Rebecca Leary

The authors would like to make the following correction to the published paper

To meet the growing need for strong clinical evidence on a global scale, both publicand private sectors have invested to standardise health data elements and achievegreater connectivity and interoperability of health data systems. This is enabled byFAIRification (making Findable, Accessible, Interoperable, and Reusable) of clinicaltrial data increasingly recognised as a crucial step in enhancing the value and utility ofclinical data across the research community. This is even more relevant in thepaediatric and rare disease field where data remains highly fragmented in terms ofdata collection practices, ontologies, and clinical reporting standards, and often lockedin silos with diverse formats and standards.

Background: Pediatric clinical research, especially in rare diseases, faces persistent challenges including the identification and recruitment of eligible patients, assessing protocol feasibility, and ensuring efficient trial execution. These issues are compounded by small, age-stratified populations and fragmented clinical data. Real-world data (RWD), especially when drawn from electronic health records (EHRs), present an opportunity to support innovative trial designs, such as real-world comparator arms and postmarketing surveillance. However, realizing this potential depends on the routine availability of structured, reusable clinical data. Objective: This proof-of-concept study aimed to assess the availability and structure of routine clinical data in European pediatric hospitals, focusing on data elements relevant for use in comparator arms and postmarketing surveillance studies. The study focused on 2 disease areas—neurofibromatosis (NF) and atopic dermatitis (AD)—as examples of rare and common conditions in children, respectively. Methods: An inventory of 113 high-value clinical data items was developed based on expert analysis of clinical protocols for NF, AD, and safety studies. These items were included in a structured web-based survey disseminated through the connect4children (c4c) National Hub network, reaching sites across. Europe. Respondents were asked to indicate how each data item is collected and stored: in structured/coded EHR fields, as free text, in external systems, or on paper. Results: Survey responses from 24 hospitals across 11 European countries revealed considerable variability in how data are captured and stored. While many general clinical and drug safety data elements—such as demographics, vital signs, and medication use—were often collected in structured formats, disease-specific and contextual variables were frequently captured as free text or not documented in a standardized way. For example, structured data capture was more prevalent for basic demographic and safety-related variables, whereas only a minority of sites recorded key disease-specific clinical details in a structured form. Lifestyle and family history data were among the least consistently documented. These gaps in structured data entry reduce the immediate reusability of EHR data for secondary research purposes. Conclusions: This study highlights gaps in the structured documentation of pediatric clinical data across European sites. While the routine collection of many variables is promising, the lack of structured and coded formats poses a barrier to reusing these data for observational studies or comparator arms. As a first step toward the broader integration of RWD into pediatric research, this study demonstrates the feasibility of assessing EHR data availability and sets the stage for future scaling across more diseases and sites.

BACKGROUND Pediatric clinical research, especially in rare diseases, faces persistent challenges including the identification and recruitment of eligible patients, assessing protocol feasibility, and ensuring efficient trial execution. These issues are compounded by small, age-stratified populations and fragmented clinical data. Real-world data (RWD), especially when drawn from electronic health records (EHRs), present an opportunity to support innovative trial designs, such as real-world comparator arms and postmarketing surveillance. However, realizing this potential depends on the routine availability of structured, reusable clinical data. OBJECTIVE This proof-of-concept study aimed to assess the availability and structure of routine clinical data in European pediatric hospitals, focusing on data elements relevant for use in comparator arms and postmarketing surveillance studies. The study focused on 2 disease areas—neurofibromatosis (NF) and atopic dermatitis (AD)—as examples of rare and common conditions in children, respectively. METHODS An inventory of 113 high-value clinical data items was developed based on expert analysis of clinical protocols for NF, AD, and safety studies. These items were included in a structured web-based survey disseminated through the connect4children (c4c) National Hub network, reaching sites across. Europe. Respondents were asked to indicate how each data item is collected and stored: in structured/coded EHR fields, as free text, in external systems, or on paper. RESULTS Survey responses from 24 hospitals across 11 European countries revealed considerable variability in how data are captured and stored. While many general clinical and drug safety data elements—such as demographics, vital signs, and medication use—were often collected in structured formats, disease-specific and contextual variables were frequently captured as free text or not documented in a standardized way. For example, structured data capture was more prevalent for basic demographic and safety-related variables, whereas only a minority of sites recorded key disease-specific clinical details in a structured form. Lifestyle and family history data were among the least consistently documented. These gaps in structured data entry reduce the immediate reusability of EHR data for secondary research purposes. CONCLUSIONS This study highlights gaps in the structured documentation of pediatric clinical data across European sites. While the routine collection of many variables is promising, the lack of structured and coded formats poses a barrier to reusing these data for observational studies or comparator arms. As a first step toward the broader integration of RWD into pediatric research, this study demonstrates the feasibility of assessing EHR data availability and sets the stage for future scaling across more diseases and sites.

The reuse of paediatric individual patient data (IPD) from clinical trials (CTs) is essential to overcome specific ethical, regulatory, methodological, and economic issues that hinder the progress of paediatric research. Sharing data through repositories enables the aggregation and dissemination of clinical information, fosters collaboration between researchers, and promotes transparency. This work aims to identify and describe existing data-sharing repositories (DSRs) developed to store, share, and reuse paediatric IPD from CTs. A rapid review of platforms providing access to electronic DSRs was conducted. A two-stage process was used to characterize DSRs: a first step of identification, followed by a second step of analysis using a set of eight purpose-built indicators. From an initial set of forty-five publicly available DSRs, twenty-one DSRs were identified as meeting the eligibility criteria. Only two DSRs were found to be totally focused on the paediatric population. Despite an increased awareness of the importance of data sharing, the results of this study show that paediatrics remains an area in which targeted efforts are still needed. Promoting initiatives to raise awareness of these DSRs and creating ad hoc measures and common standards for the sharing of paediatric CT data could help to bridge this gap in paediatric research.

The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognised that there are not enough medicines tested for all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop cross-cutting data resources that build on existing CDISC standards in an effort to standardise paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and analysed their use for standardising disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease-specific data standardisation. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardising disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions—FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organisation of further workshops and educational materials.

Introduction: Standardization of data items collected in paediatric clinical trials is an important but challenging issue. The Clinical Data Interchange Standards Consortium (CDISC) data standards are well understood by the pharmaceutical industry but lack the implementation of some paediatric specific concepts. When a paediatric concept is absent within CDISC standards, companies and research institutions take multiple approaches in the collection of paediatric data, leading to different implementations of standards and potentially limited utility for reuse. Objective: To overcome these challenges, the conect4children consortium has developed a cross-cutting paediatric data dictionary (CCPDD). Methods: The dictionary was built over three phases – scoping (including a survey sent out to ten industrial and 34 academic partners to gauge interest), creation of a longlist and consensus building for the final set of terms. The dictionary was finalized during a workshop with attendees from academia, hospitals, industry and CDISC. The attendees held detailed discussions on each data item and participated in the final vote on the inclusion of the item in the CCPDD.Results: Nine industrial and 34 academic partners responded to the survey, which showed overall interest in the development of the CCPDD. Following the final vote on 27 data items, three were rejected, six were deferred to the next version and a final opinion was sought from CDISC. The first version of the CCPDD with 25 data items was released in August 2019.Discussion and Conclusion: The continued use of the dictionary has the potential to ensure the collection of standardized data that is interoperable and can later be pooled and reused for other applications. The dictionary is already being used for case report form creation in three clinical trials. The CCPDD will also serve as one of the inputs to the Paediatric User Guide, which is being developed by CDISC.

Introduction: The conect4children (c4c) consortium was setup to facilitate the development of new drugs and therapies for paediatric populations and address key challenges associated with paediatric clinical trials. Two of the major adopting principles for c4c were academia‐industry partnership and data harmonisation and interoperability through common eCRF definitions. To understand the challenges arising out of these principles, the c4c team at Newcastle University conducted semi‐structured interviews with four c4c industry partners.Methods: Each partner was asked 10 questions about the data standards used in their company, management and maintenance of data dictionaries, how they dealt with paediatric‐specific issues, major knowledge gaps and how academia could aid in bridging these gaps. Thematic analysis was performed to identify patterns in their answers.Results: All companies use the Clinical Data Interchange Standards Consortium (CDISC) standards but face problems when certain terminology is not included in CDISC (e.g., paediatric‐specific terminologies). All companies were committed to interoperability and had strict policies about how additional terminology could be added to their dictionaries. Three of the four companies maintained a single dictionary but also had lighter versions for specific usage. The two major knowledge gaps identified from the interviews were handling of non‐CDISC terminology and maintenance of normal lab ranges in dictionaries.Discussion: To address these gaps, c4c has been working on a four‐point plan including the development of a cross‐cutting paediatric dictionary and a paediatric user guide in collaboration with CDISC.

Data dictionaries for clinical trials are often created manually, with data structures and controlled vocabularies specific for a trial or family of trials within a sponsor’s portfolio. Microsoft Excel is commonly used to capture the representation of data dictionary items but has limited functionality for this purpose. The conect4children (c4c) network is piloting the Direcht clinical data modelling tool to model their Cross Cutting Paediatric Data Dictionary (CCPDD) in a more formalised way. The first pilot had the key objective of testing whether a clinical data modelling tool could be used to represent data items from the CCPDD. The key objective of the second pilot is to establish whether a small team with little or no experience of clinical data modelling can use Direcht to expand the CCPDD. Clinical modelling is the process of structuring clinical data so it can be understood by computer systems and humans. The model contains all of the elements that are needed to define the data item. Results from the pilots show that Direcht creates a structured environment to build data items into models that fit into the larger CCPDD. Models can be represented as an HTML document, mind map, or exported in various formats for import into a computer system. Challenges identified over the course of both pilots are being addressed with c4c partners and external stakeholders.