Transforming a Leigh Syndrome Patient Registry to the OMOP Common Data Model

Transforming a Leigh Syndrome Patient Registry to the OMOP Common Data Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transforming a Leigh Syndrome Patient Registry to the OMOP Common Data Model Sushma Ghanta, Parag Shiralkar, Sophia Zilber This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8809871/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Patient- and caregiver-reported registries play a critical role in rare disease research, yet their heterogeneity and lack of interoperability limit reuse across studies and regulatory contexts. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a standardized framework to support harmonization and secondary use of observational data. Methods: We describe the transformation of a global, patient- and caregiver-reported Leigh syndrome registry led by Cure Mito Foundation into the OMOP CDM. The process included curation of registry variables and terms to standardized vocabularies, implementing extract–transform–load (ETL) procedures, and evaluating data quality using established OMOP validation and verification checks across conformance, completeness, and plausibility parameters. Results: The registry data was successfully aligned with the OMOP CDM. Key challenges reflected the non–EHR-derived, non–visit-based structure of the registry, which constrained use of certain OMOP tools, while key benefits included successful mapping to standardized vocabularies and harmonization of patient- and caregiver-reported data within the OMOP CDM. Conclusions: This work demonstrates the feasibility of transforming a patient- and caregiver-reported rare disease registry into the OMOP CDM while maintaining transparency regarding structural limitations. Standardizing patient-led registries such as the Cure Mito registry enhances interoperability and supports broader reuse for observational research, trial design, and regulatory-relevant analyses in rare diseases. Figures Figure 1 Introduction Leigh syndrome (LS) is a rare, severe, and progressive neurometabolic disorder classified under primary mitochondrial diseases. Symptoms typically begin in infancy or early childhood, although later-onset cases have been reported. Affected individuals may initially achieve early developmental milestones before experiencing progressive neurologial decline, including loss of motor, speech, and swallowing abilities. Leigh syndrome affects approximately 1 in 40,000 individuals worldwide and is associated with pathogenic variants across more than 110 mitochondrial and nuclear genes. Current treatment options remain largely supportive. The Cure Mito Foundation was founded in 2018 by parents of children with Leigh syndrome and has been part of the Chan Zuckerberg Initiative Rare As One Network since 2024 . To advance patient-centered research, Cure Mito established the Leigh Syndrome Global Patient Registry in partnership with the Coordination of Rare Diseases at Sanford (CoRDS). Launched in September 2021, the IRB-approved registry is listed on ClinicalTrials.gov (NCT01793168) and, as of January 2026, includes over 430 participants from 49 countries, making it the largest known international patient-driven registry for Leigh syndrome. From its inception, the registry emphasized transparency and patient engagement, with findings disseminated through multiple peer-reviewed publications and public-facing resources. 4 5 To enhance interoperability and research utility, Cure Mito partnered with Sumptuous Data Sciences, LLC to evaluate alignment of registry data with established data standards. An initial phase focused on transformation to Clinical Data Interchange Standards Consortium (CDISC) formats, including CDASH and SDTM, demonstrating the feasibility of mapping patient-reported real-world data to regulatory-grade data models. Building on this work, the current project extends transformation to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). This paper describes the OMOP transformation process, expands on results previously presented as a poster, and shares key implementation learnings and challenges. Establishing interoperability across both CDISC and OMOP positions the Leigh Syndrome Global Patient Registry to support broader integration of patient-reported and observational data for rare disease research. Methods The registry consisted of data collected from two patient-reported surveys. Outline of OMOP processes applied on the registry data is described below. The data transformation process utilizes various open-source tools developed by OHDSI community. 9 1. Metadata Scanning and Data Profiling: Initially, registry data was scanned to assess its structure and properties. For this purpose, we utilized the White Rabbit, an open-source utility recommended by OHDSI organization, which generated a scanned report containing a list of variables, data types, value ranges, and the frequency of each data field. This process helped us understand the data and identify issues such as missing values, inconsistent data types, and formatting variability prior to transformation. 2. Mapping and Designing OMOP CDM Specifications : Next, OMOP CDM mapping specifications were designed by utilizing the OHDSI recommended Rabbit-in-Hat open-source tool. This tool provides a visual interface for mapping each field to the appropriate OMOP CDM target tables and columns. We evaluated the transformation approach and methods provided by Rabbit in a Hat open-source tool for its validity, accuracy and overall effectiveness to get the data transformed into OMOP-CDM without losing its quality. 3. Identification of Standard Vocabulary Dictionaries Standard vocabularies corresponding to OMOP CDM v5.4 tables and required fields were identified to support consistent representation of registry data. Athena, an OHDSI-supported open-source vocabulary platform, was used to access and manage OMOP-standard vocabularies and concept identifiers. Where applicable, registry data elements were aligned to standardized concepts using Athena’s vocabulary search, concept relationships, and versioned hierarchies. This approach supported semantic consistency, interoperability with other OMOP-modeled datasets, and traceability of concept assignments across OMOP CDM tables. 4. Transformation of Source Registry Data to OMOP-CDM Following review of the mapping specifications developed using Rabbit-in-a-Hat, Leigh syndrome registry data were transformed into OMOP CDM v5.4 using an internally developed R-based codebase aligned with the finalized transformation specifications. 5. Handling Structural Characteristics In cases where source registry data required preprocessing to support alignment with the OMOP CDM, structural adjustments were applied prior to transformation. This included normalizing multi-valued survey responses (e.g., comma-separated symptom entries) into separate records, addressing duplicate data points, and reshaping variables as needed to support mapping to one or more OMOP CDM tables. 6. Missing data Missing data in the Cure Mito patient registry primarily reflects the nature of patient- and caregiver-reported survey data, including optional responses, recall limitations, and variability in respondent knowledge over time. In addition, exact dates such as date of birth were intentionally excluded from datasets shared with researchers in accordance with patient privacy protections and data governance policies. Where required by the OMOP CDM structure, placeholder or derived temporal values were used to support table population while preserving the integrity and intended interpretation of the underlying registry data. 7. Data Quality Assessment Data quality was evaluated using the OHDSI Data Quality Dashboard (version 2.7.0), assessing conformance, completeness, and plausibility checks. Data quality issues identified during initial assessment were reviewed and addressed through iterative updates to transformation logic, including refinement of concept identifier assignments, resolution of duplicate values, and reconciliation of data across OMOP CDM tables. 8. Validation: Once the data passes the data quality assessment, it was released to an independent quality controller (QC). The QC team built an independent QC codebase based on OMOP CDM mapping specifications, and compared the original transformed data to verify consistency and accuracy. Results Data quality assessment demonstrated an overall (gross) pass rate of 99%, with plausibility achieving 100% pass rates under both verification and validation criteria, conformance achieving 97% under verification and 100% under validation, and completeness achieving 100% pass rates under both verification and validation criteria. 1. OMOP CDM Transformation Overview The transformation enabled representation of longitudinal patient- and caregiver-reported registry data within the OMOP CDM framework while preserving the original survey-based structure. The overall transformation process is shown in Figure 1. 2. Domain Coverage and Data Representation Registry data elements were represented across multiple OMOP CDM domains based on clinical intent and data provenance. Core demographic information was modeled within the Person domain, while patient- and caregiver-reported clinical information was primarily represented using the Observation and Condition Occurrence domains. Additional domains were populated where applicable to reflect survey-derived data elements. Domains dependent on visit- or encounter-based data were not populated, consistent with the design of the registry. Table 1 summarizes the OMOP CDM domains used and the corresponding registry data elements represented within each domain. Table 1. Representation of Cure Mito Registry Data Across OMOP CDM Domains OMOP CDM Domain Domain Description Leigh Syndrome Registry Data Elements Represented in Domain PERSON Identifies each person with demographic details Participant demographic information VISIT_OCCURRENCE Captures details of healthcare encounters or instances of survey completion Survey completion date CONDITION_OCCURRENCE Records of conditions (e.g., diagnoses, symptoms) experienced by a person Reported diagnoses, first concerns noted, symptom history PROCEDURE_OCCURRENCE Records of procedures Reported diagnostic and genetic testing procedures DEVICE_EXPOSURE Records of medical device use Reported devices (e.g., feeding tubes, mobility devices) MEASUREMENT Records of measurements or tests Genetic testing results OBSERVATION Captures clinical facts about a person obtained in the context of examination, questioning, or a procedure Loss of milestones, caregiver burden, quality of life, family history, healthcare utilization DEATH Records of death and cause of death Reported death information SPECIMEN Information about biological samples Biospecimen information LOCATION Geographic information Participant country or state PROVIDER Information about healthcare providers Healthcare providers reported by participants CDM_SOURCE Metadata describing the source database and transformation process OMOP CDM metadata (CDM name, version, release date) 3. Concept Mapping and Standardization Outcomes Where applicable, registry data elements were mapped to standardized OMOP concept identifiers using SNOMED and LOINC vocabularies, with source values retained to support traceability. Table 2 provides illustrative examples of patient- or caregiver-reported genetic mutation responses and their corresponding mappings to OMOP standard concepts, as defined in the OHDSI ATHENA vocabulary. 6 Clinical history survey questions were similarly mapped to OMOP standard concepts, with examples shown in Table 3. Table 2. Examples of patient-reported genetic mutation responses mapped to OMOP standard concepts Registry survey question Standard concept name Standard vocabulary Standard concept ID Please indicate which gene mutation(s) the participant has, as it appears in their genetic report. MT-ATP6 gene m.8993T>G [Presence] in Blood or Tissue by Molecular genetics method LOINC 3038994 MT-ND5 gene m.13513G>A [Presence] in Blood or Tissue by Molecular genetics method LOINC 3040728 C12orf65 gene full mutation analysis in Blood or Tissue by Sequencing LOINC 46236834 MT-ND6 gene m.14459G>A [Presence] in Blood or Tissue by Molecular genetics method LOINC 44816590 Table 3. Mapping of clinical history survey questions to OMOP standard concepts Registry survey question Standard concept name Standard vocabulary Standard concept ID History of failure to thrive Failure to thrive SNOMED 437986 History of gastric motility/issues Motility disorder of intestine SNOMED 4340368 History of hypotonia Hypotonia LOINC 36308133 History of seizures Seizure SNOMED 377091 History of dystonia Dystonia SNOMED 375800 Discussion As a patient organization–led registry, Cure Mito aims to ensure that data contributed by patients and families were used broadly and effectively, maximizing research value and reflecting the time and effort invested by participants. This commitment motivated exploration of data standardization approaches that could support reuse, interoperability, and secondary research while remaining appropriate for patient-reported data. OMOP provides a standardized data framework and common vocabulary that supports interoperability, reproducibility, and the application of shared analytic tools across diverse data sources. The Cure Mito patient registry incorporates NIH Common Data Elements within one of its survey instruments and was originally designed to support analyses of patient- and caregiver-reported Leigh syndrome data. While this structure enables meaningful characterization of the registry population, additional modeling into the OMOP Common Data Model was explored to facilitate broader data sharing and alignment with external observational datasets. Because the OMOP Common Data Model is primarily designed for EHR-derived data, its structure assumes the availability of visit-based records and precise dates for clinical events. In contrast, the Cure Mito registry captures patient- and caregiver-reported information through surveys that do not consistently include visit constructs or exact start and end dates for conditions, treatments, or other clinical concepts. In addition, dates of birth – while present in the registry - are not included in the datasets shared with researchers, further limiting the ability to derive or impute event dates without introducing substantial inaccuracy. Where required by the OMOP CDM, questionnaire completion dates were used as placeholder dates to enable representation of registry data while preserving the integrity and intended use of the underlying patient-reported information. Successful mapping of registry data elements to SNOMED and LOINC standard vocabularies supports semantic interoperability and consistent representation of patient-reported data within the OMOP framework. For mitochondrial disease registries, this alignment may facilitate harmonization across registries by enabling consistent representation of shared clinical concepts and outcomes. The strong overall Data Quality Dashboard performance across plausibility, conformance, and completeness—under both verification and validation criteria—indicates that patient- and caregiver-reported registry data can be structurally aligned with OMOP CDM requirements. These results suggest that OMOP CDM can be applied to patient-reported rare disease registries, provided that findings are interpreted in light of registry structure and data governance considerations. Within the OMOP ecosystem, ATLAS, an open-source tool, offers a powerful interface for cohort definition, characterization, and visual exploration of standardized datasets and would be particularly valuable for interactive representation of registry data. In this work, ATLAS was not used because its deployment requires supporting web and database infrastructure that was not available within the computing environment used for this transformation. As a result, analyses were conducted using backend OMOP-compatible tools rather than the ATLAS interface. Future implementations that support secure hosting of ATLAS could further enhance visualization and exploratory analysis of patient-reported registry data. Consistent with the registry’s survey-based design, some OMOP analytical tools that rely on visit- or event-level temporal data were not applicable to this dataset. This reflects inherent structural differences between patient-reported registries and EHR-derived data and limits the use of analyses that depend on detailed visit- or event-based timing. The Cure Mito patient registry data have previously been transformed into CDISC standards, demonstrating alignment with structured clinical research data models commonly used to support regulatory and interventional study contexts. In contrast, the OMOP CDM is primarily designed to support observational and real-world data analyses. Experience working across both standards suggests that patient-reported rare disease registry data may align differently depending on the intended analytical purpose and data-sharing objectives. Viewed together, CDISC and OMOP represent complementary frameworks, each offering utility in distinct research and interoperability settings. To our knowledge, the Cure Mito patient registry is the only mitochondrial disease registry that has been made interoperable with both CDISC and OMOP data standards. This dual-standard alignment illustrates that patient-reported rare disease registries can be structured to support diverse analytical and data-sharing needs within a single framework. More broadly, this work demonstrates how patient-led registries can maximize data reuse and research impact while preserving governance, privacy, and the realities of patient-reported data collection. Limitations This work reflects limitations inherent to patient- and caregiver-reported rare disease registry data and to the scope of the OMOP CDM transformation. The Cure Mito registry is not derived from electronic health records and is not structured around discrete clinical encounters, which limited population of visit-based OMOP domains and the application of analyses dependent on encounter-level temporal data. In addition, certain OMOP domains were not populated due to the absence of corresponding data elements within the registry. These considerations should be taken into account when interpreting analyses derived from the transformed dataset. Conclusion The Cure Mito patient registry data were successfully represented within the OMOP Common Data Model. Data quality and validation results indicate that the transformation aligned registry data with OMOP CDM structural requirements. This alignment supports the feasibility of applying OMOP-based cohort definition and selected analytical workflows to patient-reported rare disease registry data, when interpreted within the context of registry design and governance. Overall, these findings demonstrate that patient-reported registry data can be meaningfully modeled within the OMOP CDM framework to support standardized representation and future use of OMOP-compatible analytical approaches. Declarations Ethics approval and consent to participate The Leigh Syndrome Global Patient Registry study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Sanford Health (03-13-060). Informed consent was obtained from adult participants who were able to provide consent. For participants under the age of 16, and for adult participants who lacked decisional capacity, informed consent was obtained from a parent, legal guardian, or legally authorized representative in accordance with IRB requirements. Consent for publication Not applicable Availability of data and materials Raw data from Leigh syndrome patient registry can be obtained by making a request at: https://research.sanfordhealth.org/-/media/research/files/cords/cords-researcher-access-request-fillable-form.pdf. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests No competing interests Funding This project has been made possible in part by grant number RAO3 from the CZI DAF, an advised fund of the Silicon Valley Community Foundation. Authors' contributions SZ and PS conceived the study. SG led the data transformation work. SZ, SG, and PS drafted the manuscript and critically reviewed and edited the content. All authors approved the final manuscript. Acknowledgements The authors are grateful to Dr. Danielle Boyce, MPH, DPA, for her insight, guidance, and thoughtful review. We also thank Kasey Woleben, founder and Executive Director of Cure Mito, for her leadership and support of the foundation’s mission and infrastructure that enable projects such as this. The authors thank the Sumptuous Data Sciences team members - Radhika Lakkireddy, Saidurga Pasupuleti, and Sapathagiri Bevara - for their contributions to data transformation. Finally, we are deeply thankful to the patients and caregivers who participated in the registry and made this work possible. References Rahman, S. Leigh syndrome. Handb. Clin. Neurol. 194, 43–63 (2023). DOI: 10.1016/B978-0-12-821751-1.00015-4 Official web portal of Cure Mito Foundation, www.curemito.org Coordination of Rare Diseases at Sanford (CoRDS), Official web portal of Sanford Research, https://research.sanfordhealth.org/rare-disease-registry Zilber, S., Burnworth, M., Afolabi, T. et al. Expanding research and care for Leigh syndrome: efforts of a patient-led advocacy organization. Res Involv Engagem 11, 137 (2025). https://doi.org/10.1186/s40900-025-00808-x Zilber, S., Woleben, K., Johnson, S. C., Moura de Souza, C. F., Boyce, D., Freiert, K., Boggs, C., Messahel, S., Burnworth, M. J., Afolabi, T. M., & Kayani, S. (2023). Leigh syndrome global patient registry: uniting patients and researchers worldwide. Orphanet Journal of Rare Diseases, 18, Article 264. https://doi.org/10.1186/s13023-023-02886-0 Parag Shiralkar, Bakare, P., Woleben, K., Zilber, S., (2024). Interoperability of Leigh Syndrome Patient Registry Data with Regulatory Submission Standards. Journal of the Society for Clinical Data Management, 4(1). https://doi.org/10.47912/jscdm.244 Official web portal of Sumptuous Data Sciences, LLC, www.sumptuous-ds.com Official web portal of Clinical Data Interchange Standards Consortium, www.cdisc.org Official Web portal of Observational Health Data Informatics and Sciences (OHDSI), www.ohdsi.org, All open source tools and utilities, such as ‘White Rabbit’, ‘Rabbit-in-a-Hat’, Athena, Atlas etc referred in this manuscript are developed by OHDSI organization. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 Mar, 2026 Reviews received at journal 01 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 25 Feb, 2026 Editor invited by journal 24 Feb, 2026 Submission checks completed at journal 23 Feb, 2026 First submitted to journal 23 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8809871","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599882600,"identity":"ca20d4b1-2142-4bf6-bb9f-68cc33886455","order_by":0,"name":"Sushma Ghanta","email":"","orcid":"","institution":"Sumptuous Data Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sushma","middleName":"","lastName":"Ghanta","suffix":""},{"id":599882601,"identity":"83980328-6508-4da2-b7cd-face16c4ac4c","order_by":1,"name":"Parag Shiralkar","email":"","orcid":"","institution":"Sumptuous Data Sciences","correspondingAuthor":false,"prefix":"","firstName":"Parag","middleName":"","lastName":"Shiralkar","suffix":""},{"id":599882602,"identity":"16fb108f-d94a-4177-9fbc-1fd2988f754b","order_by":2,"name":"Sophia Zilber","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYHACNoYEBhsIM4EELWlQLUTqYQPiwwzEWyPf3nvswYOa8/IGtw8f3fDwhw0Dv/TxC3i1GJw5l26QcOy24YZzaWk3EhLSGCT7cgrwa5HIMZNIYLudYHCGxwyo5TDQEB78zpOfAdLy7xxQC/834rQw3ABqSWw7ALKFDaqF/QAhv6RJJPYlG848wwZ0WFoaj2QPD35LQCEm+eObnTzfGeZnN3/Y2Mjx87A/wK+HgQeDy2NAmhYgIGjLKBgFo2AUjDAAANHFR3ksawWnAAAAAElFTkSuQmCC","orcid":"","institution":"Cure Mito Foundation","correspondingAuthor":true,"prefix":"","firstName":"Sophia","middleName":"","lastName":"Zilber","suffix":""}],"badges":[],"createdAt":"2026-02-06 17:54:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8809871/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8809871/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104012664,"identity":"d17792e6-ff30-4936-8b19-5353818dd136","added_by":"auto","created_at":"2026-03-05 16:10:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProcess flow of patient registry data transformation to OMOP\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8809871/v1/0ed62b0fc2360436a952b459.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transforming a Leigh Syndrome Patient Registry to the OMOP Common Data Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLeigh syndrome (LS) is a rare, severe, and progressive neurometabolic disorder classified under primary mitochondrial diseases. Symptoms typically begin in infancy or early childhood, although later-onset cases have been reported. Affected individuals may initially achieve early developmental milestones before experiencing progressive neurologial decline, including loss of motor, speech, and swallowing abilities. Leigh syndrome affects approximately 1 in 40,000 individuals worldwide and is associated with pathogenic variants across more than 110 mitochondrial and nuclear genes. Current treatment options remain largely supportive.\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e\u003c/p\u003e \u003cp\u003eThe Cure Mito Foundation was founded in 2018 by parents of children with Leigh syndrome and has been part of the Chan Zuckerberg Initiative Rare As One Network since 2024\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e. To advance patient-centered research, Cure Mito established the Leigh Syndrome Global Patient Registry in partnership with the Coordination of Rare Diseases at Sanford (CoRDS).\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e Launched in September 2021, the IRB-approved registry is listed on ClinicalTrials.gov (NCT01793168) and, as of January 2026, includes over 430 participants from 49 countries, making it the largest known international patient-driven registry for Leigh syndrome.\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e\u003c/p\u003e \u003cp\u003e From its inception, the registry emphasized transparency and patient engagement, with findings disseminated through multiple peer-reviewed publications and public-facing resources. \u003csup\u003e4 5\u003c/sup\u003e To enhance interoperability and research utility, Cure Mito partnered with Sumptuous Data Sciences, LLC to evaluate alignment of registry data with established data standards.\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e An initial phase focused on transformation to Clinical Data Interchange Standards Consortium (CDISC) formats, including CDASH and SDTM, demonstrating the feasibility of mapping patient-reported real-world data to regulatory-grade data models.\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e\u003c/p\u003e \u003cp\u003eBuilding on this work, the current project extends transformation to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e This paper describes the OMOP transformation process, expands on results previously presented as a poster, and shares key implementation learnings and challenges. Establishing interoperability across both CDISC and OMOP positions the Leigh Syndrome Global Patient Registry to support broader integration of patient-reported and observational data for rare disease research.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe registry consisted of data collected from two patient-reported surveys. Outline of OMOP processes applied on the registry data is described below. The data transformation process utilizes various open-source tools developed by OHDSI community. \u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Metadata Scanning and Data Profiling:\u003c/strong\u003e Initially, registry data was scanned to assess its structure and properties. For this purpose, we utilized the White Rabbit, an open-source utility recommended by OHDSI organization, which generated a scanned report containing a list of variables, data types, value ranges, and the frequency of each data field. \u0026nbsp;This process helped us understand the data and identify issues such as missing values, inconsistent data types, and formatting variability prior to transformation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMapping and Designing OMOP CDM Specifications\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, OMOP CDM mapping specifications were designed by utilizing the OHDSI recommended Rabbit-in-Hat open-source tool. This tool provides a visual interface for mapping each field to the appropriate OMOP CDM target tables and columns. We evaluated the transformation approach and methods provided by Rabbit in a Hat open-source tool for its validity, accuracy and overall effectiveness to get the data transformed into OMOP-CDM without losing its quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Identification of Standard Vocabulary Dictionaries\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStandard vocabularies corresponding to OMOP CDM v5.4 tables and required fields were identified to support consistent representation of registry data. Athena, an OHDSI-supported open-source vocabulary platform, was used to access and manage OMOP-standard vocabularies and concept identifiers. Where applicable, registry data elements were aligned to standardized concepts using Athena\u0026rsquo;s vocabulary search, concept relationships, and versioned hierarchies. This approach supported semantic consistency, interoperability with other OMOP-modeled datasets, and traceability of concept assignments across OMOP CDM tables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. \u0026nbsp; \u0026nbsp;Transformation of Source Registry Data to OMOP-CDM\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing review of the mapping specifications developed using Rabbit-in-a-Hat, Leigh syndrome registry data were transformed into OMOP CDM v5.4 using an internally developed R-based codebase aligned with the finalized transformation specifications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Handling Structural Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn cases where source registry data required preprocessing to support alignment with the OMOP CDM, structural adjustments were applied prior to transformation. This included normalizing multi-valued survey responses (e.g., comma-separated symptom entries) into separate records, addressing duplicate data points, and reshaping variables as needed to support mapping to one or more OMOP CDM tables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Missing data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMissing data in the Cure Mito patient registry primarily reflects the nature of patient- and caregiver-reported survey data, including optional responses, recall limitations, and variability in respondent knowledge over time. In addition, exact dates such as date of birth were intentionally excluded from datasets shared with researchers in accordance with patient privacy protections and data governance policies. Where required by the OMOP CDM structure, placeholder or derived temporal values were used to support table population while preserving the integrity and intended interpretation of the underlying registry data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Data Quality Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData quality was evaluated using the OHDSI Data Quality Dashboard (version 2.7.0), assessing conformance, completeness, and plausibility checks. Data quality issues identified during initial assessment were reviewed and addressed through iterative updates to transformation logic, including refinement of concept identifier assignments, resolution of duplicate values, and reconciliation of data across OMOP CDM tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eValidation:\u0026nbsp;\u003c/strong\u003eOnce the data passes the data quality assessment, it was released to an independent quality controller (QC). The QC team built an independent QC codebase based on OMOP CDM mapping specifications, and compared the original transformed data to verify consistency and accuracy.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eData quality assessment demonstrated an overall (gross) pass rate of 99%, with plausibility achieving 100% pass rates under both verification and validation criteria, conformance achieving 97% under verification and 100% under validation, and completeness achieving 100% pass rates under both verification and validation criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. OMOP CDM Transformation Overview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transformation enabled representation of longitudinal patient- and caregiver-reported registry data within the OMOP CDM framework while preserving the original survey-based structure. The overall transformation process is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; Domain Coverage and Data Representation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegistry data elements were represented across multiple OMOP CDM domains based on clinical intent and data provenance. Core demographic information was modeled within the Person domain, while patient- and caregiver-reported clinical information was primarily represented using the Observation and Condition Occurrence domains. Additional domains were populated where applicable to reflect survey-derived data elements. Domains dependent on visit- or encounter-based data were not populated, consistent with the design of the registry. Table 1 summarizes the OMOP CDM domains used and the corresponding registry data elements represented within each domain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Representation of Cure Mito Registry Data Across OMOP CDM Domains\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOMOP CDM Domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDomain Description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLeigh Syndrome Registry Data Elements Represented in Domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePERSON\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIdentifies each person with demographic details\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eParticipant demographic information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVISIT_OCCURRENCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCaptures details of healthcare encounters or instances of survey completion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSurvey completion date\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCONDITION_OCCURRENCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRecords of conditions (e.g., diagnoses, symptoms) experienced by a person\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReported diagnoses, first concerns noted, symptom history\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePROCEDURE_OCCURRENCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRecords of procedures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReported diagnostic and genetic testing procedures\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDEVICE_EXPOSURE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRecords of medical device use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReported devices (e.g., feeding tubes, mobility devices)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMEASUREMENT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRecords of measurements or tests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGenetic testing results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOBSERVATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCaptures clinical facts about a person obtained in the context of examination, questioning, or a procedure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLoss of milestones, caregiver burden, quality of life, family history, healthcare utilization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDEATH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRecords of death and cause of death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReported death information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSPECIMEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInformation about biological samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBiospecimen information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLOCATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGeographic information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eParticipant country or state\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePROVIDER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInformation about healthcare providers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthcare providers reported by participants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCDM_SOURCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMetadata describing the source database and transformation process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOMOP CDM metadata (CDM name, version, release date)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3. Concept Mapping and Standardization Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhere applicable, registry data elements were mapped to standardized OMOP concept identifiers using SNOMED and LOINC vocabularies, with source values retained to support traceability. Table 2 provides illustrative examples of patient- or caregiver-reported genetic mutation responses and their corresponding mappings to OMOP standard concepts, as defined in the OHDSI ATHENA vocabulary. \u003csup\u003e6\u003c/sup\u003e Clinical history survey questions were similarly mapped to OMOP standard concepts, with examples shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Examples of patient-reported genetic mutation responses mapped to OMOP standard concepts\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegistry survey question\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard concept name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard vocabulary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard concept ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003ePlease indicate which gene mutation(s) the participant has, as it appears in their genetic report.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eMT-ATP6 gene m.8993T\u0026gt;G [Presence] in Blood or Tissue by Molecular genetics method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLOINC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e3038994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eMT-ND5 gene m.13513G\u0026gt;A [Presence] in Blood or Tissue by Molecular genetics method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLOINC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e3040728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eC12orf65 gene full mutation analysis in Blood or Tissue by Sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLOINC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e46236834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003eMT-ND6 gene m.14459G\u0026gt;A [Presence] in Blood or Tissue by Molecular genetics method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLOINC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e44816590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Mapping of clinical history survey questions to OMOP standard concepts\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegistry survey question\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard concept name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard vocabulary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard concept ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eHistory of failure to thrive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eFailure to thrive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eSNOMED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e437986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eHistory of gastric motility/issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eMotility disorder of intestine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eSNOMED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e4340368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eHistory of hypotonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eHypotonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eLOINC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e36308133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eHistory of seizures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eSeizure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eSNOMED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e377091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eHistory of dystonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eDystonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003eSNOMED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e375800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs a patient organization\u0026ndash;led registry, Cure Mito aims to ensure that data contributed by patients and families were used broadly and effectively, maximizing research value and reflecting the time and effort invested by participants. This commitment motivated exploration of data standardization approaches that could support reuse, interoperability, and secondary research while remaining appropriate for patient-reported data.\u003c/p\u003e \u003cp\u003eOMOP provides a standardized data framework and common vocabulary that supports interoperability, reproducibility, and the application of shared analytic tools across diverse data sources.\u003c/p\u003e \u003cp\u003eThe Cure Mito patient registry incorporates NIH Common Data Elements within one of its survey instruments and was originally designed to support analyses of patient- and caregiver-reported Leigh syndrome data. While this structure enables meaningful characterization of the registry population, additional modeling into the OMOP Common Data Model was explored to facilitate broader data sharing and alignment with external observational datasets.\u003c/p\u003e \u003cp\u003eBecause the OMOP Common Data Model is primarily designed for EHR-derived data, its structure assumes the availability of visit-based records and precise dates for clinical events. In contrast, the Cure Mito registry captures patient- and caregiver-reported information through surveys that do not consistently include visit constructs or exact start and end dates for conditions, treatments, or other clinical concepts. In addition, dates of birth \u0026ndash; while present in the registry - are not included in the datasets shared with researchers, further limiting the ability to derive or impute event dates without introducing substantial inaccuracy. Where required by the OMOP CDM, questionnaire completion dates were used as placeholder dates to enable representation of registry data while preserving the integrity and intended use of the underlying patient-reported information.\u003c/p\u003e \u003cp\u003eSuccessful mapping of registry data elements to SNOMED and LOINC standard vocabularies supports semantic interoperability and consistent representation of patient-reported data within the OMOP framework. For mitochondrial disease registries, this alignment may facilitate harmonization across registries by enabling consistent representation of shared clinical concepts and outcomes.\u003c/p\u003e \u003cp\u003eThe strong overall Data Quality Dashboard performance across plausibility, conformance, and completeness\u0026mdash;under both verification and validation criteria\u0026mdash;indicates that patient- and caregiver-reported registry data can be structurally aligned with OMOP CDM requirements. These results suggest that OMOP CDM can be applied to patient-reported rare disease registries, provided that findings are interpreted in light of registry structure and data governance considerations.\u003c/p\u003e \u003cp\u003eWithin the OMOP ecosystem, ATLAS, an open-source tool, offers a powerful interface for cohort definition, characterization, and visual exploration of standardized datasets and would be particularly valuable for interactive representation of registry data. In this work, ATLAS was not used because its deployment requires supporting web and database infrastructure that was not available within the computing environment used for this transformation. As a result, analyses were conducted using backend OMOP-compatible tools rather than the ATLAS interface. Future implementations that support secure hosting of ATLAS could further enhance visualization and exploratory analysis of patient-reported registry data.\u003c/p\u003e \u003cp\u003eConsistent with the registry\u0026rsquo;s survey-based design, some OMOP analytical tools that rely on visit- or event-level temporal data were not applicable to this dataset. This reflects inherent structural differences between patient-reported registries and EHR-derived data and limits the use of analyses that depend on detailed visit- or event-based timing.\u003c/p\u003e \u003cp\u003eThe Cure Mito patient registry data have previously been transformed into CDISC standards, demonstrating alignment with structured clinical research data models commonly used to support regulatory and interventional study contexts. In contrast, the OMOP CDM is primarily designed to support observational and real-world data analyses. Experience working across both standards suggests that patient-reported rare disease registry data may align differently depending on the intended analytical purpose and data-sharing objectives. Viewed together, CDISC and OMOP represent complementary frameworks, each offering utility in distinct research and interoperability settings.\u003c/p\u003e \u003cp\u003eTo our knowledge, the Cure Mito patient registry is the only mitochondrial disease registry that has been made interoperable with both CDISC and OMOP data standards. This dual-standard alignment illustrates that patient-reported rare disease registries can be structured to support diverse analytical and data-sharing needs within a single framework. More broadly, this work demonstrates how patient-led registries can maximize data reuse and research impact while preserving governance, privacy, and the realities of patient-reported data collection.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis work reflects limitations inherent to patient- and caregiver-reported rare disease registry data and to the scope of the OMOP CDM transformation. The Cure Mito registry is not derived from electronic health records and is not structured around discrete clinical encounters, which limited population of visit-based OMOP domains and the application of analyses dependent on encounter-level temporal data. In addition, certain OMOP domains were not populated due to the absence of corresponding data elements within the registry. These considerations should be taken into account when interpreting analyses derived from the transformed dataset.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Cure Mito patient registry data were successfully represented within the OMOP Common Data Model. Data quality and validation results indicate that the transformation aligned registry data with OMOP CDM structural requirements. This alignment supports the feasibility of applying OMOP-based cohort definition and selected analytical workflows to patient-reported rare disease registry data, when interpreted within the context of registry design and governance. Overall, these findings demonstrate that patient-reported registry data can be meaningfully modeled within the OMOP CDM framework to support standardized representation and future use of OMOP-compatible analytical approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Leigh Syndrome Global Patient Registry study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Sanford Health (03-13-060). Informed consent was obtained from adult participants who were able to provide consent. For participants under the age of 16, and for adult participants who lacked decisional capacity, informed consent was obtained from a parent, legal guardian, or legally authorized representative in accordance with IRB requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data from Leigh syndrome patient registry can be obtained by making a request at: https://research.sanfordhealth.org/-/media/research/files/cords/cords-researcher-access-request-fillable-form.pdf. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project has been made possible in part by grant number RAO3 from the CZI DAF, an advised fund of the Silicon Valley Community Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSZ and PS conceived the study. SG led the data transformation work. SZ, SG, and PS drafted the manuscript and critically reviewed and edited the content. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to Dr. Danielle Boyce, MPH, DPA, for her insight, guidance, and thoughtful review. We also thank Kasey Woleben, founder and Executive Director of Cure Mito, for her leadership and support of the foundation\u0026rsquo;s mission and infrastructure that enable projects such as this. The authors thank the Sumptuous Data Sciences team members - Radhika Lakkireddy, Saidurga Pasupuleti, and Sapathagiri Bevara - for their contributions to data transformation. Finally, we are deeply thankful to the patients and caregivers who participated in the registry and made this work possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRahman, S. Leigh syndrome. Handb. Clin. Neurol. 194, 43\u0026ndash;63 (2023). DOI: 10.1016/B978-0-12-821751-1.00015-4\u003c/li\u003e\n\u003cli\u003eOfficial web portal of Cure Mito Foundation, www.curemito.org\u003c/li\u003e\n\u003cli\u003eCoordination of Rare Diseases at Sanford (CoRDS), Official web portal of Sanford Research, https://research.sanfordhealth.org/rare-disease-registry\u003c/li\u003e\n\u003cli\u003eZilber, S., Burnworth, M., Afolabi, T. et al. Expanding research and care for Leigh syndrome: efforts of a patient-led advocacy organization. Res Involv Engagem 11, 137 (2025). https://doi.org/10.1186/s40900-025-00808-x\u003c/li\u003e\n\u003cli\u003eZilber, S., Woleben, K., Johnson, S. C., Moura de Souza, C. F., Boyce, D., Freiert, K., Boggs, C., Messahel, S., Burnworth, M. J., Afolabi, T. M., \u0026amp; Kayani, S. (2023). Leigh syndrome global patient registry: uniting patients and researchers worldwide. Orphanet Journal of Rare Diseases, 18, Article 264. https://doi.org/10.1186/s13023-023-02886-0\u003c/li\u003e\n\u003cli\u003eParag Shiralkar, Bakare, P., Woleben, K., Zilber, S., (2024). Interoperability of Leigh Syndrome Patient Registry Data with Regulatory Submission Standards. Journal of the Society for Clinical Data Management, 4(1). https://doi.org/10.47912/jscdm.244\u003c/li\u003e\n\u003cli\u003eOfficial web portal of Sumptuous Data Sciences, LLC, www.sumptuous-ds.com\u003c/li\u003e\n\u003cli\u003eOfficial web portal of Clinical Data Interchange Standards Consortium, www.cdisc.org\u003c/li\u003e\n\u003cli\u003eOfficial Web portal of Observational Health Data Informatics and Sciences (OHDSI), www.ohdsi.org, All open source tools and utilities, such as \u0026lsquo;White Rabbit\u0026rsquo;, \u0026lsquo;Rabbit-in-a-Hat\u0026rsquo;, Athena, Atlas etc referred in this manuscript are developed by OHDSI organization.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8809871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8809871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003ePatient- and caregiver-reported registries play a critical role in rare disease research, yet their heterogeneity and lack of interoperability limit reuse across studies and regulatory contexts. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a standardized framework to support harmonization and secondary use of observational data.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003e We describe the transformation of a global, patient- and caregiver-reported Leigh syndrome registry led by Cure Mito Foundation into the OMOP CDM. The process included curation of registry variables and terms to standardized vocabularies, implementing extract\u0026ndash;transform\u0026ndash;load (ETL) procedures, and evaluating data quality using established OMOP validation and verification checks across conformance, completeness, and plausibility parameters.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe registry data was successfully aligned with the OMOP CDM. Key challenges reflected the non\u0026ndash;EHR-derived, non\u0026ndash;visit-based structure of the registry, which constrained use of certain OMOP tools, while key benefits included successful mapping to standardized vocabularies and harmonization of patient- and caregiver-reported data within the OMOP CDM.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThis work demonstrates the feasibility of transforming a patient- and caregiver-reported rare disease registry into the OMOP CDM while maintaining transparency regarding structural limitations. Standardizing patient-led registries such as the Cure Mito registry enhances interoperability and supports broader reuse for observational research, trial design, and regulatory-relevant analyses in rare diseases.\u003c/p\u003e","manuscriptTitle":"Transforming a Leigh Syndrome Patient Registry to the OMOP Common Data Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 16:10:30","doi":"10.21203/rs.3.rs-8809871/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"72460885579715364470847295492822946911","date":"2026-03-03T09:40:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T21:02:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43179720924568610505334522146820030039","date":"2026-02-28T15:01:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91049309399720167579115228855305853923","date":"2026-02-28T10:53:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T22:49:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T15:22:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T05:14:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T16:59:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2026-02-23T16:55:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4fd56a90-7e8d-4293-8863-24f8be257b9a","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T16:10:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 16:10:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8809871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8809871","identity":"rs-8809871","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}