Trusted Data Spaces as a Viable and Sustainable Solution for Networks of Population-Based Patient Registries

Harmonization and integration of health data remain as the focus of many ongoing efforts toward the goal of optimizing health and health care policies. Population-based patient registries constitute a critical element of these endeavors. Although their main function is monitoring and surveillance of a particular disease within a given population, they are also an important data source for epidemiology. Comparing indicators across national boundaries brings an extra dimension to the use of registry data, especially in regions where supranational initiatives are or could be coordinated to leverage good practices; this is particularly relevant for the European Union. However, strict data protection laws can unintentionally hamper the efforts of data harmonization to ensure the removal of statistical bias in the individual data sets, thereby compromising the integrated value of registries' data. Consequently, there is the motivation for creating a new paradigm to ensure that registries can operate in an environment that is not unnecessarily restrictive and to allow accurate comparison of data to better ascertain the measures and practices that are most conducive to the public health of societies. The pan-European organizational model of cancer registries, owing to its long and successful establishment, was considered as a sound basis from which to proceed toward such a paradigm. However, it has certain drawbacks, particularly regarding governance, scalability, and resourcing, which are essential elements to consider for a generic patient registry model. These issues are addressed in a proposal of an adapted model that promises a valuable pan-European data resource for epidemiological research, while providing a closely regulated environment for the processing of pseudonymized patient summary data on a broader scale than has hitherto been possible.

Rare lysosomal disease registries: lessons learned over three decades of real-world evidence

Lysosomal storage disorders (LSD) are rare diseases, caused by inherited deficiencies of lysosomal enzymes/transporters, that affect 1 in 7000 to 1 in 8000 newborns. Individuals with LSDs face long diagnostic journeys during which debilitating and life-threatening events can occur. Clinical trials and classical descriptions of LSDs typically focus on common manifestations, which are not representative of the vast phenotypic heterogeneity encountered in real-world experience. Additionally, recognizing that there was a limited understanding of the natural history, disease progression, and real-world clinical outcomes of rare LSDs, a collaborative partnership was pioneered 30 years ago to address these gaps. The Rare Disease Registries (RDR) (for Gaucher, Fabry, Mucopolysaccharidosis type I, and Pompe), represent the largest observational database for these LSDs. Over the past thirty years, data from the RDRs have helped to inform scientific understanding and the development of comprehensive monitoring and treatment guidelines by creating a framework for data collection and establishing a standard of care, with an overarching goal to improve the quality of life of affected patients. Here, we highlight the history, process, and impact of the RDRs, and discuss the lessons learned and future directions.

Remodeling an existing rare disease registry to be used in regulatory context: Lessons learned and recommendations

Disease registries have been used as an interesting source of real-world data for supporting regulatory decision-making. In fact, drug studies based on registries cover pre-approval investigation, registry randomized clinical trials, and post-authorization studies. This opportunity has been investigated particularly for rare diseases—conditions affecting a small number of individuals worldwide—that represent a peculiar scenario. Several guidelines, concepts, suggestions, and laws are already available to support the design or improvement of a rare disease registry, opening the way for implementation of a registry capable of managing regulatory purposes. The present study aims to highlight the key stages performed for remodeling the existing Registry of Multiple Osteochondromas—REM into a tool consistent with EMA observations and recommendations, as well as to lead the readers through the entire adapting, remodeling, and optimizing process. The process included a variety of procedures that can be summarized into three closely related categories: semantic interoperability, data quality, and governance. At first, we strengthened interoperability within the REM registry by integrating ontologies and standards for proper data collection, in accordance with FAIR principles. Second, to increase data quality, we added additional parameters and domains and double-checked to limit human error to a bare minimum. Finally, we established two-level governance that has increased the visibility for the scientific community and for patients and carers. In conclusion, our remodeled REM registry fits with most of the scientific community’s needs and indications, as well as the best techniques for providing real-world evidence for regulatory aspects.

Opportunities and Challenges for Machine Learning in Rare Diseases

Rare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. This situation calls for innovative solutions to support the decision process via quantitative and automated tools. Machine learning brings to the stage a wealth of powerful inference methods; however, matching the health conditions with advanced statistical techniques raises methodological, technological, and even ethical issues. In this contribution, we critically point to the specificities of the dialog of rare diseases with machine learning techniques concentrating on the key steps and challenges that may hamper or create actionable knowledge and value for the patient together with some on-field methodological suggestions and considerations.

Are clinicians using routinely collected data to drive practice improvement? A cross-sectional survey

BACKGROUND

Clinical registry participation is a measure of healthcare quality. Limited knowledge exists on Australian hospitals' participation in clinical registries and whether this registry data informs quality improvement initiatives.

OBJECTIVE

To identify participation in clinical registries, determine if registry data inform quality improvement initiatives, and identify registry participation enablers and clinicians' educational needs to improve use of registry data to drive practice change.

METHODS

A self-administered survey was distributed to staff coordinating registries in seven hospitals in New South Wales, Australia. Eligible registries were international-, national- and state-based clinical, condition-/disease-specific and device/product registries.

RESULTS

Response rate was 70% (97/139). Sixty-two (64%) respondents contributed data to 46 eligible registries. Registry reports were most often received by nurses (61%) and infrequently by hospital executives (8.4%). Less than half used registry data 'always' or 'often' to influence practice improvement (48%) and care pathways (49%). Protected time for data collection (87%) and benchmarking (79%) were 'very likely' or 'likely' to promote continued participation. Over half 'strongly agreed' or 'agreed' that clinical practice improvement training (79%) and evidence-practice gap identification (77%) would optimize use of registry data.

CONCLUSIONS

Registry data are generally only visible to local speciality units and not routinely used to inform quality improvement. Centralized on-going registry funding, accessible and transparent integrated information systems combined with data informed improvement science education could be first steps to promote quality data-driven clinical improvement initiatives.

Lessons learned from hemolytic uremic syndrome registries: recommendations for implementation

BACKGROUND

Hemolytic uremic syndrome (HUS) is a rare condition which diagnosed with the triad of thrombocytopenia, microangiopathic hemolytic anemia, and acute renal injury. There is a high requirement for research to discover treatments. HUS registries can be used as an important information infrastructure. In this study, we identified and compared the different features of HUS registries to present a guide for the development and implementation of HUS registries.

RESULTS

The purposes of registries were classified as clinical (9 registries), research (7 registries), and epidemiological (5 registries), and only 3 registries pursued all three types of purposes. The data set included demographic data, medical and family history, para-clinical and diagnostic measures, treatment and pharmacological data, complications, and outcomes. The assessment strategies of data quality included monthly evaluation and data audit, the participation of physicians to collect data, editing and correcting data errors, increasing the rate of data completion, following guidelines and data quality training, using specific data quality indicators, and real-time evaluation of data at the time of data entry. 8 registries include atypical HUS patients, and 7 registries include all patients regardless of age. Only two registries focused on children. 4 registries apply prospective and 4 applied both prospective, and retrospective data collection. Finally, specialized hospitals were the main data source for these registries.

CONCLUSION

Based on the findings, we suggested a learning framework for developing and implementing an HUS registry. This framework includes lessons learned and suggestions for HUS registry purposes, minimum data set, data quality assurance, data collection methods, inclusion and exclusion criteria as well as data sources. This framework can help researchers develop HUS registries.

Surveys and Registries on Catheter Ablation of Atrial Fibrillation: Fifteen Years of History

Surveys and registries are widely used in medicine as valuable tools to integrate the information from randomized and observational studies. Early after its introduction in daily practice and parallel to its escalating popularity, catheter ablation of atrial fibrillation has been the subject of several surveys and registries. Over the years, relevant aspects associated with atrial fibrillation ablation have been investigated using these tools, including procedural safety and efficacy, discontinuation of anticoagulation therapy and risk of stroke postablation, and outcomes in special populations. The aim of this article is to provide a comprehensive review of the contributions offered by surveys and registries in catheter ablation of atrial fibrillation over the past 15 years.

Interoperability of population-based patient registries

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Inter-linkage of patient-registry data provides a rich source of secondary data usage.

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Mapping of heterogeneous metadata systems can be accomplished via semantic links.

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Semantic linkage between patient registries provides the basis for interoperability.

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Semantic metadata registry frameworks can facilitate access back to primary data sources.

Enabling full interoperability within and between population-based patient-registry domains would open up access to a rich and unique source of health data for secondary data usage. Previous attempts to tackle patient-registry interoperability have met with varying degrees of success, but a unifying solution remains elusive. The purpose of this paper is to show by practical example how a solution is attainable via the implementation of an existing framework based of the concept of federated, semantic metadata registries. One important feature motivating the use of this framework is that it can be implemented gradually and independently within each patient-registry domain. By employing linked open data principles, the framework extends the ISO/IEC 11179 standard to provide both syntactic and semantic interoperability of data elements with the means of specifying automated extraction scripts for retrieval of data from different registry content models. The examples provided address the domain of European population-based cancer registries to demonstrate the feasibility of the approach. One of the examples shows how quick gains are derivable by allowing retrieval of aggregated core data sets. The other examples show how aggregated full sets of data and record-level data might also be retrieved from each local registry. An infrastructure of patient-registry domains adhering to the principles of the framework would provide the semantic contexts and inter-linkage of data necessary for automated search and retrieval of registry data. It would thereby also lay the foundation for making registry data serviceable to artificial intelligence (AI) applications.

Patient Registries: An Underused Resource for Medicines Evaluation : Operational proposals for increasing the use of patient registries in regulatory assessments

INTRODUCTION

Patient registries, 'organised systems that use observational methods to collect uniform data on a population defined by a particular disease, condition, or exposure, and that is followed over time', are potentially valuable sources of data for supporting regulatory decision-making, especially for products to treat rare diseases. Nevertheless, patient registries are greatly underused in regulatory assessments. Reasons include heterogeneity in registry design and in the data collected, even across registries for the same disease, as well as unreliable data quality and data sharing impediments. The Patient Registries Initiative was established by the European Medicines Agency in 2015 to support registries in collecting data suitable to contribute to regulatory assessments, especially post-authorisation safety and effectiveness studies.

METHODS

We conducted a qualitative synthesis of the published observations and recommendations from an initiative-led multi-stakeholder consultation and four disease-specific patient registry workshops. We identified the primary factors facilitating the use of registry data in regulatory assessments. We generated proposals on operational measures needed from stakeholders including registry holders, patients, healthcare professionals, regulators, marketing authorisation applicants and holders, and health technology assessment bodies for implementing these.

RESULTS

Ten factors were identified as facilitating registry use for supporting regulatory assessments of medicinal products. Proposals on operational measures needed for implementation were categorised according to three themes: (1) nature of the data collected and registry quality assurance processes; (2) registry governance, informed consent, data protection and sharing; and (3) stakeholder communication and planning of benefit-risk assessments.

CONCLUSIONS

These are the first explicit proposals, from a regulatory perspective, on operational methods for increasing the use of patient registries in medicines regulation. They apply to registry holders, patients, regulators, marketing authorisation holders/applicants and healthcare stakeholders broadly, and their implementation would greatly facilitate the use of these valuable data sources in regulatory decision-making.

Evaluation of the quality of clinical data collection for a pan-Canadian cohort of children affected by inherited metabolic diseases: lessons learned from the Canadian Inherited Metabolic Diseases Research Network

BACKGROUND

The Canadian Inherited Metabolic Diseases Research Network (CIMDRN) is a pan-Canadian practice-based research network of 14 Hereditary Metabolic Disease Treatment Centres and over 50 investigators. CIMDRN aims to develop evidence to improve health outcomes for children with inherited metabolic diseases (IMD). We describe the development of our clinical data collection platform, discuss our data quality management plan, and present the findings to date from our data quality assessment, highlighting key lessons that can serve as a resource for future clinical research initiatives relating to rare diseases.

METHODS

At participating centres, children born from 2006 to 2015 who were diagnosed with one of 31 targeted IMD were eligible to participate in CIMDRN's clinical research stream. For all participants, we collected a minimum data set that includes information about demographics and diagnosis. For children with five prioritized IMD, we collected longitudinal data including interventions, clinical outcomes, and indicators of disease management. The data quality management plan included: design of user-friendly and intuitive clinical data collection forms; validation measures at point of data entry, designed to minimize data entry errors; regular communications with each CIMDRN site; and routine review of aggregate data.

RESULTS

As of June 2019, CIMDRN has enrolled 798 participants of whom 764 (96%) have complete minimum data set information. Results from our data quality assessment revealed that potential data quality issues were related to interpretation of definitions of some variables, participants who transferred care across institutions, and the organization of information within the patient charts (e.g., neuropsychological test results). Little information was missing regarding disease ascertainment and diagnosis (e.g., ascertainment method - 0% missing).

DISCUSSION

Using several data quality management strategies, we have established a comprehensive clinical database that provides information about care and outcomes for Canadian children affected by IMD. We describe quality issues and lessons for consideration in future clinical research initiatives for rare diseases, including accurately accommodating different clinic workflows and balancing comprehensiveness of data collection with available resources. Integrating data collection within clinical care, leveraging electronic medical records, and implementing core outcome sets will be essential for achieving sustainability.

Engineering Requirements of a Herpes Simplex Virus Patient Registry: Discovery Phase of a Real-World Evidence Platform to Advance Pharmacogenomics and Personalized Medicine

Comprehensive pharmacogenomic understanding requires both robust genomic and demographic data. Patient registries present an opportunity to collect large amounts of robust, patient-level data. Pharmacogenomic advancement in the treatment of infectious diseases is yet to be fully realised. Herpes simplex virus (HSV) is one disease for which pharmacogenomic understanding is wanting. This paper aims to understand the key factors that impact data collection quality for medical registries and suggest potential design features of an HSV medical registry to overcome current constraints and allow for this data to be used as a complement to genomic and clinical data to further the treatment of HSV. This paper outlines the discovery phase for the development of an HSV registry with the aim of learning about the users and their contexts, the technological constraints and the potential improvements that can be made. The design requirements and user stories for the HSV registry have been identified for further alpha phase development. The current landscape of HSV research and patient registry development were discussed. Through the analysis of the current state of the art and thematic user analysis, potential design features were elucidated to facilitate the collection of high-quality, robust patient-level data which could contribute to advances in pharmacogenomic understanding and personalised medicine in HSV. The user requirements specification for the development of an HSV registry has been summarised and implementation strategies for the alpha phase discussed.

Research methodology and practical issues relating to the conduct of a medical device registry

BACKGROUND

The postmarket research goal is to assess "generalizability" or "external validity" to see if the early results of clinical trials with investigational devices are reproducible in everyday practice in the real world and the longer term. Registries have an important but ambivalent role in achieving this goal.

METHODS

Although registries are common, in practice they follow the regulatory processes that appear designed primarily for pharmaceutical clinical trials and confirmatory studies. We review the literature to assess different definitions and the role of registries in the hierarchy of scientific evidence. We analyze common characteristics affecting registry design, implementation, and governance as well as safety reporting and off-label use while describing the experience of setting up an international, prospective registry for an endovascular device used to treat abdominal aortic aneurysms.

RESULTS

Key areas in which to distinguish registries from trials are as follows: eligibility, setting (patients and institutions), device configurations and iterations, the use of design and quality "spaces," a focus on systematic quality checks (rather than source data monitoring), open-ended follow-up, flexibility in the definition of end points and sample sizes, data sharing, and publishing commitments.

CONCLUSION

Both clinical trials and registries are essential and complementary research methods and the strengths and weaknesses of each need to be recognized. The specific characteristics of registry research deserve to be acknowledged and safeguarded in the regulations governing clinical investigations with medical devices.

Addressing the Data Linking Challenges

BACKGROUND

PARENT JA (cross-border Patient Registries iNiTiative Joint Action), a joint EU and Member States project, has conducted a research among EU patient registries aimed at gathering information on the registries' interoperability readiness. Leaning on the information and data collected through the previous PARENT JA research, this study aims to provide more detailed view into the registry holders' practical challenges with data linking. Since the studies which dealt with patient data exchange have often neglected the registry holders' performance of data exchange, we wanted to put a spotlight on various EU registry holders' practices and operations, aiming to detect their needs and concerns in the process of running an interoperable registry. The focus of this study was identifying the main practices and challenges in patient registries interoperability improvement.

METHODS

The basis for this analysis were the data collected in the series of structured interviews. The size of the interview sample was 13 patient registries, each from a different EU country. The structured interview consisted of nine questions and was conducted in two parts: oral and written. The answers were analysed using open coding.

RESULTS

Results are interpreted in the context of the six main themes that emerged through a comprehensive analysis. (1) Examples of data exchange: The most common reported data exchange practices were seen only as a way to achieve the most immediate needs and interests of the individual registries. (2) Awareness and use of international standards: International data and clinical standards were not widely used by the interviewed registries. (3) Use of data models and formats: In the area of data models and formats there is no universally used practice. (4) Data request protocols and procedures: Procedures and protocols varied, mostly depending on the national legal systems in which the patient registries operated. (5) Data security and integrity: Security of personal data was a universal concern for all registry holders that were interviewed; identifiable individual data was shared only in one case. (6) Opportunities and challenges of registry interoperability: most registry holders responded that their registries were well prepared for interoperability practices and that data exchange has never been their primary operative concern.

CONCLUSIONS

Most of the difficulties regarding data linking were not necessarily associated with technical issues, which registry holders listed outright. Our analysis showed that the lack of interoperability came as a result of organizational or legal constraints that made the registries unable to process and conduct data linking quickly and effectively with other sources.

mosaicQA - A General Approach to Facilitate Basic Data Quality Assurance for Epidemiological Research

BACKGROUND

Epidemiological studies are based on a considerable amount of personal, medical and socio-economic data. To answer research questions with reliable results, epidemiological research projects face the challenge of providing high quality data. Consequently, gathered data has to be reviewed continuously during the data collection period.

OBJECTIVES

This article describes the development of the mosaicQA-library for non-statistical experts consisting of a set of reusable R functions to provide support for a basic data quality assurance for a wide range of application scenarios in epidemiological research.

METHODS

To generate valid quality reports for various scenarios and data sets, a general and flexible development approach was needed. As a first step, a set of quality-related questions, targeting quality aspects on a more general level, was identified. The next step included the design of specific R-scripts to produce proper reports for metric and categorical data. For more flexibility, the third development step focussed on the generalization of the developed R-scripts, e.g. extracting characteristics and parameters. As a last step the generic characteristics of the developed R functionalities and generated reports have been evaluated using different metric and categorical datasets.

RESULTS

The developed mosaicQA-library generates basic data quality reports for multivariate input data. If needed, more detailed results for single-variable data, including definition of units, variables, descriptions, code lists and categories of qualified missings, can easily be produced.

CONCLUSIONS

The mosaicQA-library enables researchers to generate reports for various kinds of metric and categorical data without the need for computational or scripting knowledge. At the moment, the library focusses on the data structure quality and supports the assessment of several quality indicators, including frequency, distribution and plausibility of research variables as well as the occurrence of missing and extreme values. To simplify the installation process, mosaicQA has been released as an official R-package.