epidemiological, pharmacovigilance, pediatric, equity, decision-support and precision-management questions.[13–58] Abbreviations: ADEM, acute disseminated encephalomyelitis; AQP4-IgG, aquaporin-4 immunoglobulin G; CSF, cerebrospinal fluid; EDSS, Expanded Disability Status Scale; GCL-IPL, ganglion-cell layer/inner plexiform layer; GFAP, glial fibrillary acidic protein; IgG, immunoglobulin G; IVIG, intravenous immunoglobulin; LETM, longitudinally extensive transverse myelitis; MOGAD, myelin oligodendrocyte glycoprotein antibody-associated disease; MOG-IgG, myelin oligodendrocyte glycoprotein immunoglobulin G; MRI, magnetic resonance imaging; MS, multiple sclerosis; NfL, neurofilament light chain; NMOSD, neuromyelitis optica spectrum disorder; OCB, oligoclonal bands; OCT, optical coherence tomography; PLEX, plasma exchange; RNFL, retinal nerve fiber layer; VEP, visual evoked potential.

4. Technical architecture, interoperability and data quality

The registry should begin with a pragmatic, secure and low-barrier platform but be designed from the start for interoperability and long-term scalability. Technical sophistication should not delay implementation, but poor data architecture should not be accepted as the price of speed. The registry must be able to start simply, scale safely and mature into an interoperable platform for real-world evidence, pharmacovigilance, policy reporting, clinical-trial readiness and precision medicine.

A hybrid architecture is recommended: REDCap or an equivalent electronic case-report form for user-friendly clinical data entry; an OMOP-compatible backend for standardized analytics; and a FAIR-compliant metadata repository for data dictionaries, variable definitions, provenance and access procedures.[50–54] REDCap is suitable for early registry phases because it supports structured forms, user rights, audit trails, branching logic, longitudinal events, data exports, multicenter workflows and metadata-driven research data capture.[50,51] The OMOP Common Data Model should be used as the analytical target structure or future backend because it enables standardized observational health-data analysis across heterogeneous sources.[53,54] FAIR principles would ensure that registry metadata and approved datasets are findable, accessible under appropriate governance, interoperable through shared vocabularies and reusable for ethically approved research.[52] Figure 4 illustrates the proposed FAIR–OMOP–OHDSI data architecture underpinning the registry, demonstrating how structured clinical information progresses from data capture through standardized transformation, metadata management, analytical processing, and federated multicountry reporting while maintaining national data sovereignty.

Figure 4illustrates the proposed FAIR-OMOP-OHDSI data architecture. The revised figure removes proprietary logos and presents a standards-based pathway from clinical sites and electronic case-report forms through ETL, vocabulary mapping, an OMOP-compatible analytical backend, FAIR metadata, data-quality dashboards and federated analytics.

Future linkage with hospital electronic medical records, laboratory systems, pharmacy systems, imaging repositories and national health-information exchanges should use internationally recognized interoperability approaches such as HL7 FHIR where feasible.[55] This would reduce duplicate data entry and allow the registry to mature from a research database into a learning health-system infrastructure.

A formal vocabulary-mapping workflow should be established from the start. Local diagnostic terms, Arabic/English clinical labels, antibody-test names, MRI descriptors, OCT variables, EDSS scores, pediatric developmental fields and MOGAD-specific phenotypes should be mapped to standardized vocabularies whenever possible. Athena can be used for concept discovery within the OMOP vocabulary ecosystem, while Usagi can support semi-automated source-to-concept mapping followed by expert clinical review. SNOMED CT should be prioritized for diagnoses and phenotypes, LOINC for laboratory and measurement fields, RxNorm or equivalent drug vocabularies for therapies, and ICD codes for health-system linkage. Where no adequate standard concept exists, carefully governed custom neuroimmunology extensions may be required. Initial mapping of high-frequency terms should be feasible within 3-6 months if supported by a dedicated technical working group, data manager and clinical validation panel.[53–55]

During the pilot phase, data quality should be monitored through predefined completeness, conformance and plausibility checks. Early dashboards should focus on essential fields, including diagnosis, criteria version, AQP4-IgG and MOG-IgG assay documentation, MRI phenotype, relapse dates, treatment exposure, EDSS or equivalent disability measures, visual outcomes, pediatric module completeness and follow-up status. During national scaling and full GCC integration, OHDSI-compatible tools such as ATLAS and HADES could be used for cohort definition, incidence and prevalence estimation, treatment-pathway analysis, pharmacovigilance, comparative-effectiveness studies and patient-level prediction.[53,54]

Given the sensitivity of health data and the diversity of GCC data-protection frameworks, a federated or nationally controlled architecture is likely to be the most practical, legally robust and politically acceptable model for a GCC-wide registry. Each national hub should retain control over patient-level data, with analytic scripts, common definitions and aggregate outputs enabling cross-country learning. A centralized regional repository of identifiable patient-level records should not be assumed. Statistical disclosure control, minimum cell-size rules, small-number suppression and pre-specified policies for rare phenotypes and serostatus combinations should be agreed by the Data Governance Board before multicountry analyses begin.

A registry that claims to measure disease occurrence must specify how that claim will be substantiated. Four methodological commitments should be made at protocol stage: case-definition validation through independent chart review; ascertainment completeness using at least one independent data source such as pharmacy disease-modifying therapy dispensing records; denominator discipline with citizen and non-citizen rates reported separately where feasible; and age- and sex-standardization to a stated reference population. Expatriate estimates should be interpreted cautiously because pre-entry health screening, insurance status, employment changes, repatriation and transfer of care may bias measured prevalence downward. Figure 5 depicts the proposed end-to-end data flow and interoperability architecture, showing how information captured during routine clinical care is validated, de-identified, integrated with imaging, laboratory, pharmacy, and patient-reported outcomes, and translated into regional analytics and policy-relevant outputs.

Figure 5depicts data flow from clinical encounters through structured registry forms, imaging metadata, antibody laboratory data, pharmacy exposure data, relapse records, disability measures, OCT or visual outcomes, patient-reported outcomes and optional biospecimen, biomarker or genetic datasets.

Table 4. Technical platform and data architecture requirements.

Requirement	Minimum standard	Advanced target
Data capture	REDCap-compatible structured forms	Integrated national e-health capture
Data model	Registry-specific dictionary	OMOP-compatible backend
Criteria provenance	Criteria version and reclassification fields	Automated temporal comparability checks
Interoperability	CSV/API export, standard codes	HL7 FHIR and imaging linkage
Imaging	MRI metadata fields including optic nerve, central vein sign and paramagnetic rim lesions	DICOM repository linkage and structured MRI reporting
Laboratory	AQP4/MOG assay fields, CSF kappa free light chain, NfL/GFAP platform fields	Direct lab-system interface
Security	Role-based access, audit trails, encryption	Federated privacy-preserving analytics
Governance	Data-access committee	Multicountry federated governance
Vocabulary mapping	Standard terms where available	Athena/Usagi-supported source-to-concept mapping
Data quality	Manual audits	OHDSI Data Quality Dashboard
Analytics	Descriptive reports	ATLAS/HADES-compatible analyses
AI readiness	Structured variables, missingness codes	Validated prediction models under explicit AI governance
Precision modules	Optional biospecimen/genetics/biomarker sub-study fields	Linked multi-omics and pharmacogenomic datasets
Patient engagement	Consent and PRO modules	Patient portal and dynamic consent
Reporting	Annual registry report	Dashboards and policy briefs

Table 4 footnote: Technical requirements are based on REDCap research-data capture, OMOP common-data-model principles, HL7 FHIR interoperability, FAIR data principles, privacy-by-design, secure health-data governance, ethical AI principles, OHDSI-compatible analytics and controlled optional linkage to biospecimen, genetics and biomarker modules.[50–55] Abbreviations: AI, artificial intelligence; API, application programming interface; AQP4, aquaporin-4; ATLAS, Advanced Analytics for Observational Healthcare Data; DICOM, Digital Imaging and Communications in Medicine; FAIR, Findable, Accessible, Interoperable, Reusable; FHIR, Fast Healthcare Interoperability Resources; GFAP, glial fibrillary acidic protein; HADES, Health Analytics Data-to-Evidence Suite; HL7, Health Level Seven International; MOG, myelin oligodendrocyte glycoprotein; MRI, magnetic resonance imaging; NfL, neurofilament light chain; OHDSI, Observational Health Data Sciences and Informatics; OMOP, Observational Medical Outcomes Partnership; PRO, patient-reported outcome; REDCap, Research Electronic Data Capture.

Table 4. Technical platform and data architecture requirements.

Requirement	Minimum standard	Advanced target
Data capture	REDCap-compatible structured forms	Integrated national e-health capture
Data model	Registry-specific dictionary	OMOP-compatible backend
Criteria provenance	Criteria version and reclassification fields	Automated temporal comparability checks
Interoperability	CSV/API export, standard codes	HL7 FHIR and imaging linkage
Imaging	MRI metadata fields including optic nerve, central vein sign and paramagnetic rim lesions	DICOM repository linkage and structured MRI reporting
Laboratory	AQP4/MOG assay fields, CSF kappa free light chain, NfL/GFAP platform fields	Direct lab-system interface
Security	Role-based access, audit trails, encryption	Federated privacy-preserving analytics
Governance	Data-access committee	Multicountry federated governance
Vocabulary mapping	Standard terms where available	Athena/Usagi-supported source-to-concept mapping
Data quality	Manual audits	OHDSI Data Quality Dashboard
Analytics	Descriptive reports	ATLAS/HADES-compatible analyses
AI readiness	Structured variables, missingness codes	Validated prediction models under explicit AI governance
Precision modules	Optional biospecimen/genetics/biomarker sub-study fields	Linked multi-omics and pharmacogenomic datasets
Patient engagement	Consent and PRO modules	Patient portal and dynamic consent
Reporting	Annual registry report	Dashboards and policy briefs

Table 4 footnote: Technical requirements are based on REDCap research-data capture, OMOP common-data-model principles, HL7 FHIR interoperability, FAIR data principles, privacy-by-design, secure health-data governance, ethical AI principles, OHDSI-compatible analytics and controlled optional linkage to biospecimen, genetics and biomarker modules.[50–55] Abbreviations: AI, artificial intelligence; API, application programming interface; AQP4, aquaporin-4; ATLAS, Advanced Analytics for Observational Healthcare Data; DICOM, Digital Imaging and Communications in Medicine; FAIR, Findable, Accessible, Interoperable, Reusable; FHIR, Fast Healthcare Interoperability Resources; GFAP, glial fibrillary acidic protein; HADES, Health Analytics Data-to-Evidence Suite; HL7, Health Level Seven International; MOG, myelin oligodendrocyte glycoprotein; MRI, magnetic resonance imaging; NfL, neurofilament light chain; OHDSI, Observational Health Data Sciences and Informatics; OMOP, Observational Medical Outcomes Partnership; PRO, patient-reported outcome; REDCap, Research Electronic Data Capture.

5. Implementation, ethics, sustainability and impact measurement

The registry should be implemented in phases to avoid overambition and early failure. A phased roadmap allows technical testing, governance refinement, data-quality monitoring and stakeholder confidence before full GCC integration. Implementation should follow established principles of feasibility testing, stakeholder engagement, iterative adaptation, sustainability planning and measurable implementation outcomes.[44–49]

Phase 1 should involve pilot implementation in 2-3 countries over 12-18 months. The pilot should include countries with complementary strengths: one with mature MS infrastructure, one with strong NMOSD/MOGAD evidence and one underrepresented country where the registry can demonstrate capacity-building value. A practical pilot could include Bahrain, Saudi Arabia and the UAE, with Kuwait, Qatar and Oman joining during scale-up. Bahrain would be a highly informative pilot site because smaller, centralized health systems can test near-national ascertainment workflows, while Saudi Arabia offers scale and regional neuroimmunology leadership and the UAE provides a strong platform for private-public healthcare coordination and specialist demyelinating-disease services.[1–12]

Key deliverables for Phase 1 should include a master protocol, ethics templates, consent forms, REDCap database, data dictionary, site training, first 300-500 enrolled patients, a basic data-quality dashboard and a first annual report. OMOP mapping should begin with high-value variables rather than the full dataset to avoid technical overload. Phase 2, from months 18-36, should expand each country to national hubs and additional hospitals, with private-sector participation, pediatric nodes, ophthalmology referral pathways, laboratory standardization and structured vocabulary mapping. Phase 3, from months 36-60, should mature into full GCC integration, with federated reporting, cross-country dashboards, pharmacovigilance outputs, health-economic studies, precision-neuroimmunology sub-studies, clinical-trial feasibility tools, publication governance and policy-reporting schedules. A detailed Gantt-style implementation roadmap is provided in Supplementary Table S5.

For a pilot involving 2-3 countries and 5-8 sites, first-year cost may reasonably fall into the range of USD 250,000-600,000 depending on staffing, platform hosting, imaging linkage and availability of existing institutional REDCap infrastructure. A practical pilot budget may allocate approximately 40-45% to personnel and coordination, 20-25% to technical infrastructure and security, 10-15% to training and site activation, 10-15% to data quality and statistical support, and 5-10% to meetings, dissemination and contingency. Full GCC scaling may require USD 1-2.5 million over 3-5 years, particularly if imaging repositories, patient portals, OMOP backend infrastructure, federated analytics or Data Quality Dashboard deployment are added. Optional multi-omics, biospecimen and pharmacogenomic modules would require separate funding, governance, consent and ethics approval.

Major risks include inconsistent data entry, variable ethics timelines, incomplete follow-up, laboratory heterogeneity, lack of protected coordinator time, underparticipation of private-sector providers, uncertainty about expatriate follow-up, vocabulary-mapping burden and overambitious technical design. These risks should be mitigated through a minimal mandatory dataset, clear case definitions, site training, data-quality dashboards, staged expansion, national hub leadership, phased OMOP mapping and early publication of aggregate annual reports.

A GCC registry must be scientifically ambitious but ethically conservative. Trust will determine sustainability. The registry should incorporate tiered informed consent, pediatric assent, privacy protection, data minimization, equity monitoring and transparent data-access governance. Consent should distinguish clinical registry participation, future research use, biological samples, imaging repository, data linkage, international collaboration and recontact for studies. Pediatric consent should include parental consent and age-appropriate assent where applicable.

The registry should reflect the lived reality of Gulf healthcare systems, where citizens and expatriates, public and private providers, national referral centers and cross-border professional networks all contribute to the true burden of demyelinating disease. Expatriates should be included because excluding them would bias incidence, service-utilization, treatment-access and outcome analyses. The registry should include residency status and mobility fields while protecting privacy and documenting loss to follow-up due to repatriation, insurance change, employment change or cross-border relocation.

Equity must be measured rather than assumed. The registry should assess whether women, men, citizens, expatriates, children, low-income patients and privately insured patients receive comparable diagnostic testing, MRI access, antibody testing, biologic treatment, rehabilitation and long-term follow-up. Genetic, consanguinity, puberty, fertility, pregnancy and family-burden fields require culturally appropriate wording and should be optional unless essential for clinical interpretation or approved research. Genetic and biomarker modules should be separately consented, ethically reviewed, scientifically justified and protected by strict privacy safeguards.

The registry should use mixed funding from ministries of health, national research councils, universities, hospitals, philanthropic foundations, professional societies and carefully governed industry partnerships. Industry funding should be transparent, unrestricted where possible and separated from data-access decisions. To preserve scientific credibility and public trust, the registry should remain as independent as possible from pharmaceutical-industry influence, with any industry support limited to transparent, non-controlling funding that does not determine governance, data access, analysis, interpretation, authorship, publication decisions or clinical recommendations. Figure 6 outlines the proposed phased implementation strategy for the GCC Demyelinating Disease Registry, illustrating the progression from pilot implementation to national expansion and ultimately to full GCC-wide integration, with incremental development of governance, interoperability, pharmacovigilance, and precision-neuroimmunology capabilities.

Figure 6summarizes the proposed phased implementation plan over 60 months, from pilot implementation and ethics approvals through national scaling, pediatric module activation, imaging and laboratory harmonization, vocabulary mapping, optional precision modules, dashboards, pharmacovigilance and trial readiness.

Table 5. Key performance indicators and success metrics.

Domain	KPI	Target
Enrollment	Number of enrolled patients	≥500 pilot; ≥3,000 regional within 5 years; longer-term mature coverage approaching the majority of prevalent demyelinating-disease cases
Coverage	Number of participating countries	2-3 pilot; 6 full GCC
Diagnostic quality	Criteria version and AQP4/MOG assay type documented	≥90% of tested patients
Follow-up	Annual follow-up completion	≥80%
Pediatric capture	Pediatric module completion	≥80% of pediatric cases
Equity	Citizen/expatriate access indicators	Annual reporting
Imaging	Core MRI data completeness	≥75% core MRI fields; structured central vein sign and paramagnetic rim lesion fields where available
Outcomes	Relapse and disability fields	≥85% completion
Clinical decision support	Draft diagnostic/referral/escalation pathways	Initial outputs by Phase 2
FAIR readiness	Data dictionary, metadata, provenance, access policy	Completed by Phase 1
OMOP readiness	Core fields mapped to OMOP-compatible structure	Initiated Phase 1; expanded Phase 2
Data quality	Completeness, conformance, plausibility checks	Quarterly Phase 1; semiannual Phase 2
Research	Peer-reviewed outputs	≥2 annually after year 2
Policy	Annual report to stakeholders	Yearly
Pharmacovigilance	Biologic safety reports including rituximab/biosimilars and targeted NMOSD/MOGAD agents	Annual
Precision module readiness	Optional consent, biospecimen, biomarker/genetics governance	Established by Phase 2
Trial readiness	Feasibility queries completed	≥3 by year 5

Table 5 footnote: KPIs reflect registry-quality indicators, data-completeness standards, real-world evidence requirements, pharmacovigilance needs, implementation-science outcomes, health-system performance metrics, clinical decision-support development, FAIR-OMOP readiness and readiness for ethically governed precision-neuroimmunology sub-studies.[44–58] Abbreviations: AQP4, aquaporin-4; FAIR, Findable, Accessible, Interoperable, Reusable; GCC, Gulf Cooperation Council; KPI, key performance indicator; MOG, myelin oligodendrocyte glycoprotein; MRI, magnetic resonance imaging; OMOP, Observational Medical Outcomes Partnership.

Table 5. Key performance indicators and success metrics.

Domain	KPI	Target
Enrollment	Number of enrolled patients	≥500 pilot; ≥3,000 regional within 5 years; longer-term mature coverage approaching the majority of prevalent demyelinating-disease cases
Coverage	Number of participating countries	2-3 pilot; 6 full GCC
Diagnostic quality	Criteria version and AQP4/MOG assay type documented	≥90% of tested patients
Follow-up	Annual follow-up completion	≥80%
Pediatric capture	Pediatric module completion	≥80% of pediatric cases
Equity	Citizen/expatriate access indicators	Annual reporting
Imaging	Core MRI data completeness	≥75% core MRI fields; structured central vein sign and paramagnetic rim lesion fields where available
Outcomes	Relapse and disability fields	≥85% completion
Clinical decision support	Draft diagnostic/referral/escalation pathways	Initial outputs by Phase 2
FAIR readiness	Data dictionary, metadata, provenance, access policy	Completed by Phase 1
OMOP readiness	Core fields mapped to OMOP-compatible structure	Initiated Phase 1; expanded Phase 2
Data quality	Completeness, conformance, plausibility checks	Quarterly Phase 1; semiannual Phase 2
Research	Peer-reviewed outputs	≥2 annually after year 2
Policy	Annual report to stakeholders	Yearly
Pharmacovigilance	Biologic safety reports including rituximab/biosimilars and targeted NMOSD/MOGAD agents	Annual
Precision module readiness	Optional consent, biospecimen, biomarker/genetics governance	Established by Phase 2
Trial readiness	Feasibility queries completed	≥3 by year 5

Table 5 footnote: KPIs reflect registry-quality indicators, data-completeness standards, real-world evidence requirements, pharmacovigilance needs, implementation-science outcomes, health-system performance metrics, clinical decision-support development, FAIR-OMOP readiness and readiness for ethically governed precision-neuroimmunology sub-studies.[44–58] Abbreviations: AQP4, aquaporin-4; FAIR, Findable, Accessible, Interoperable, Reusable; GCC, Gulf Cooperation Council; KPI, key performance indicator; MOG, myelin oligodendrocyte glycoprotein; MRI, magnetic resonance imaging; OMOP, Observational Medical Outcomes Partnership.

6. Priority research questions

The proposed registry would answer several questions that cannot be reliably addressed by fragmented retrospective studies, single-center cohorts or early descriptive regional registries. Previous Gulf NMOSD studies have confirmed regional case recognition and demonstrated the feasibility of collaborative registry work, but they have not provided definitive denominator-based prevalence and incidence estimates across all GCC countries.[10,11] The registry should therefore move the field from descriptive recognition toward population-based, longitudinal, serologically standardized, FAIR-OMOP-enabled, clinically actionable and precision-oriented epidemiology.

6.1. Epidemiology and ascertainment

• What are the true population-based prevalence, incidence, serostatus distribution and diagnostic-delay patterns of NMOSD and MOGAD across GCC countries when standardized diagnostic criteria, AQP4-IgG and MOG-IgG assay documentation, citizen and expatriate denominators, and longitudinal registry ascertainment are applied?
• What is the true prevalence and incidence of MS, recurrent optic neuritis, LETM and pediatric acquired demyelination across GCC countries?
• How much apparent country-level variation reflects true epidemiology versus differences in referral, antibody testing, MRI access, publication density, health-system structure, criteria version and registry maturity?
• What proportion of patients initially diagnosed with MS are later reclassified as NMOSD, MOGAD or another antibody-associated demyelinating disorder?

6.2. Clinical pathways and outcomes

• Which AQP4-IgG and MOG-IgG testing pathways produce the highest diagnostic reliability in regional practice?
• What are the clinical phenotypes, relapse patterns, MRI features and visual outcomes of GCC patients with MOGAD?
• How do children with MOGAD, ADEM-like presentations, optic neuritis or LETM perform longitudinally in cognition, school achievement, growth, puberty and quality of life?
• Are citizens and expatriates receiving comparable access to MRI, antibody testing, biologic therapy, rehabilitation and long-term follow-up?
• What are the real-world effectiveness and safety profiles of anti-CD20 therapies, rituximab biosimilars, complement inhibitors, IL-6 pathway therapies, IVIG and conventional immunosuppressants in GCC NMOSD and MOGAD populations?
• Can registry-derived clinical pathways improve diagnostic accuracy, reduce time to antibody testing, standardize relapse escalation and support rational biologic-treatment selection across GCC health systems?

6.3. Precision neuroimmunology

• Can ethically governed registry data support hypothesis generation, biomarker selection and consortium participation in studies of genetic, immunological and gene-environment factors across Arabian Gulf populations?
• Which clinical, serological, imaging, biomarker, environmental and treatment-exposure variables predict relapse risk, disability accumulation, visual recovery, adverse events and therapeutic response in GCC populations?
• Can registry-derived risk models predict relapse, poor visual recovery, disability accumulation, treatment failure or loss to follow-up, and can those models be externally validated before clinical deployment?

6.4. Digital health and health-system infrastructure

• Can FAIR-OMOP-compatible architecture improve reproducibility, data quality, multicountry comparability and federated real-world evidence generation for rare neuroimmunological diseases in the GCC?

These questions define a precision-neuroimmunology, clinical decision-support and digital-health agenda for the Arabian Gulf. They also provide direct value to ministries of health, clinicians, patients, families, payers, researchers and future clinical-trial networks.[44–58]

7. Conclusion and call to action

The GCC region has the clinical expertise, tertiary-care infrastructure, professional networks and strategic need to establish a unified demyelinating-disease registry. What it lacks is not ambition, but coordination. The GCC no longer lacks clinical activity in neuroimmunology; it lacks a harmonized regional infrastructure capable of converting dispersed clinical practice into cumulative regional evidence.

The proposed registry builds directly on prior Gulf experience, including the NMOAG registry, which demonstrated that regional NMOSD collaboration is feasible.[11] However, the next step must be broader: a prospective, multisyndromic, pediatric-inclusive, interoperable, policy-relevant, FAIR-OMOP-compatible, clinically actionable and precision-ready platform that integrates MS, NMOSD, MOGAD, recurrent optic neuritis, LETM and pediatric acquired demyelination. The proposed registry does not replace the NMOAG concept; it converts that pioneering disease-specific experience into a unified GCC-wide demyelinating-disease infrastructure.

The Bahrain experience remains instructive, not because retrospective national updates alone can prove changing disease occurrence, but because small, centralized health systems can test whether near-national clinical ascertainment can be translated into prospective surveillance. Apparent changes in prevalence must be interpreted through the lens of diagnostic criteria revisions, MRI access, survival, migration, case-finding method and follow-up completeness. A living registry can make these factors measurable rather than speculative.

A unified GCC Demyelinating Disease Registry would transform individual clinical encounters into cumulative regional knowledge. It would convert diagnostic uncertainty into structured classification, turn isolated case series into longitudinal real-world evidence, measure equity rather than assuming it, and support responsible biologic access, pharmacovigilance and trial readiness. It would allow the region to measure what it treats, improve what it measures and lead where it has previously been underrepresented.

Rather than importing an external registry template, the proposed model adapts international registry, FAIR, OMOP, OHDSI, MS registry-network and implementation-science principles to the demographic, cultural, regulatory and service-delivery realities of the Arabian Gulf. If successfully implemented, the GCC registry could serve as a scalable model for other Middle Eastern, North African and emerging health-system regions seeking to harmonize rare neuroimmunological disease data across heterogeneous health systems.

The call to action is clear: establish the steering committee, approve the core dataset, launch the pilot, build FAIR-OMOP-compatible infrastructure, enroll the first patients, publish the first registry report and make the GCC visible in global demyelinating-disease science.

Ethical Approval: Not applicable. This manuscript is a strategic framework and implementation roadmap and does not report patient-level data, involve participant recruitment, or analyse identifiable or anonymised individual-level clinical records. Future implementation of the proposed registry would require appropriate institutional and national ethics approvals before patient-level data collection.

Consent to Participate: Not applicable.

Consent for Publication: Not applicable. The manuscript does not contain identifiable individual patient information, clinical images or personal data requiring consent for publication.