Standardizing Rare Disease Data in Brazil: A Delphi-Based Approach for the First National Registry

Standardizing Rare Disease Data in Brazil: A Delphi-Based Approach for the First National Registry | 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 Standardizing Rare Disease Data in Brazil: A Delphi-Based Approach for the First National Registry Filipe Andrade Bernardi, Tatiana Takahasi Komoto, André Luiz Teixeira Vinci, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6701865/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction: Rare diseases (RD) are characterized by low prevalence, affecting fewer than 1 in 2,000 individuals, yet impacting millions globally. In Brazil, RD management is challenged by geographic disparities and unequal access to care. To address these gaps, the Brazilian Rare Diseases Network (RARAS) developed a Minimum Data Set (MDS) to standardize RD documentation. Objective: To validate the proposed MDS through a Delphi consensus process with clinical experts. Methods: This study used the Delphi method to validate the MDS, involving clinical experts. A structured questionnaire with 55 items across nine data categories was distributed via REDCap. Experts evaluated the relevance of each variable and reached a consensus, which was defined as ≥80% agreement. Results: Fifty-two professionals were invited; 16 responded in round one, and 11 in round two. Suggestions for additions and removals were reviewed after each round. Seven data categories achieved over 81.8% agreement, reaching consensus levels between 90.9% and 100%. The final MDS includes care indicators and facilitates consistent documentation and care coordination. Conclusions: This study demonstrates the value of standardized data collection in strengthening RD surveillance, clinical workflows, and research. Figures Figure 1 Introduction Rare diseases (RD) affect a small percentage of the population, typically less than 1 in 2,000 individuals (Montserrat Moliner and Waligora 2017). Despite their low prevalence, they impact millions worldwide, with over 7,000 distinct RDs identified (World Health Organization 2023). The heterogeneous nature of these diseases presents significant challenges for healthcare systems, including delayed diagnoses, limited treatment options, and a need for standardized care protocols. Due to their low prevalence, people living with RD frequently face delayed diagnoses, restricted access to effective treatment, and a lack of standardized care protocols (Adachi et al. 2023). Managing RD in Brazil is particularly challenging due to the country's vast geographic diversity and regional disparities in healthcare access. Many genetic services essential for diagnosing and treating RD are concentrated in public universities and reference hospitals in large urban centers, limiting accessibility for individuals needing these services (Iriart et al. 2019). Thus, remote areas often struggle with limited resources, leading to an unequal distribution of healthcare services, which hinders the timely diagnosis and treatment of RD. Moreover, despite establishing the Brazilian Policy for Comprehensive Care for People with Rare Diseases, which aims to ensure comprehensive care and implement actions to reduce morbidity and mortality in this population, many challenges persist in achieving these objectives (BRASIL 2021). Among the main obstacles are the lack of standardized information, issues with data sharing between the various health network nodes, and challenges observed in other parts of the world (Alves et al. 2021; Félix et al. 2022). Therefore, comprehensive and harmonized data collection is critical to effectively addressing these challenges, and this is where the Minimum Data Set (MDS) concept becomes vital. An MDS is a standardized collection of essential data elements necessary for the accurate documentation, analysis, and management of diseases. It enables consistent and comparable data across healthcare settings and facilitates better diagnosis, treatment, and research (Bernardi et al. 2023). In response to these challenges, the Brazilian Rare Diseases Network (RARAS) developed a specific MDS for RD to support high-quality data collection and interoperability within the national health system. By establishing a standardized set of core data elements, the MDS enables consistent documentation of RD cases, improves data exchange across healthcare levels, and facilitates integration into national and international registries. This is especially relevant in a complex system like Brazil’s Unified Health System (SUS), where regional disparities and fragmented information hinder effective coordination. This study aims to validate the proposed MDS through a Delphi consensus process with clinical experts. The resulting dataset represents essential information for RD case documentation, enabling accurate patient characterization and supporting evidence-based diagnosis, treatment, and policy development. Methods This study is based on the systematic review findings conducted by Bernardi et al. (Bernardi et al. 2023). The review analyzed and compared MDSs used worldwide in health networks for RD. The review included 20 articles and identified significant terminology and structure variations among RD MDSs. This lack of uniformity hinders international collaboration and data comparison, complicating RD management (Bernardi et al. 2023). The research also compared the identified MDSs with World Health Organization (WHO) guidelines, pinpointing discrepancies and areas for improvement to align the MDSs with global best practices (Mehl et al. 2021). One of the study's results included a recommendation of a MDS for RD (see Supplementary Material 2 – Detailed Variable List, Initial Version). MDSs are structured collections of essential and categorical variables representing clinical scenarios within health systems. They ensure consistency in data recording, support comprehensive documentation, and enable analysis and interoperability across institutions. In the RD context, where data are often fragmented and heterogeneous, standardizing information is crucial for improving diagnosis, care coordination, and research (Bernardi et al. 2023). The MDS proposed in this study builds upon global recommendations, including those from the WHO, and aims to unify data elements across healthcare levels and regions in Brazil (Mehl et al. 2021; Bernardi et al. 2023). To validate this MDS, we applied the Delphi method, a structured approach for achieving consensus among experts through iterative rounds of evaluation and feedback. Following the definition of the research question and selection of qualified participants, a facilitator coordinated the process, ensuring consistency, anonymity, and systematic communication (Niederberger and Spranger 2020). Experts responded to structured questionnaires assessing the relevance of variables. Their suggestions were reviewed by the research team and used to refine the dataset in subsequent rounds (Okoli and Pawlowski 2004). This methodology enabled transparent decision-making and continuous improvement of the MDS based on expert judgment. Researchers analyze data from the first round, incorporate suggestions (if applicable), and construct a second-round questionnaire for items lacking consensus. This process is repeated until a consensus among experts or until a certain number of attempts is reached, depending on the research context. The Delphi method design includes the following elements: research question, expert selection, number and sample of experts, structured questionnaire construction, information and content provided to experts, feedback management, data analysis, feedback development and implementation, and research communication and administration (Niederberger and Spranger 2020; Drumm et al. 2022). Figure 1 presents a detailed flowchart of the Delphi method used to validate the MDS in the context of RD in Brazil. The flowchart illustrates the main stages of the study, from initial preparation and conceptual analysis to questionnaire development, participant selection, and consensus refinement. It highlights the iterative interaction between experts and researchers to achieve the final consensus on the MDS. Questionnaire Preparation The questionnaire contained 55 questions across nine data categories: Participant Characterization, Eligibility, Identification, Diagnosis, Treatment, Medical Consultation, Comorbidity, Outcome, and Other. In the first round, the Participant Characterization section only had one question (P0) that assessed participants' experience with MDS in healthcare. The questionnaire used only questions P1, P2, and P3 for the remaining eight categories representing the MDS in the reference study. P1 evaluated participants' agreement on the appropriateness of variables in each category, while P2 and P3 focused on whether variables should be added or removed from these categories, respectively (Table 4). The questionnaire was designed and implemented using the Research Electronic Data Capture (REDCap) platform's survey module. This feature allows researchers to collect data anonymously, ensuring participant confidentiality. Each respondent received a unique, randomized survey link via email, preventing the identification of individual responses while enabling submission status tracking. The survey design also incorporated features such as branching logic, which dynamically adjusted the displayed questions based on previous answers, ensuring that participants only interacted with relevant sections of the questionnaire. REDCap is a secure, web-based application widely used for research studies due to its compliance with international data protection regulations. The platform provides automated workflows for survey distribution, including scheduled email invitations and reminders, which were utilized to maximize participant engagement. All collected data were stored on institutional REDCap servers with restricted access and regular backups, ensuring data security and integrity (Harris et al. 2009). Participants rated their responses using five levels of agreement: strongly agree, agree, neutral, disagree, and strongly disagree. For P2 and P3, participants indicated whether they wanted to suggest adding or removing variables (Table 4). When they answered affirmatively, they were asked to provide qualitative justifications for their suggestions. Table 4: Fundamental questions of the Delphi Study on Minimum Data Sets. ID Questions Possible Answers P0 Based on your experience with the use of Minimum Data Sets in healthcare, please indicate the extent to which you agree or disagree with the following statement: "I use or have used a Minimum Data Set (MDS) in healthcare as part of my professional activities (service, care, management, or research), either at the institution mentioned or another institution." 'Strongly agree' or 'Agree' or 'Neutral’ or ‘Disagree’ or 'Strongly disagree' or 'I don't know' P1 "The data/variables described in the * category encompass the processes carried out (service, care, management, or research) in my institution regarding RD." 'Strongly agree' or 'Agree' or 'Neutral’ or ‘Disagree’ or 'Strongly disagree' or 'I don't know' P2 Based on the context of the healthcare institution you are involved in, would you add data/variables in the * category beyond those already described in the Fundamental MDS document? 'Yes' or 'No' P3 Based on the context of the healthcare institution you are involved in, would you remove data/variables in the * category? 'Yes' or 'No' Note: * The asterisk (*) represents a placeholder replaced in each question section by one of the eight variables: eligibility, identification, diagnosis, treatment, medical consultation, comorbidities, outcome, and others. Participants were sent the questionnaire to evaluate the original MDS, and their responses were then compiled and analyzed to create an adapted MDS. All responses were de-identified during the data extraction process to guarantee further anonymity. This ensured the research team could analyze the data without linking responses to individual participants. This process was repeated at the end of each round. Experts in the field of RD at the RARAS network were invited to participate in the voluntary and anonymous study. Participants were briefed via an initial email that included a description of the study objectives, the survey’s anonymous format, and instructions for completing the questionnaire. They were informed that their responses would contribute to refining the MDS but that their identities would remain confidential throughout the process. Data analysis and synthesis The questionnaire responses were analyzed using a mixed-methods approach, incorporating quantitative and qualitative techniques. Responses were included if participants answered all questions or had, at most, one missing response. Incomplete questionnaires were reviewed to determine if the missing data could impact the results, and sensitivity analyses were conducted to assess the robustness of the findings. Data was extracted from REDCap in .csv format and imported into Google Sheets and statistical software for further processing. The REDCap platform provided built-in audit trails to ensure data integrity during extraction and analysis (Harris et al. 2009). Absolute and relative frequencies were computed for multiple-choice questions to identify group preferences, excluding respondents who indicated having no opinion on the subject. The research team adopted an 80% agreement threshold to determine consensus, a commonly used benchmark in Delphi studies for defining convergence among expert opinions (Diamond et al. 2014). Agreement levels were calculated for each variable and category of the MDS, both individually and collectively, by combining ‘Strongly agree' or 'Agree' answers. Any variable or category that did not reach this threshold was subject to a new round of evaluation to refine the dataset and ensure comprehensive expert agreement. To address missing data, the research team analyzed and determined if the missing responses were randomly distributed or if there were patterns that could introduce bias into the results. Randomly missing data were deemed acceptable, whereas systematic missingness required further investigation. If significant gaps were identified or the missing data compromised the validity of a participant’s input, those responses were excluded based on pre-defined exclusion criteria. This ensured that only high-quality and representative data informed the consensus process (Alwateer et al. 2024). Quantitative data analysis was conducted using the statistical software SPSS. Although concordance measures such as Kendall’s W, Fleiss’ Kappa, and Cohen’s Kappa were tested, they showed low agreement levels, suggesting random response patterns across rounds. Therefore, we adopted the Content Validity Index (CVI) as the primary metric for assessing consensus. The CVI reflects the proportion of experts rated each item as “agree” or “strongly agree,” relative to the total number of responses. Delphi studies commonly use this approach to evaluate content validity and item relevance, especially when expert panel sizes are limited. Items reaching or exceeding the 80% agreement threshold were considered validated and retained in the final version of the MDS (Roni and Djajadikerta 2021). A qualitative content analysis was conducted for open-ended questions. Suggestions for adding or removing data were coded and categorized to identify recurring themes and ensure alignment with the study objectives. To validate the relevance of these suggestions, two independent experts (T.M.F. and B.M.O. - not involved in the Delphi panel) reviewed each proposed addition or removal. The experts evaluated the alignment of the suggestions with the MDS framework, their relevance to RD, and potential overlaps with existing fields. The unanimous agreement of these reviewers was required for a suggestion to be accepted or rejected. The iterative Delphi process allowed the research team to systematically compile and analyze results from each round. Each round's findings were summarized in a report shared with participants in a special session after the monthly RARAS meeting. This ensured transparency and provided feedback on how their input contributed to the evolving MDS. The summary included visualizations such as bar charts and heatmaps to display agreement levels and highlight areas requiring further discussion. These results generated new MDS versions and served as the basis for subsequent rounds until a consensus was reached. Ethical concerns This study did not involve human participants, data, or tissue. Ethics approval and consent to participate are not applicable, as this study was based solely on publicly available datasets and did not recruit new human participants or collect data. However, the primary study that originated this research was approved by the Research Ethics Committee (REC) of the Hospital de Clínicas de Porto Alegre, Brazil (approval number: 33970820.0.1001.5327). Results Descriptive analysis of participants A total of 52 health professionals were invited to take part in this study. In the first round, 16 participants provided valid responses, resulting in a response rate of 30.8%. In the second round, only 11 initial respondents (68.75%) completed the questionnaire. The participants had significant field experience, averaging 20.37 (± 8.91) years. Their agreement with MDS in daily practice was 75.0%, indicating a solid use of MDS for other clinical fields. Among the participants, professors were the largest group at 38.9%, followed by the doctors/preceptors at 27.8% (Table 1). Table 1: Distribution of participants by professional position. Position held Participants (n) Participants (%) Professor/Lecturer* 7 41.2% Doctor/Preceptor 5 29.4% Research Coordinator 1 5.9% RD Reference Service Coordinator 1 5.9% Outpatient Services Manager 2 11.8% Department Coordinator 1 5.9% Total 17 100% *: One professional reported working as a teacher in addition to their roles as Department Coordinators. Iterative Refinement of the MDS The Delphi process played a pivotal role in refining the MDS variables, gathering expert feedback over two rounds, and ensuring that the final version was comprehensive, practical, and meaningful for documenting RD. Initially, the MDS contained 59 fields across eight data categories: Participant Characterization, Eligibility, Identification, Diagnosis, Treatment, Medical Consultation, Comorbidity, and Outcome (Supplementary Material 2 – Detailed Variable List, Initial Version). Through careful discussion and consensus, adjustments were made to balance the breadth of data collected with its usability and efficiency. In the first round of the study, six categories had participants’ agreement above 80.0%, with the Identification category showing the highest level of agreement (100.0%), and Consultation (75.0%) and Outcome (56.3%) exhibiting the lowest level of agreement. Participants' suggestions showed that Treatment (n=10) and Medical Consultation (n=10) received the highest number of suggestions for addition, followed by Outcome (n=8). The categories Identification (n=7), and Diagnosis (n=7) had the most removal suggestions, with participants strongly perceiving redundancy, whereas Treatment (n=1) and Comorbidity (n=0) exhibited the highest level of agreement. The distribution of suggestions for additions and removals by category is summarized in Table 2. Seven new fields were added after the experts assessed the relevance during the first round (Supplementary Material 2 – Detailed Variable List). Treatment and Outcome had the highest added fields (3 and 2, respectively). Four fields were removed ( Supplementary Material 2 – Detailed Variable List), two of which were in the Diagnosis category (Table 2). Thus, the MDS was compiled after the first round and used for evaluation by the participants in the second round, which comprised 62 fields (Supplementary Material 4 – Round-by-Round Analysis Results). Participants’ agreement in all categories was above 80.0% in the second round. Eligibility, Treatment, and Comorbidity had total agreement (100.0%); Identification, Medical Consultation, Outcome, and Other had high agreement (90.9%); and Diagnosis exhibited the lowest level of agreement (81.5%). The total number of suggestions in this round had significantly decreased (from 50 to 13). Medical Consultation remained the category with the most suggestions for addition, though these decreased from 10 in the first round to 4 in the second. Comorbidity maintained a high level of agreement, with no suggestions for addition. Regarding removal, Diagnosis continued to receive the most tips for exclusion. However, the number decreased from 7 in the first round to 4 in the second. In this round, the experts' validation of the suggestions resulted in adding two new fields and removing five, four of which were in the Diagnosis category (Table 3). Thus, the MDS compiled after the second round comprised 59 fields (see Supplementary Material 4 – Round-by-Round Analysis Results). This final version of the MDS is intended to serve as a reference for future implementation in national RD registries and healthcare information systems. Table 2: Participants' suggestions for adding and removing variables across Delphi Study rounds. Variable Additions (Round 1) Additions (Round 2) Removals (Round 1) Removals (Round 2) Eligibility 3 1 4 0 Identification 2 2 7 1 Diagnosis 6 2 7 4 Treatment 10 2 1 1 Medical Consultation 10 4 3 0 Comorbidity 7 0 0 0 Outcome 8 2 5 1 Other 4 2 0 1 TOTAL 50 13 27 8 Consensus Achievement and Agreement Levels The Delphi rounds demonstrated a growing consensus among health professionals. By the end of the second round, the agreement had risen significantly, with seven of the eight categories (Eligibility, Identification, Treatment, Medical Consultation, Comorbidity, Outcome, and Other) achieving between 90.9% and 100% agreement, and all of them above 81.8% (see Table 3). This increase reflects the success of the iterative process in aligning the MDS with expert expectations and the needs of healthcare providers dealing with RD in Brazil. Generally, consensus levels between 70% and 80% are commonly accepted in Delphi studies to validate critical agreement among discussion groups and expert committees, suggesting a convergence of opinions. Reaching this level of agreement, and even exceeding 80%, suggests robustness and process effectiveness. It is also worth noting that this threshold may vary depending on the study contexts (Diamond et al. 2014). In this case, the heterogeneity of the RD domain, intrinsic to the multidisciplinary nature of the health fields involved in care, assistance, research, and development, combined with the large volume and variety of data routinely generated, has created an environment with significant gaps in structured knowledge, which is often fragmented (Bernardi et al. 2023). Thus, by reaching this level of agreement and considering the study context, we deemed the number of rounds sufficient and concluded this study phase. Since all the categories had more than 80.0% agreement, no more rounds were deemed necessary, and the MDS that resulted from the second round was considered the final version. Compared with the initial MDS, the final MDS (Supplementary Material 2 – Detailed Variable List) included nine new fields, with the Treatment and Outcome categories adding the most (three each). Nine fields were also removed, with the Diagnosis category experiencing the most removals (6 fields). Furthermore, adjustments were made to the Portuguese of four fields to prevent potential misunderstandings regarding their meaning (Table 3). Table 3: Changes in MDS throughout rounds CMD Questions (n) Additions Removals Edits Original 59 - - - After the first round 62 7 4 3 After the second round (Final) 59 2 5 1 TOTAL - 9 9 4 Note: Experts decided to add, remove, or edit MDS questions throughout the rounds. In summary, the Delphi process fostered a thoughtful, collaborative approach to balancing thorough data collection with efficiency. By adding fields that provide deeper insights into the complexities of RD and removing redundant or impractical ones, the final structure ensures that the data captured is both meaningful and practical, supporting better research, clinical care, and health outcomes. Discussion Our study revealed significant results that contribute to improving public health systems and managing RD information in Brazil. Through the Delphi technique, we identified a consensus among experts on including essential data in the MDS, such as demographic information, diagnosis, treatment, and clinical history. However, divergences arose in areas such as categorizing comorbidities and collecting data on quality of life. The results reinforce the importance of a standardized MDS for RD(Bernardi et al. 2023 ), since it helps ensure data quality and the effectiveness of health policies (Montserrat Moliner and Waligora 2017 ; BRASIL 2021; Adachi et al. 2023 ). The observed divergences indicate greater clarity and standardization in key areas essential for understanding individuals' needs (Iriart et al. 2019 ; Mehl et al. 2021 ). The MDS offers a practical solution to address data fragmentation, particularly by promoting interoperability and enabling the efficient reuse of information on a global scale. It also facilitates the systematic organization of data into structured categories, enhancing the potential for analysis and information sharing across healthcare units and research networks. Including fields specific to clinical outcomes and treatments provides a more detailed understanding of individual needs and responses, which is crucial for planning interventions and monitoring results (Panitz and Rodrigues 2024 ). Our study's findings align with previous research that highlights the importance of standardizing MDS to improve the management of RD processes. Dr. Bulathsinhala et al. (Bulathsinhala et al. 2019 ) and Sansone et al. (Sansone et al. 2020 ) emphasize the need for standardized outcome measures to monitor disease progression and assess treatment efficacy in RD populations (Bulathsinhala et al. 2019 ; Sansone et al. 2020 ). Slayter et al. (Slayter et al. 2021 ) studied SMA in Canada using a similar Delphi methodology and found the need for a standardized toolkit to improve high-quality data monitoring and collection (Slayter et al. 2021 ). Our results corroborate these findings but also advance by identifying specific areas that require greater standardization. The standard data infrastructure must incorporate a strategy suited to aggregating and correlating health data in a findable, accessible, interoperable, and reusable manner. This approach enhances data quality, facilitates networked collaboration, and contributes to achieving the objectives outlined in national health policies. Data interoperability is a critical factor in meeting these goals, as the networked operation of health systems involves multiple nodes across different levels of complexity. These nodes must function in an integrated and adaptive manner to efficiently and effectively address the population's needs at all healthcare delivery (Touré et al. 2023 ). Interoperability should not be limited to operational information system data internal to specific institutions. Still, it must also ensure the availability of these data for external government information systems with a strategic role. This aims to enhance decision-making and strengthen regional, national, and global actions. To achieve this, the ecosystem requires data that is accessible and comprehensible to both humans and credentialed machines (Touré et al. 2023 ; Johansson et al. 2024 ). In this context, the principles of the Semantic Web emerge as fundamental pillars for defining structured and semantically annotated data. This approach enables not only the representation of data but also the precise contextualization of information, reducing ambiguities and preventing misinformation within the healthcare system (Touré et al. 2023 ). The development of MDS must also consider the interoperability and integration of data with existing systems, such as electronic health records and digital health platforms. Standardized terminologies, compatible formats, and interoperable technologies are critical for achieving the MDS's full potential and preventing data fragmentation or redundancy (Choquet et al. 2015 ). In this way, data, information, and knowledge can be structured, distributed, and disseminated accurately, precisely, and effectively, contributing to advancing evidence-based medicine. Furthermore, this structuring enables the extrapolation of results into tangible benefits for the population, facilitating the development of public policies that effectively address social demands in the healthcare sector (Johansson et al. 2024 ). In the context of RD, European initiatives such as the European Joint Programme on Rare Diseases (EJP RD) highlight how integrating Common Data Elements with domain-specific elements can significantly improve research quality and healthcare. These initiatives underscore the importance of aligning international efforts to overcome technical and cultural barriers and emphasize the need for flexible systems that evolve to meet emerging research and clinical practice needs (Abaza et al. 2022 ). Implementing the proposed MDS can significantly improve the coordination of care and monitoring of RD in Brazil. Integrating standardized data into the SUS can facilitate resource allocation and enhance healthcare quality. The literature suggests that standardizing MDS can lead to a better understanding of RD trajectories and treatment responses (Hodgkinson et al. 2020 ). In addition, the MDS's structured nature allows for future integration with genomic databases, supporting precision medicine efforts and enhancing epidemiological surveillance in RD (Chabanon et al. 2018 ). Brazil's vast geographic and regional diversity also poses unique challenges, such as disparities in access to healthcare services and variability in data collection capacity. Implementing a robust MDS requires strategies for regional adaptation and continuous training of teams responsible for data entry and analysis (Panitz and Rodrigues 2024 ). In Brazil, the Ministry of Health, in conjunction with the SUS, establishes the premises of the national health policy. These guidelines encompass tools and processes that support management, decision-making, monitoring health information systems and indicators, and evaluating healthcare services and measuring their impact. Additionally, this framework includes developing and structuring the regulatory network, which governs patient referral processes across different levels of care, from primary to highly complex services, ensuring compliance with the legal commitment to provide universal and comprehensive healthcare. To ensure the success of these procedures, MDSs are an informational device provided for in this national plan (BRASIL 2024). As established in Brazil's national health policy, MDSs constitute a strategy to reduce the fragmentation of healthcare systems by standardizing clinical-administrative data and optimizing the integrated operation of the SUS network. Adopting this strategy will be mandatory for all health systems in the country by 2027 to support management, planning, monitoring, and evaluation activities. Additionally, MDSs play a fundamental role in formulating, monitoring, and updating public policies, contributing to the development of national health statistics. In this way, they enable a more accurate characterization of the population's demographic and epidemiological profile, enhancing metrics for performance analysis and the efficient allocation of public resources (BRASIL 2024). One of our study's main strengths is the Delphi methodology, which allows for a structured and iterative approach to gathering and refining expert opinions. This methodology facilitates the identification of consensus among professionals from diverse backgrounds while accommodating differing perspectives. The iterative nature of the process ensures that all suggestions are thoroughly evaluated and disagreements are addressed systematically, leading to more robust and actionable outcomes (Diamond et al. 2014 ). By incorporating multiple rounds of feedback and analysis, the Delphi method enhances the reliability and validity of the final results, particularly in complex and multidisciplinary fields such as RD (Roni and Djajadikerta 2021 ; Panitz and Rodrigues 2024 ). However, the representativeness of the participants can be a limitation, as only a subset of RD experts in Brazil participated. Another limitation is the lack of longitudinal data to validate the proposed MDS, suggesting the need for future studies to confirm its effectiveness and applicability. During MDS validation, challenges were observed, especially in the quality of life and comorbidities categories. These areas are inherently subjective and multidimensional, often leading to disagreements on priority variables. Addressing these challenges may involve complementary approaches, including the active participation of people living with RDs and their caregivers, whose perspectives can significantly enrich the development of more user-centered indicators (Revorêdo et al. 2015 ). Future studies should focus on validating the proposed MDS in different regional and international contexts. Investigating the MDS's applicability in various health settings and populations can provide valuable insights to enhance data standardization further. Moreover, as suggested by Messina and Sframeli (Messina and Sframeli 2020 ) and Sansone et al. (Sansone et al. 2020 ), the inclusion of quality-of-life metrics should be explored more deeply to ensure a comprehensive and person-centered approach. Notably, the consensus threshold has been achieved and exceeded in a field as heterogeneous as RD. The reproducibility and transparency of the Delphi method used also make it a valuable reference for other countries seeking to establish MDS for RD from early-stage initiatives. The diversity of these conditions, each with distinct clinical manifestations, diagnostic challenges (Félix et al. 2022 ), and management needs (Alves et al. 2021 ), makes standardization particularly complex. Reaching an agreement despite this variability underscores the potential applicability of the findings in shaping more cohesive and practical approaches to RD research and care. To address potential biases inherent in the Delphi method, careful measures were implemented to ensure diversity and representativeness among the experts, neutrality in the design of questions, and fairness in the analysis of responses. These steps aimed to mitigate biases such as conformity, anchoring, dominance, and homogeneity. Anonymity in responses and blinded rounds were instrumental in minimizing interpersonal influences. Additionally, both qualitative and quantitative analyses were employed. Although statistical tools such as Kendall’s coefficient of concordance and intraclass correlation were tested, they showed low agreement levels and were not retained for final analysis. Instead, the CVI was adopted as the primary metric for evaluating expert consensus, based on the proportion of agreement among participants in each round (Niederberger and Spranger 2020 ). The transparency in documenting the entire process was pivotal for identifying potential limitations and enhancing reproducibility. Strategies such as consistency checks between rounds and blinded procedures further ensured the validity and reliability of the findings. Conclusion The standardization of an MDS represents a significant advance in managing information on RD in Brazil. The approach enabled defining essential variables and identifying gaps and challenges that can guide future improvements. Integrating this MDS into the public healthcare system will improve the quality of records, facilitate health monitoring, formulate data-driven policies, and develop research that expands knowledge and available solutions for RD. However, this initiative's success depends on careful implementation, with investment in infrastructure, professional training, and continuous improvement of data collection and analysis tools. Declarations Competing interests The authors declare no competing interests. The authors acknowledge using generative AI tools (e.g., ChatGPT) for language refinement. All content, ideas, and conclusions presented are solely those of the authors. Funding The Brazilian National Council for Scientific and Technological Development (CNPq) supported this study (grant number 443030/2019-7) as part of the Brazilian Rare Diseases Network project. CNPq also supports TMF (grant number 306861/2019-4). The São Paulo State Research Support Foundation (FAPESP) funded this study as part of the PPPP project (grant number 2023/10203-8), which DA coordinated. Acknowledgements The authors would like to thank all collaborators of the RARAS network for their invaluable contributions to this study. Their dedication and expertise in data collection, analysis, and governance were instrumental in advancing this research. We also acknowledge the support the participating institutions and professionals provided during the development and implementation of the RARAS initiative. A complete list of consortium members who contributed to this study is provided in Supplementary Material 5. Data Availability The dataset associated with this study has been deposited in publicly accessible repositories to ensure transparency and reproducibility. The primary data is available in the Lattes Data repository under the following DOI: doi:10.57810/lattesdata/XEL53O. Additionally, a copy of the dataset has been made available on Figshare for broader accessibility: 10.6084/m9.figshare.26324938. These repositories provide persistent identifiers and support open data-sharing standards, ensuring the dataset can be accessed, referenced, and reused in future research. Code Availability The analysis and tools are publicly available on GitHub: https://github.com/RARAS-BR. This study's metadata and associated standards are registered and accessible via the FAIRsharing record (https://fairsharing.org/10.25504/FAIRsharing.d7b6c8) and the DMP at the ARGOS record, which has the DOI 10.5281/zenodo.13643431 and is available at https://argos.openaire.eu/explore-plans/overview/public/8d4a6851-f96e-4a29-ad79-37e8bf30d17a). References Abaza H, Kadioglu D, Martin S, et al. Domain-specific common data elements for rare disease registration: Conceptual approach of a European joint initiative toward semantic interoperability in rare disease research. JMIR Med Inform . 2022;10. https://doi.org/10.2196/32158 Adachi T, El-Hattab AW, Jain R, et al. Enhancing equitable access to rare disease diagnosis and treatment around the world: A review of evidence, policies, and challenges. Int J Environ Res Public Health . 2023;20:4732. https://doi.org/10.3390/IJERPH20064732 Alves D, Yamada DB, Bernardi FA, et al. Mapping, infrastructure, and data analysis for the Brazilian network of rare diseases: Protocol for the RARASnet observational cohort study. JMIR Res Protoc . 2021. https://doi.org/10.2196/24826 Alwateer M, Atlam E-S, Mohammed M, et al. Missing data imputation: A comprehensive review. J Comput Commun . 2024;12:53–75. https://doi.org/10.4236/jcc.2024.1211004 Bernardi FA, de Oliveira BM, Yamada DB, et al. The minimum data set for rare diseases: Systematic review. J Med Internet Res . 2023. Brasil. Ministério da Saúde. Política Nacional de Informação e Informática em Saúde (PNIIS). 2021 [cited 2023 Jul 23]. Available from: https://bvsms.saude.gov.br/bvs/saudelegis/cns/2022/res0659_15_06_2022.html Brasil. Ministério da Saúde. Plano Nacional de Saúde - PNS 2024–2027 — Ministério da Saúde. 2024 [cited 2025 Feb 10]. Available from: https://www.gov.br/saude/pt-br/acesso-a-informacao/gestao-do-sus/instrumentos-de-planejamento/pns/plano-nacional-de-saude-pns-2024-2027/view Bulathsinhala L, Eleangovan N, Heaney LG, et al. Development of the International Severe Asthma Registry (ISAR): A modified Delphi study. J Allergy Clin Immunol Pract . 2019;7:578–88.e2. https://doi.org/10.1016/j.jaip.2018.08.016 Chabanon A, Seferian AM, Daron A, et al. Prospective and longitudinal natural history study of patients with type 2 and 3 spinal muscular atrophy: Baseline data NatHis-SMA study. PLoS One . 2018;13:e0201004. https://doi.org/10.1371/journal.pone.0201004 Choquet R, Maaroufi M, De Carrara A, et al. A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research. J Am Med Inform Assoc . 2015;22:76–85. https://doi.org/10.1136/amiajnl-2014-002794 Diamond IR, Grant RC, Feldman BM, et al. Defining consensus: a systematic review recommends methodologic criteria for reporting of Delphi studies. J Clin Epidemiol . 2014;67:401–9. https://doi.org/10.1016/j.jclinepi.2013.12.002 Drumm S, Bradley C, Moriarty F. ‘More of an art than a science’? The development, design and mechanics of the Delphi Technique. Res Soc Adm Pharm . 2022;18:2230–6. https://doi.org/10.1016/j.sapharm.2021.06.027 Félix TM, de Oliveira BM, Artifon M, et al. Epidemiology of rare diseases in Brazil: protocol of the Brazilian Rare Diseases Network (RARAS-BRDN). Orphanet J Rare Dis . 2022. https://doi.org/10.1186/s13023-022-02254-4 Harris PA, Taylor R, Thielke R, et al. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform . 2009;42:377–81. https://doi.org/10.1016/j.jbi.2008.08.010 Hodgkinson VL, Oskoui M, Lounsberry J, et al. A National Spinal Muscular Atrophy Registry for real-world evidence. Can J Neurol Sci . 2020;47:810–5. https://doi.org/10.1017/cjn.2020.111 Iriart JAB, Nucci MF, Muniz TP, et al. From the search for diagnosis to treatment uncertainties: challenges of care for rare genetic diseases in Brazil. Cien Saude Colet . 2019;24:3637. https://doi.org/10.1590/1413-812320182410.01612019 Johansson LF, Laurie S, Spalding D, et al. An interconnected data infrastructure to support large-scale rare disease research. Gigascience . 2024;13. https://doi.org/10.1093/gigascience/giae058 Mehl G, Tunçalp Ö, Ratanaprayul N, et al. WHO SMART guidelines: optimising country-level use of guideline recommendations in the digital age. Lancet Digit Health . 2021;3:e213–6. https://doi.org/10.1016/s2589-7500(21)00038-8 Messina S, Sframeli M. New treatments in spinal muscular atrophy: positive results and new challenges. J Clin Med . 2020;9:1–16. https://doi.org/10.3390/jcm9072222 Montserrat Moliner A, Waligora J. The European Union policy in the field of rare diseases. Adv Exp Med Biol . 2017;1031:561–87. https://doi.org/10.1007/978-3-319-67144-4_30 Niederberger M, Spranger J. Delphi Technique in Health Sciences: A Map. Front Public Health . 2020;8:561103. https://doi.org/10.3389/fpubh.2020.00457 Okoli C, Pawlowski SD. The Delphi method as a research tool: an example, design considerations and applications. Information & Management . 2004;42:15–29. https://doi.org/10.1016/j.im.2003.11.002 Panitz LM, Rodrigues W. BRAZILIAN MINIMUM BASIC DATA SET (CMD): Fundamentals, development and implementation. Perspectivas em Ciência da Informação . 2024;29:e-46451. https://doi.org/10.1590/1981-5344/46451 Revorêdo LDS, Maia RS, Torres GDV, Chaves Maia EM. O Uso Da Técnica Delphi Em Saúde: Uma Revisão Integrativa De Estudos Brasileiros. Revista Arquivos de Ciências da Saúde . 2015;22:16. https://doi.org/10.17696/2318-3691.22.2.2015.136 Roni SM, Djajadikerta HG. Data Analysis with SPSS for Survey-based Research . 1st ed. Springer Singapore; 2021. Sansone VA, Walter MC, Attarian S, et al. Measuring Outcomes in Adults with Spinal Muscular Atrophy – Challenges and Future Directions – Meeting Report. J Neuromuscul Dis . 2020;7:523–34. https://doi.org/10.3233/jnd-200534 Slayter J, Hodgkinson V, Lounsberry J, et al. A Canadian Adult Spinal Muscular Atrophy Outcome Measures Toolkit: Results of a National Consensus using a Modified Delphi Method. J Neuromuscul Dis . 2021;8:579–88. https://doi.org/10.3233/jnd-200617 Touré V, Krauss P, Gnodtke K, et al. FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network. Scientific Data . 2023;10:1–11. https://doi.org/10.1038/s41597-023-02028-y World Health Organization. Rare diseases [Internet]. 2023. Available from: https://www.who.int/standards/classifications/frequently-asked-questions/rare-diseases. Accessed 11 Jul 2023. Additional Declarations No competing interests reported. Supplementary Files 1SupplementaryMaterialQuestionnaire.pdf 1 - Questionnaire: The Delphi questionnaire used in the study, including the 55 questions across nine data categories (e.g., Participant Characterization, Eligibility, Identification, etc.), could be shared as supplementary material. This would include questions P1, P2, and P3 and their respective response scales. 2SupplementaryMaterialDetailedVariableList.xlsx 2- Detailed Variable List: A detailed list of the MDS variables at different stages (initial, intermediate, and final) could be made available, highlighting the added, removed, or modified fields in each Delphi round, as mentioned in the results (Table 3). 3SupplementaryMaterialParticipantDemographics.pdf 3- Participant Demographics: A more detailed table of participant demographics, including geographic distribution, years of experience, and professional roles, could provide additional context for the expert contributions. 4SupplementaryMaterialRoundbyRoundAnalysisResults.pdf 4- Round-by-Round Analysis Results: As supplementary material, detailed reports of the quantitative and qualitative analyses for each Delphi round, including graphs or tables of consensus levels by category, could be included. 5SupplementaryMaterial5ListofContributorsEN.docx 5- List of Contributors: This document lists all professionals and institutions that contributed to the Delphi validation process and supported the development of the Brazilian Rare Diseases Minimum Data Set, including names, affiliations, and roles in the study. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Jun, 2025 Reviews received at journal 07 Jun, 2025 Reviews received at journal 06 Jun, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers agreed at journal 27 May, 2025 Reviewers agreed at journal 27 May, 2025 Reviewers invited by journal 27 May, 2025 Editor assigned by journal 21 May, 2025 Submission checks completed at journal 21 May, 2025 First submitted to journal 19 May, 2025 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-6701865","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463305639,"identity":"78a6c8e8-6332-4175-892f-df79685b9f0d","order_by":0,"name":"Filipe Andrade Bernardi","email":"data:image/png;base64,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","orcid":"","institution":"Laboratory of Health Intelligence (LIS), Ribeirão Preto Medical School - 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Questionnaire:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Delphi questionnaire used in the study, including the 55 questions across nine data categories (e.g., Participant Characterization, Eligibility, Identification, etc.), could be shared as supplementary material. This would include questions P1, P2, and P3 and their respective response scales.\u003c/p\u003e","description":"","filename":"1SupplementaryMaterialQuestionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6701865/v1/8ffbe70fef2132695bc9820e.pdf"},{"id":83591810,"identity":"c22bddf7-2278-4305-9510-cf89ee1cc52c","added_by":"auto","created_at":"2025-05-29 06:24:04","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":191506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2- Detailed Variable List:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA detailed list of the MDS variables at different stages (initial, intermediate, and final) could be made available, highlighting the added, removed, or modified fields in each Delphi round, as mentioned in the results (Table 3).\u003c/p\u003e","description":"","filename":"2SupplementaryMaterialDetailedVariableList.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6701865/v1/066055cf2186fc8f7c70b176.xlsx"},{"id":83591809,"identity":"a1c843a1-eaba-4b0a-96cb-0d79abc9334a","added_by":"auto","created_at":"2025-05-29 06:24:04","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":58638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3- Participant Demographics\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eA more detailed table of participant demographics, including geographic distribution, years of experience, and professional roles, could provide additional context for the expert contributions.\u003c/p\u003e","description":"","filename":"3SupplementaryMaterialParticipantDemographics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6701865/v1/1f27b6a1de7547def53bb5a2.pdf"},{"id":83591336,"identity":"bf646360-ff73-404a-91f1-d4213938ed98","added_by":"auto","created_at":"2025-05-29 06:16:04","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":576349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e4- Round-by-Round Analysis Results\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAs supplementary material, detailed reports of the quantitative and qualitative analyses for each Delphi round, including graphs or tables of consensus levels by category, could be included.\u003c/p\u003e","description":"","filename":"4SupplementaryMaterialRoundbyRoundAnalysisResults.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6701865/v1/d3f8d94bc0e6dd7a4fc46200.pdf"},{"id":83591333,"identity":"c081759c-b208-4b1e-aa75-052ca27904e2","added_by":"auto","created_at":"2025-05-29 06:16:04","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":30275,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e5- List of Contributors\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis document lists all professionals and institutions that contributed to the Delphi validation process and supported the development of the Brazilian Rare Diseases Minimum Data Set, including names, affiliations, and roles in the study.\u003c/p\u003e","description":"","filename":"5SupplementaryMaterial5ListofContributorsEN.docx","url":"https://assets-eu.researchsquare.com/files/rs-6701865/v1/242fa52efa8a6999241ec69f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Standardizing Rare Disease Data in Brazil: A Delphi-Based Approach for the First National Registry","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRare diseases (RD) affect a small percentage of the population, typically less than 1 in 2,000 individuals (Montserrat Moliner and Waligora 2017). Despite their low prevalence, they impact millions worldwide, with over 7,000 distinct RDs identified (World Health Organization 2023). The heterogeneous nature of these diseases presents significant challenges for healthcare systems, including delayed diagnoses, limited treatment options, and a need for standardized care protocols. Due to their low prevalence, people living with RD frequently face delayed diagnoses, restricted access to effective treatment, and a lack of standardized care protocols (Adachi et al. 2023).\u003c/p\u003e\n\u003cp\u003eManaging RD in Brazil is particularly challenging due to the country\u0026apos;s vast geographic diversity and regional disparities in healthcare access. Many genetic services essential for diagnosing and treating RD are concentrated in public universities and reference hospitals in large urban centers, limiting accessibility for individuals needing these services (Iriart et al. 2019). Thus, remote areas often struggle with limited resources, leading to an unequal distribution of healthcare services, which hinders the timely diagnosis and treatment of RD.\u003c/p\u003e\n\u003cp\u003eMoreover, despite establishing the Brazilian Policy for Comprehensive Care for People with Rare Diseases, which aims to ensure comprehensive care and implement actions to reduce morbidity and mortality in this population, many challenges persist in achieving these objectives (BRASIL 2021). Among the main obstacles are the lack of standardized information, issues with data sharing between the various health network nodes, and challenges observed in other parts of the world (Alves et al. 2021; F\u0026eacute;lix et al. 2022).\u003c/p\u003e\n\u003cp\u003eTherefore, comprehensive and harmonized data collection is critical to effectively addressing these challenges, and this is where the Minimum Data Set (MDS) concept becomes vital. An MDS is a standardized collection of essential data elements necessary for the accurate documentation, analysis, and management of diseases. It enables consistent and comparable data across healthcare settings and facilitates better diagnosis, treatment, and research (Bernardi et al. 2023).\u003c/p\u003e\n\u003cp\u003eIn response to these challenges, the Brazilian Rare Diseases Network (RARAS) developed a specific MDS for RD to support high-quality data collection and interoperability within the national health system. By establishing a standardized set of core data elements, the MDS enables consistent documentation of RD cases, improves data exchange across healthcare levels, and facilitates integration into national and international registries. This is especially relevant in a complex system like Brazil\u0026rsquo;s Unified Health System (SUS), where regional disparities and fragmented information hinder effective coordination.\u003c/p\u003e\n\u003cp\u003eThis study aims to validate the proposed MDS through a Delphi consensus process with clinical experts. The resulting dataset represents essential information for RD case documentation, enabling accurate patient characterization and supporting evidence-based diagnosis, treatment, and policy development.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study is based on the systematic review findings conducted by Bernardi et al. (Bernardi et al. 2023). The review analyzed and compared MDSs used worldwide in health networks for RD. The review included 20 articles and identified significant terminology and structure variations among RD MDSs. This lack of uniformity hinders international collaboration and data comparison, complicating RD management (Bernardi et al. 2023). The research also compared the identified MDSs with World Health Organization (WHO) guidelines, pinpointing discrepancies and areas for improvement to align the MDSs with global best practices (Mehl et al. 2021). One of the study\u0026apos;s results included a recommendation of a MDS for RD (see Supplementary Material 2 \u0026ndash; Detailed Variable List, Initial Version).\u003c/p\u003e\n\u003cp\u003eMDSs are structured collections of essential and categorical variables representing clinical scenarios within health systems. They ensure consistency in data recording, support comprehensive documentation, and enable analysis and interoperability across institutions. In the RD context, where data are often fragmented and heterogeneous, standardizing information is crucial for improving diagnosis, care coordination, and research (Bernardi et al. 2023). The MDS proposed in this study builds upon global recommendations, including those from the WHO, and aims to unify data elements across healthcare levels and regions in Brazil (Mehl et al. 2021; Bernardi et al. 2023).\u003c/p\u003e\n\u003cp\u003eTo validate this MDS, we applied the Delphi method, a structured approach for achieving consensus among experts through iterative rounds of evaluation and feedback. Following the definition of the research question and selection of qualified participants, a facilitator coordinated the process, ensuring consistency, anonymity, and systematic communication (Niederberger and Spranger 2020). Experts responded to structured questionnaires assessing the relevance of variables. Their suggestions were reviewed by the research team and used to refine the dataset in subsequent rounds (Okoli and Pawlowski 2004). This methodology enabled transparent decision-making and continuous improvement of the MDS based on expert judgment.\u003c/p\u003e\n\u003cp\u003eResearchers analyze data from the first round, incorporate suggestions (if applicable), and construct a second-round questionnaire for items lacking consensus. This process is repeated until a consensus among experts or until a certain number of attempts is reached, depending on the research context. The Delphi method design includes the following elements: research question, expert selection, number and sample of experts, structured questionnaire construction, information and content provided to experts, feedback management, data analysis, feedback development and implementation, and research communication and administration (Niederberger and Spranger 2020; Drumm et al. 2022).\u003c/p\u003e\n\u003cp\u003eFigure 1 presents a detailed flowchart of the Delphi method used to validate the MDS in the context of RD in Brazil. The flowchart illustrates the main stages of the study, from initial preparation and conceptual analysis to questionnaire development, participant selection, and consensus refinement. It highlights the iterative interaction between experts and researchers to achieve the final consensus on the MDS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuestionnaire Preparation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire contained 55 questions across nine data categories: Participant Characterization, Eligibility, Identification, Diagnosis, Treatment, Medical Consultation, Comorbidity, Outcome, and Other. In the first round, the Participant Characterization section only had one question (P0) that assessed participants\u0026apos; experience with MDS in healthcare. The questionnaire used only questions P1, P2, and P3 for the remaining eight categories representing the MDS in the reference study. P1 evaluated participants\u0026apos; agreement on the appropriateness of variables in each category, while P2 and P3 focused on whether variables should be added or removed from these categories, respectively (Table 4).\u003cbr\u003e\u0026nbsp; The questionnaire was designed and implemented using the Research Electronic Data Capture (REDCap) platform\u0026apos;s survey module. This feature allows researchers to collect data anonymously, ensuring participant confidentiality. Each respondent received a unique, randomized survey link via email, preventing the identification of individual responses while enabling submission status tracking. The survey design also incorporated features such as branching logic, which dynamically adjusted the displayed questions based on previous answers, ensuring that participants only interacted with relevant sections of the questionnaire. REDCap is a secure, web-based application widely used for research studies due to its compliance with international data protection regulations. The platform provides automated workflows for survey distribution, including scheduled email invitations and reminders, which were utilized to maximize participant engagement. All collected data were stored on institutional REDCap servers with restricted access and regular backups, ensuring data security and integrity (Harris et al. 2009).\u003c/p\u003e\n\u003cp\u003eParticipants rated their responses using five levels of agreement: strongly agree, agree, neutral, disagree, and strongly disagree. For P2 and P3, participants indicated whether they wanted to suggest adding or removing variables (Table 4). When they answered affirmatively, they were asked to provide qualitative justifications for their suggestions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Fundamental questions of the Delphi Study on Minimum Data Sets.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePossible Answers\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 style=\"width: 36px;\"\u003e\n \u003cp\u003eP0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003eBased on your experience with the use of Minimum Data Sets in healthcare, please indicate the extent to which you agree or disagree with the following statement: \u0026quot;I use or have used a Minimum Data Set (MDS) in healthcare as part of my professional activities (service, care, management, or research), either at the institution mentioned or another institution.\u0026quot; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026apos;Strongly agree\u0026apos; or \u0026apos;Agree\u0026apos; or \u0026apos;Neutral\u0026rsquo; or \u0026lsquo;Disagree\u0026rsquo; or \u0026apos;Strongly disagree\u0026apos; or\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u0026apos;I don\u0026apos;t know\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u0026quot;The data/variables described in the * category encompass the processes carried out (service, care, management, or research) in my institution regarding RD.\u0026quot; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026apos;Strongly agree\u0026apos; or \u0026apos;Agree\u0026apos; or \u0026apos;Neutral\u0026rsquo; or \u0026lsquo;Disagree\u0026rsquo; or \u0026apos;Strongly disagree\u0026apos; or\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u0026apos;I don\u0026apos;t know\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003eBased on the context of the healthcare institution you are involved in, would you add data/variables in the * category beyond those already described in the Fundamental MDS document?\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026apos;Yes\u0026apos; or \u0026apos;No\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003eBased on the context of the healthcare institution you are involved in, would you remove data/variables in the * category? \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026apos;Yes\u0026apos; or \u0026apos;No\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: * The asterisk (*) represents a placeholder replaced in each question section by one of the eight variables: eligibility, identification, diagnosis, treatment, medical consultation, comorbidities, outcome, and others.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants were sent the questionnaire to evaluate the original MDS, and their responses were then compiled and analyzed to create an adapted MDS. All responses were de-identified during the data extraction process to guarantee further anonymity. This ensured the research team could analyze the data without linking responses to individual participants. This process was repeated at the end of each round.\u003c/p\u003e\n\u003cp\u003eExperts in the field of RD at the RARAS network were invited to participate in the voluntary and anonymous study. Participants were briefed via an initial email that included a description of the study objectives, the survey\u0026rsquo;s anonymous format, and instructions for completing the questionnaire. They were informed that their responses would contribute to refining the MDS but that their identities would remain confidential throughout the process.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData analysis and synthesis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire responses were analyzed using a mixed-methods approach, incorporating quantitative and qualitative techniques. Responses were included if participants answered all questions or had, at most, one missing response. Incomplete questionnaires were reviewed to determine if the missing data could impact the results, and sensitivity analyses were conducted to assess the robustness of the findings.\u003c/p\u003e\n\u003cp\u003eData was extracted from REDCap in .csv format and imported into Google Sheets and statistical software for further processing. The REDCap platform provided built-in audit trails to ensure data integrity during extraction and analysis (Harris et al. 2009). Absolute and relative frequencies were computed for multiple-choice questions to identify group preferences, excluding respondents who indicated having no opinion on the subject.\u003c/p\u003e\n\u003cp\u003eThe research team adopted an 80% agreement threshold to determine consensus, a commonly used benchmark in Delphi studies for defining convergence among expert opinions (Diamond et al. 2014). Agreement levels were calculated for each variable and category of the MDS, both individually and collectively, by combining \u0026lsquo;Strongly agree\u0026apos; or \u0026apos;Agree\u0026apos; answers. Any variable or category that did not reach this threshold was subject to a new round of evaluation to refine the dataset and ensure comprehensive expert agreement.\u003c/p\u003e\n\u003cp\u003eTo address missing data, the research team analyzed and determined if the missing responses were randomly distributed or if there were patterns that could introduce bias into the results. Randomly missing data were deemed acceptable, whereas systematic missingness required further investigation. If significant gaps were identified or the missing data compromised the validity of a participant\u0026rsquo;s input, those responses were excluded based on pre-defined exclusion criteria. This ensured that only high-quality and representative data informed the consensus process (Alwateer et al. 2024).\u003c/p\u003e\n\u003cp\u003eQuantitative data analysis was conducted using the statistical software SPSS. Although concordance measures such as Kendall\u0026rsquo;s W, Fleiss\u0026rsquo; Kappa, and Cohen\u0026rsquo;s Kappa were tested, they showed low agreement levels, suggesting random response patterns across rounds. Therefore, we adopted the Content Validity Index (CVI) as the primary metric for assessing consensus. The CVI reflects the proportion of experts rated each item as \u0026ldquo;agree\u0026rdquo; or \u0026ldquo;strongly agree,\u0026rdquo; relative to the total number of responses. Delphi studies commonly use this approach to evaluate content validity and item relevance, especially when expert panel sizes are limited. Items reaching or exceeding the 80% agreement threshold were considered validated and retained in the final version of the MDS (Roni and Djajadikerta 2021).\u003c/p\u003e\n\u003cp\u003eA qualitative content analysis was conducted for open-ended questions. Suggestions for adding or removing data were coded and categorized to identify recurring themes and ensure alignment with the study objectives. To validate the relevance of these suggestions, two independent experts (T.M.F. and B.M.O. - not involved in the Delphi panel) reviewed each proposed addition or removal. The experts evaluated the alignment of the suggestions with the MDS framework, their relevance to RD, and potential overlaps with existing fields. The unanimous agreement of these reviewers was required for a suggestion to be accepted or rejected.\u003c/p\u003e\n\u003cp\u003eThe iterative Delphi process allowed the research team to systematically compile and analyze results from each round. Each round\u0026apos;s findings were summarized in a report shared with participants in a special session after the monthly RARAS meeting. This ensured transparency and provided feedback on how their input contributed to the evolving MDS. The summary included visualizations such as bar charts and heatmaps to display agreement levels and highlight areas requiring further discussion. These results generated new MDS versions and served as the basis for subsequent rounds until a consensus was reached.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical concerns\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human participants, data, or tissue. Ethics approval and consent to participate are not applicable, as this study was based solely on publicly available datasets and did not recruit new human participants or collect data. However, the primary study that originated this research was approved by the Research Ethics Committee (REC) of the Hospital de Cl\u0026iacute;nicas de Porto Alegre, Brazil (approval number: 33970820.0.1001.5327).\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e\u003cem\u003eDescriptive analysis of participants\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eA total of 52 health professionals were invited to take part in this study. In the first round, 16 participants provided valid responses, resulting in a response rate of 30.8%. In the second round, only 11 initial respondents (68.75%) completed the questionnaire. The participants had significant field experience, averaging 20.37 (\u0026plusmn; 8.91) years. Their agreement with MDS in daily practice was 75.0%, indicating a solid use of MDS for other clinical fields. Among the participants, professors were the largest group at 38.9%, followed by the doctors/preceptors at 27.8% (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: Distribution of participants by professional position.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"494\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosition held\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants (%)\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 style=\"width: 184px;\"\u003e\n \u003cp\u003eProfessor/Lecturer*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e41.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eDoctor/Preceptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e29.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eResearch Coordinator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e5.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eRD Reference Service Coordinator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e5.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eOutpatient Services Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e11.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eDepartment Coordinator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e5.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*: One professional reported working as a teacher in addition to their roles as Department Coordinators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIterative Refinement of the MDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Delphi process played a pivotal role in refining the MDS variables, gathering expert feedback over two rounds, and ensuring that the final version was comprehensive, practical, and meaningful for documenting RD. Initially, the MDS contained 59 fields across eight data categories: Participant Characterization, Eligibility, Identification, Diagnosis, Treatment, Medical Consultation, Comorbidity, and Outcome (Supplementary Material 2 \u0026ndash; Detailed Variable List, Initial Version). Through careful discussion and consensus, adjustments were made to balance the breadth of data collected with its usability and efficiency.\u003c/p\u003e\n\u003cp\u003eIn the first round of the study, six categories had participants\u0026rsquo; agreement above 80.0%, with the Identification category showing the highest level of agreement (100.0%), and Consultation (75.0%) and Outcome (56.3%) exhibiting the lowest level of agreement.\u003c/p\u003e\n\u003cp\u003eParticipants\u0026apos; suggestions showed that Treatment (n=10) and Medical Consultation (n=10) received the highest number of suggestions for addition, followed by Outcome (n=8). The categories Identification (n=7), and Diagnosis (n=7) had the most removal suggestions, with participants strongly perceiving redundancy, whereas Treatment (n=1) and Comorbidity (n=0) exhibited the highest level of agreement. The distribution of suggestions for additions and removals by category is summarized in Table 2.\u003c/p\u003e\n\u003cp\u003eSeven new fields were added after the experts assessed the relevance during the first round (Supplementary Material 2 \u0026ndash; Detailed Variable List). Treatment and Outcome had the highest added fields (3 and 2, respectively). Four fields were removed ( Supplementary Material 2 \u0026ndash; Detailed Variable List), two of which were in the Diagnosis category (Table 2). Thus, the MDS was compiled after the first round and used for evaluation by the participants in the second round, which comprised 62 fields (Supplementary Material 4 \u0026ndash; Round-by-Round Analysis Results).\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; agreement in all categories was above 80.0% in the second round. Eligibility, Treatment, and Comorbidity had total agreement (100.0%); Identification, Medical Consultation, Outcome, and Other had high agreement (90.9%); and Diagnosis exhibited the lowest level of agreement (81.5%).\u003cbr\u003e\u0026nbsp; The total number of suggestions in this round had significantly decreased (from 50 to 13). Medical Consultation remained the category with the most suggestions for addition, though these decreased from 10 in the first round to 4 in the second. Comorbidity maintained a high level of agreement, with no suggestions for addition. Regarding removal, Diagnosis continued to receive the most tips for exclusion. However, the number decreased from 7 in the first round to 4 in the second.\u003c/p\u003e\n\u003cp\u003eIn this round, the experts\u0026apos; validation of the suggestions resulted in adding two new fields and removing five, four of which were in the Diagnosis category (Table 3). Thus, the MDS compiled after the second round comprised 59 fields (see Supplementary Material 4 \u0026ndash; Round-by-Round Analysis Results). This final version of the MDS is intended to serve as a reference for future implementation in national RD registries and healthcare information systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Participants\u0026apos; suggestions for adding and removing variables across Delphi Study rounds.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditions (Round 1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditions\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Round 2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRemovals\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Round 1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRemovals\u0026nbsp;\u003cbr\u003e\u0026nbsp;(Round 2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eEligibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eIdentification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eMedical Consultation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOTAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsensus Achievement and Agreement Levels\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Delphi rounds demonstrated a growing consensus among health professionals. By the end of the second round, the agreement had risen significantly, with seven of the eight categories (Eligibility, Identification, Treatment, Medical Consultation, Comorbidity, Outcome, and Other) achieving between 90.9% and 100% agreement, and all of them above 81.8% (see Table 3). This increase reflects the success of the iterative process in aligning the MDS with expert expectations and the needs of healthcare providers dealing with RD in Brazil.\u003c/p\u003e\n\u003cp\u003eGenerally, consensus levels between 70% and 80% are commonly accepted in Delphi studies to validate critical agreement among discussion groups and expert committees, suggesting a convergence of opinions. Reaching this level of agreement, and even exceeding 80%, suggests robustness and process effectiveness. It is also worth noting that this threshold may vary depending on the study contexts (Diamond et al. 2014).\u003c/p\u003e\n\u003cp\u003eIn this case, the heterogeneity of the RD domain, intrinsic to the multidisciplinary nature of the health fields involved in care, assistance, research, and development, combined with the large volume and variety of data routinely generated, has created an environment with significant gaps in structured knowledge, which is often fragmented (Bernardi et al. 2023). Thus, by reaching this level of agreement and considering the study context, we deemed the number of rounds sufficient and concluded this study phase.\u003c/p\u003e\n\u003cp\u003eSince all the categories had more than 80.0% agreement, no more rounds were deemed necessary, and the MDS that resulted from the second round was considered the final version. Compared with the initial MDS, the final MDS (Supplementary Material 2 \u0026ndash; Detailed Variable List) included nine new fields, with the Treatment and Outcome categories adding the most (three each). Nine fields were also removed, with the Diagnosis category experiencing the most removals (6 fields). Furthermore, adjustments were made to the Portuguese of four fields to prevent potential misunderstandings regarding their meaning (Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3: Changes in MDS throughout rounds\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"563\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestions (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRemovals\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEdits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eOriginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eAfter the first round\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eAfter the second round (Final)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOTAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Experts decided to add, remove, or edit MDS questions throughout the rounds.\u003c/p\u003e\n\u003cp\u003eIn summary, the Delphi process fostered a thoughtful, collaborative approach to balancing thorough data collection with efficiency. By adding fields that provide deeper insights into the complexities of RD and removing redundant or impractical ones, the final structure ensures that the data captured is both meaningful and practical, supporting better research, clinical care, and health outcomes.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study revealed significant results that contribute to improving public health systems and managing RD information in Brazil. Through the Delphi technique, we identified a consensus among experts on including essential data in the MDS, such as demographic information, diagnosis, treatment, and clinical history. However, divergences arose in areas such as categorizing comorbidities and collecting data on quality of life. The results reinforce the importance of a standardized MDS for RD(Bernardi et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), since it helps ensure data quality and the effectiveness of health policies (Montserrat Moliner and Waligora \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; BRASIL 2021; Adachi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The observed divergences indicate greater clarity and standardization in key areas essential for understanding individuals' needs (Iriart et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mehl et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MDS offers a practical solution to address data fragmentation, particularly by promoting interoperability and enabling the efficient reuse of information on a global scale. It also facilitates the systematic organization of data into structured categories, enhancing the potential for analysis and information sharing across healthcare units and research networks. Including fields specific to clinical outcomes and treatments provides a more detailed understanding of individual needs and responses, which is crucial for planning interventions and monitoring results (Panitz and Rodrigues \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study's findings align with previous research that highlights the importance of standardizing MDS to improve the management of RD processes. Dr. Bulathsinhala et al. (Bulathsinhala et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Sansone et al. (Sansone et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) emphasize the need for standardized outcome measures to monitor disease progression and assess treatment efficacy in RD populations (Bulathsinhala et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sansone et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Slayter et al. (Slayter et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied SMA in Canada using a similar Delphi methodology and found the need for a standardized toolkit to improve high-quality data monitoring and collection (Slayter et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our results corroborate these findings but also advance by identifying specific areas that require greater standardization.\u003c/p\u003e \u003cp\u003eThe standard data infrastructure must incorporate a strategy suited to aggregating and correlating health data in a findable, accessible, interoperable, and reusable manner. This approach enhances data quality, facilitates networked collaboration, and contributes to achieving the objectives outlined in national health policies. Data interoperability is a critical factor in meeting these goals, as the networked operation of health systems involves multiple nodes across different levels of complexity. These nodes must function in an integrated and adaptive manner to efficiently and effectively address the population's needs at all healthcare delivery (Tour\u0026eacute; et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInteroperability should not be limited to operational information system data internal to specific institutions. Still, it must also ensure the availability of these data for external government information systems with a strategic role. This aims to enhance decision-making and strengthen regional, national, and global actions. To achieve this, the ecosystem requires data that is accessible and comprehensible to both humans and credentialed machines (Tour\u0026eacute; et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Johansson et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, the principles of the Semantic Web emerge as fundamental pillars for defining structured and semantically annotated data. This approach enables not only the representation of data but also the precise contextualization of information, reducing ambiguities and preventing misinformation within the healthcare system (Tour\u0026eacute; et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe development of MDS must also consider the interoperability and integration of data with existing systems, such as electronic health records and digital health platforms. Standardized terminologies, compatible formats, and interoperable technologies are critical for achieving the MDS's full potential and preventing data fragmentation or redundancy (Choquet et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this way, data, information, and knowledge can be structured, distributed, and disseminated accurately, precisely, and effectively, contributing to advancing evidence-based medicine. Furthermore, this structuring enables the extrapolation of results into tangible benefits for the population, facilitating the development of public policies that effectively address social demands in the healthcare sector (Johansson et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of RD, European initiatives such as the European Joint Programme on Rare Diseases (EJP RD) highlight how integrating Common Data Elements with domain-specific elements can significantly improve research quality and healthcare. These initiatives underscore the importance of aligning international efforts to overcome technical and cultural barriers and emphasize the need for flexible systems that evolve to meet emerging research and clinical practice needs (Abaza et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImplementing the proposed MDS can significantly improve the coordination of care and monitoring of RD in Brazil. Integrating standardized data into the SUS can facilitate resource allocation and enhance healthcare quality. The literature suggests that standardizing MDS can lead to a better understanding of RD trajectories and treatment responses (Hodgkinson et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, the MDS's structured nature allows for future integration with genomic databases, supporting precision medicine efforts and enhancing epidemiological surveillance in RD (Chabanon et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBrazil's vast geographic and regional diversity also poses unique challenges, such as disparities in access to healthcare services and variability in data collection capacity. Implementing a robust MDS requires strategies for regional adaptation and continuous training of teams responsible for data entry and analysis (Panitz and Rodrigues \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Brazil, the Ministry of Health, in conjunction with the SUS, establishes the premises of the national health policy. These guidelines encompass tools and processes that support management, decision-making, monitoring health information systems and indicators, and evaluating healthcare services and measuring their impact. Additionally, this framework includes developing and structuring the regulatory network, which governs patient referral processes across different levels of care, from primary to highly complex services, ensuring compliance with the legal commitment to provide universal and comprehensive healthcare. To ensure the success of these procedures, MDSs are an informational device provided for in this national plan (BRASIL 2024).\u003c/p\u003e \u003cp\u003eAs established in Brazil's national health policy, MDSs constitute a strategy to reduce the fragmentation of healthcare systems by standardizing clinical-administrative data and optimizing the integrated operation of the SUS network. Adopting this strategy will be mandatory for all health systems in the country by 2027 to support management, planning, monitoring, and evaluation activities. Additionally, MDSs play a fundamental role in formulating, monitoring, and updating public policies, contributing to the development of national health statistics. In this way, they enable a more accurate characterization of the population's demographic and epidemiological profile, enhancing metrics for performance analysis and the efficient allocation of public resources (BRASIL 2024).\u003c/p\u003e \u003cp\u003eOne of our study's main strengths is the Delphi methodology, which allows for a structured and iterative approach to gathering and refining expert opinions. This methodology facilitates the identification of consensus among professionals from diverse backgrounds while accommodating differing perspectives. The iterative nature of the process ensures that all suggestions are thoroughly evaluated and disagreements are addressed systematically, leading to more robust and actionable outcomes (Diamond et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). By incorporating multiple rounds of feedback and analysis, the Delphi method enhances the reliability and validity of the final results, particularly in complex and multidisciplinary fields such as RD (Roni and Djajadikerta \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Panitz and Rodrigues \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the representativeness of the participants can be a limitation, as only a subset of RD experts in Brazil participated. Another limitation is the lack of longitudinal data to validate the proposed MDS, suggesting the need for future studies to confirm its effectiveness and applicability.\u003c/p\u003e \u003cp\u003eDuring MDS validation, challenges were observed, especially in the quality of life and comorbidities categories. These areas are inherently subjective and multidimensional, often leading to disagreements on priority variables. Addressing these challenges may involve complementary approaches, including the active participation of people living with RDs and their caregivers, whose perspectives can significantly enrich the development of more user-centered indicators (Revor\u0026ecirc;do et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFuture studies should focus on validating the proposed MDS in different regional and international contexts. Investigating the MDS's applicability in various health settings and populations can provide valuable insights to enhance data standardization further. Moreover, as suggested by Messina and Sframeli (Messina and Sframeli \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Sansone et al. (Sansone et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the inclusion of quality-of-life metrics should be explored more deeply to ensure a comprehensive and person-centered approach.\u003c/p\u003e \u003cp\u003eNotably, the consensus threshold has been achieved and exceeded in a field as heterogeneous as RD. The reproducibility and transparency of the Delphi method used also make it a valuable reference for other countries seeking to establish MDS for RD from early-stage initiatives. The diversity of these conditions, each with distinct clinical manifestations, diagnostic challenges (F\u0026eacute;lix et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and management needs (Alves et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), makes standardization particularly complex. Reaching an agreement despite this variability underscores the potential applicability of the findings in shaping more cohesive and practical approaches to RD research and care.\u003c/p\u003e \u003cp\u003eTo address potential biases inherent in the Delphi method, careful measures were implemented to ensure diversity and representativeness among the experts, neutrality in the design of questions, and fairness in the analysis of responses. These steps aimed to mitigate biases such as conformity, anchoring, dominance, and homogeneity. Anonymity in responses and blinded rounds were instrumental in minimizing interpersonal influences. Additionally, both qualitative and quantitative analyses were employed. Although statistical tools such as Kendall\u0026rsquo;s coefficient of concordance and intraclass correlation were tested, they showed low agreement levels and were not retained for final analysis. Instead, the CVI was adopted as the primary metric for evaluating expert consensus, based on the proportion of agreement among participants in each round (Niederberger and Spranger \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The transparency in documenting the entire process was pivotal for identifying potential limitations and enhancing reproducibility. Strategies such as consistency checks between rounds and blinded procedures further ensured the validity and reliability of the findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe standardization of an MDS represents a significant advance in managing information on RD in Brazil. The approach enabled defining essential variables and identifying gaps and challenges that can guide future improvements. Integrating this MDS into the public healthcare system will improve the quality of records, facilitate health monitoring, formulate data-driven policies, and develop research that expands knowledge and available solutions for RD. However, this initiative's success depends on careful implementation, with investment in infrastructure, professional training, and continuous improvement of data collection and analysis tools.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. The authors acknowledge using generative AI tools (e.g., ChatGPT) for language refinement. All content, ideas, and conclusions presented are solely those of the authors.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Brazilian National Council for Scientific and Technological Development (CNPq) supported this study (grant number 443030/2019-7) as part of the Brazilian Rare Diseases Network project. CNPq also supports TMF (grant number 306861/2019-4). The S\u0026atilde;o Paulo State Research Support Foundation (FAPESP) funded this study as part of the PPPP project (grant number 2023/10203-8), which DA coordinated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all collaborators of the RARAS network for their invaluable contributions to this study. Their dedication and expertise in data collection, analysis, and governance were instrumental in advancing this research. We also acknowledge the support the participating institutions and professionals provided during the development and implementation of the RARAS initiative. A complete list of consortium members who contributed to this study is provided in Supplementary Material 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset associated with this study has been deposited in publicly accessible repositories to ensure transparency and reproducibility. The primary data is available in the Lattes Data repository under the following DOI: doi:10.57810/lattesdata/XEL53O. Additionally, a copy of the dataset has been made available on Figshare for broader accessibility: 10.6084/m9.figshare.26324938. These repositories provide persistent identifiers and support open data-sharing standards, ensuring the dataset can be accessed, referenced, and reused in future research. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis and tools are publicly available on GitHub: https://github.com/RARAS-BR. This study\u0026apos;s metadata and associated standards are registered and accessible via the FAIRsharing record (https://fairsharing.org/10.25504/FAIRsharing.d7b6c8) and the DMP at the ARGOS record, which has the DOI 10.5281/zenodo.13643431 and is available at https://argos.openaire.eu/explore-plans/overview/public/8d4a6851-f96e-4a29-ad79-37e8bf30d17a). \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbaza H, Kadioglu D, Martin S, et al. 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From the search for diagnosis to treatment uncertainties: challenges of care for rare genetic diseases in Brazil. \u003cem\u003eCien Saude Colet\u003c/em\u003e. 2019;24:3637. https://doi.org/10.1590/1413-812320182410.01612019\u003c/li\u003e\n \u003cli\u003eJohansson LF, Laurie S, Spalding D, et al. An interconnected data infrastructure to support large-scale rare disease research. \u003cem\u003eGigascience\u003c/em\u003e. 2024;13. https://doi.org/10.1093/gigascience/giae058\u003c/li\u003e\n \u003cli\u003eMehl G, Tun\u0026ccedil;alp \u0026Ouml;, Ratanaprayul N, et al. WHO SMART guidelines: optimising country-level use of guideline recommendations in the digital age. \u003cem\u003eLancet Digit Health\u003c/em\u003e. 2021;3:e213\u0026ndash;6. https://doi.org/10.1016/s2589-7500(21)00038-8\u003c/li\u003e\n \u003cli\u003eMessina S, Sframeli M. New treatments in spinal muscular atrophy: positive results and new challenges. \u003cem\u003eJ Clin Med\u003c/em\u003e. 2020;9:1\u0026ndash;16. https://doi.org/10.3390/jcm9072222\u003c/li\u003e\n \u003cli\u003eMontserrat Moliner A, Waligora J. The European Union policy in the field of rare diseases. \u003cem\u003eAdv Exp Med Biol\u003c/em\u003e. 2017;1031:561\u0026ndash;87. https://doi.org/10.1007/978-3-319-67144-4_30\u003c/li\u003e\n \u003cli\u003eNiederberger M, Spranger J. Delphi Technique in Health Sciences: A Map. \u003cem\u003eFront Public Health\u003c/em\u003e. 2020;8:561103. https://doi.org/10.3389/fpubh.2020.00457\u003c/li\u003e\n \u003cli\u003eOkoli C, Pawlowski SD. The Delphi method as a research tool: an example, design considerations and applications. \u003cem\u003eInformation \u0026amp; Management\u003c/em\u003e. 2004;42:15\u0026ndash;29. https://doi.org/10.1016/j.im.2003.11.002\u003c/li\u003e\n \u003cli\u003ePanitz LM, Rodrigues W. BRAZILIAN MINIMUM BASIC DATA SET (CMD): Fundamentals, development and implementation. \u003cem\u003ePerspectivas em Ci\u0026ecirc;ncia da Informa\u0026ccedil;\u0026atilde;o\u003c/em\u003e. 2024;29:e-46451. https://doi.org/10.1590/1981-5344/46451\u003c/li\u003e\n \u003cli\u003eRevor\u0026ecirc;do LDS, Maia RS, Torres GDV, Chaves Maia EM. O Uso Da T\u0026eacute;cnica Delphi Em Sa\u0026uacute;de: Uma Revis\u0026atilde;o Integrativa De Estudos Brasileiros. \u003cem\u003eRevista Arquivos de Ci\u0026ecirc;ncias da Sa\u0026uacute;de\u003c/em\u003e. 2015;22:16. https://doi.org/10.17696/2318-3691.22.2.2015.136\u003c/li\u003e\n \u003cli\u003eRoni SM, Djajadikerta HG. \u003cem\u003eData Analysis with SPSS for Survey-based Research\u003c/em\u003e. 1st ed. Springer Singapore; 2021.\u003c/li\u003e\n \u003cli\u003eSansone VA, Walter MC, Attarian S, et al. 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Available from: https://www.who.int/standards/classifications/frequently-asked-questions/rare-diseases. Accessed 11 Jul 2023.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Rare Diseases](https://link.springer.com/journal/44162)","snPcode":"44162","submissionUrl":"https://submission.nature.com/new-submission/44162/3","title":"Journal of Rare Diseases","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6701865/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6701865/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Rare diseases (RD) are characterized by low prevalence, affecting fewer than 1 in 2,000 individuals, yet impacting millions globally. In Brazil, RD management is challenged by geographic disparities and unequal access to care. To address these gaps, the Brazilian Rare Diseases Network (RARAS) developed a Minimum Data Set (MDS) to standardize RD documentation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo validate the proposed MDS through a Delphi consensus process with clinical experts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study used the Delphi method to validate the MDS, involving clinical \u0026nbsp;experts. A structured questionnaire with 55 items across nine data categories \u0026nbsp;was distributed via REDCap. Experts evaluated the relevance of each variable \u0026nbsp;and reached a consensus, which was defined as ≥80% agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFifty-two professionals were invited; 16 responded in round one, and 11 in round two. Suggestions for additions and removals were reviewed after each round. Seven data categories achieved over 81.8% agreement, reaching consensus levels between 90.9% and 100%. The final MDS includes care indicators and facilitates consistent documentation and care coordination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study demonstrates the value of standardized data collection in strengthening RD surveillance, clinical workflows, and research.\u003c/p\u003e","manuscriptTitle":"Standardizing Rare Disease Data in Brazil: A Delphi-Based Approach for the First National Registry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-29 06:15:59","doi":"10.21203/rs.3.rs-6701865/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-09T08:19:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-07T19:23:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T15:53:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238923740240440501194093790341331127510","date":"2025-05-28T22:31:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113479374066149748690016880866818457475","date":"2025-05-27T14:15:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189258565252858617104750448519354512543","date":"2025-05-27T11:40:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-27T09:26:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-21T13:33:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-21T13:27:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Rare Diseases","date":"2025-05-19T20:24:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Rare Diseases](https://link.springer.com/journal/44162)","snPcode":"44162","submissionUrl":"https://submission.nature.com/new-submission/44162/3","title":"Journal of Rare Diseases","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6071c763-9117-4d65-b07d-2979c5130bfa","owner":[],"postedDate":"May 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T19:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-29 06:15:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6701865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6701865","identity":"rs-6701865","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}