How to design a registry for undiagnosed patients in the framework of rare disease diagnosis: suggestions on software, data set and coding system

Computer-assisted patient identification tool in inborn errors of metabolism - potential for rare disease patient registry and big data analysis

BACKGROUND

Patient registries are crucial for rare disease management. However, manual registry construction is labor-intensive and often not user-friendly. Our goal is to establish Hong Kong's first computer-assisted patient identification tool for rare diseases, starting with inborn errors of metabolism (IEM).

METHODS

Patient data from 2010 to 2019 was retrieved from electronic databases. Through big data analytics, patient data were filtered based on specific IEM-related biochemical and genetic tests. Clinical notes were analyzed using a rule-based natural language processing technique called regular expression. The algorithm classified each extracted paragraph as "IEM-related" or "not IEM-related." Pathologists reviewed the paragraphs for curation, and the algorithm's performance was evaluated.

RESULTS

Out of 46,419 patients with IEM-related tests, the algorithm identified 100 as "IEM-related." After pathologists' validation, 96 cases were confirmed as true IEM, with 1 uncertain case and 3 false positives. A secondary ascertainment yielded a sensitivity of 92.3% compared to our previously published IEM cohort.

CONCLUSIONS

Our artificial intelligence approach provides a novel method to identify IEM patients, facilitating the creation of a centralized, computer-assisted rare disease patient registry at the local and national levels. This data can potentially be accessed by multiple stakeholders for collaborative research and to enhance healthcare management for rare diseases.

Transition database for rare diseases and its use for clinical documentation

Patients with rare diseases commonly suffer from severe symptoms as well as chronic and sometimes life-threatening effects. Not only the rarity of the diseases but also the poor documentation of rare diseases often leads to an immense delay in diagnosis. One of the main problems here is the inadequate coding with common classifications such as the International Statistical Classification of Diseases and Related Health Problems. Instead, the ORPHAcode enables precise naming of the diseases. So far, just few approaches report in detail how the technical implementation of the ORPHAcode is done in clinical practice and for research. We present a concept and implementation of storing and mapping of ORPHAcodes. The Transition Database for Rare Diseases contains all the information of the Orphanet catalog and serves as the basis for documentation in the clinical information system as well as for monitoring Key Performance Indicators for rare diseases at the hospital. The five-step process (especially using open source tools and the DataVault 2.0 logic) for set-up the Transition Database allows the approach to be adapted to local conditions as well as to be extended for additional terminologies and ontologies.

A Quality Assessment of the ARM-Net Registry Design and Data Collection

BACKGROUND

Registries are important in rare disease research. The Anorectal Malformation Network (ARM-Net) registry is a well-established European patient registry collecting demographic, clinical, and functional outcome data. We assessed the quality of this registry through review of the structure, data elements, collected data, and user experience.

MATERIAL AND METHODS

Design and data elements were assessed for completeness, consistency, usefulness, accuracy, validity, and comparability. An intra- and inter-user variability study was conducted through monitoring and re-registration of patients. User experience was assessed via a questionnaire on registration, design of registry, and satisfaction.

RESULTS

We evaluated 119 data elements, of which 107 were utilized and comprised 42 string and 65 numeric elements. A minority (37.0%) of the 2278 included records had complete data, though this improved to 83.5% when follow-up elements were excluded. Intra-observer variability demonstrated 11.7% incongruence, while inter-observer variability was 14.7%. Users were predominantly pediatric surgeons and typically registered patients within 11-30 min. Users did not experience any significant difficulties with data entry and were generally satisfied with the registry, but preferred more longitudinal data and patient-reported outcomes.

CONCLUSIONS

The ARM-Net registry presents one of the largest ARM cohorts. Although its collected data are valuable, they are susceptible to error and user variability. Continuous evaluations are required to maintain relevant and high-quality data and to achieve long-term sustainability. With the recommendations resulting from this study, we call for rare disease patient registries to take example and aim to continuously improve their data quality to enhance the small, but impactful, field of rare disease research.

LEVEL OF EVIDENCE

V.

Dos and don'ts in designing a computerized oral and lip squamous cell cancer registry

BACKGROUND

In the last ten years, many countries have started to develop constructive systems for registering common diseases and cancers. In this research, we intended to determine and identify the minimum data set (MDS) required for the design of the oral and lip squamous cell cancer registration system in Iran.

METHODS AND MATERIAL

At first, primary information elements related to disease registries were extracted using scientific papers published in reliable databases. After reviewing the books, related main guidelines, and 42 valid articles, the initial draft of a researcher-made questionnaire was compiled. To validate the questionnaire, two focus group meetings were held with 29 expert panel members. The final version of this questionnaire was prepared by extracting different questions and categories and receiving numerous pieces of feedback from specialists. Lastly, a final survey was conducted by the experts who were present at the previous stage.

RESULTS

Out of 29 experts participating in the study, 17 (58.62%) were men and 12 (40.37%) were women. The age range of experts varies from 34 to 58 years. One hundred-fourteen items, which are divided into ten main parts, were considered the main information elements of the registry design. The main minimum data sets have pertained to the demographic and clinical information of the patient, information related to the consumed drugs, initial diagnostic evaluations of the patient, biopsy, tumor staging at the time of diagnosis, clinical characteristics of the tumor, surgery, histopathological characteristics of the tumor, pathologic stage classification, radiotherapy details, follow-up information, and disease registry capabilities. The distinctive characteristics of the oral and lip squamous cell cancer registry systems, such as the title of the disease registration programme, the population being studied, the geographic extent of the registration, its primary goals, the definition of the condition, the technique of diagnosis, and the kind of registration, are all included in a model.

CONCLUSION

The benefits of designing and implementing disease registries can include timely access to medical records, registration of information related to patient care and follow-up of patients, the existence of standard forms and the existence of standard information elements, and the existence of an integrated information system at the country level.

Challenges in mapping European rare disease databases, relevant for ML-based screening technologies in terms of organizational, FAIR and legal principles: scoping review

Background

Given the increased availability of data sources such as hospital information systems, electronic health records, and health-related registries, a novel approach is required to develop artificial intelligence-based decision support that can assist clinicians in their diagnostic decision-making and shorten rare disease patients' diagnostic odyssey. The aim is to identify key challenges in the process of mapping European rare disease databases, relevant to ML-based screening technologies in terms of organizational, FAIR and legal principles.

Methods

A scoping review was conducted based on the PRISMA-ScR checklist. The primary article search was conducted in three electronic databases (MEDLINE/Pubmed, Scopus, and Web of Science) and a secondary search was performed in Google scholar and on the organizations' websites. Each step of this review was carried out independently by two researchers. A charting form for relevant study analysis was developed and used to categorize data and identify data items in three domains - organizational, FAIR and legal.

Results

At the end of the screening process, 73 studies were eligible for review based on inclusion and exclusion criteria with more than 60% (n = 46) of the research published in the last 5 years and originated only from EU/EEA countries. Over the ten-year period (2013-2022), there is a clear cycling trend in the publications, with a peak of challenges reporting every four years. Within this trend, the following dynamic was identified: except for 2016, organizational challenges dominated the articles published up to 2018; legal challenges were the most frequently discussed topic from 2018 to 2022. The following distribution of the data items by domains was observed - (1) organizational (n = 36): data accessibility and sharing (20.2%); long-term sustainability (18.2%); governance, planning and design (17.2%); lack of harmonization and standardization (17.2%); quality of data collection (16.2%); and privacy risks and small sample size (11.1%); (2) FAIR (n = 15): findable (17.9%); accessible sustainability (25.0%); interoperable (39.3%); and reusable (17.9%); and (3) legal (n = 33): data protection by all means (34.4%); data management and ownership (22.9%); research under GDPR and member state law (20.8%); trust and transparency (13.5%); and digitalization of health (8.3%). We observed a specific pattern repeated in all domains during the process of data charting and data item identification - in addition to the outlined challenges, good practices, guidelines, and recommendations were also discussed. The proportion of publications addressing only good practices, guidelines, and recommendations for overcoming challenges when mapping RD databases in at least one domain was calculated to be 47.9% (n = 35).

Conclusion

Despite the opportunities provided by innovation - automation, electronic health records, hospital-based information systems, biobanks, rare disease registries and European Reference Networks - the results of the current scoping review demonstrate a diversity of the challenges that must still be addressed, with immediate actions on ensuring better governance of rare disease registries, implementing FAIR principles, and enhancing the EU legal framework.

Data saves lives: optimising routinely collected clinical data for rare disease research

Necessity driven organisational change in the post-pandemic landscape has seen health care providers adopting innovations to manage and process health data. These include the use of 'real-world' datasets of routinely collected clinical information, enabling data-driven delivery. Rare disease risks being 'left-behind' unless our clinical and research communities engage with the challenges and opportunities afforded by the burgeoning field of health data informatics. We address the challenges to the meaningful use and reuse of rare disease data, and, through a series of recommendations around workforce education, harmonisation of taxonomy, and ensuring an inclusive health data environment, we highlight the role that those who manage rare disease must play in addressing them.

The Minimum Data Set for Rare Diseases: Systematic Review

BACKGROUND

The minimum data set (MDS) is a collection of data elements to be grouped using a standard approach to allow the use of data for clinical and research purposes. Health data are typically voluminous, complex, and sometimes too ambiguous to generate indicators that can provide knowledge and information on health. This complexity extends further to the rare disease (RD) domain. MDSs are essential for health surveillance as they help provide services and generate recommended population indicators. There is a bottleneck in international literature that reveals a global problem with data collection, recording, and structuring in RD.

OBJECTIVE

This study aimed to identify and analyze the MDSs used for RD in health care networks worldwide and compare them with World Health Organization (WHO) guidelines.

METHODS

The population, concept, and context methodology proposed by the Joanna Briggs Institute was used to define the research question of this systematic review. A total of 4 databases were reviewed, and all the processes were reported using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. The data elements were analyzed, extracted, and organized into 10 categories according to WHO digital health guidelines. The quality assessment used the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist.

RESULTS

We included 20 studies in our review, 70% (n=14) of which focused on a specific health domain and 30% (n=6) of which referred to RD in general. WHO recommends that health systems and networks use standard terminology to exchange data, information, knowledge, and intelligence in health. However, there was a lack of terminological standardization of the concepts in MDSs. Moreover, the selected studies did not follow the same standard structure for classifying the data from their MDSs. All studies presented MDSs with limitations or restrictions because they covered only a specific RD, or their scope of application was restricted to a specific context or geographic region. Data science methods and clinical experience were used to design, structure, and recommend a fundamental global MDS for RD patient records in health care networks.

CONCLUSIONS

Our study highlights the difficulties in standardizing and categorizing findings from MDSs for RD because of the varying structures used in different studies. The fundamental RD MDS designed in this study comprehensively covers the data needs in the clinical and management sectors. These results can help public policy makers support other aspects of their policies. We highlight the potential of our results to help strategic decisions related to RD.

TRIAL REGISTRATION

PROSPERO CRD42021221593; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221593.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1016/j.procs.2021.12.034.

The Korean undiagnosed diseases program phase I: expansion of the nationwide network and the development of long-term infrastructure

BACKGROUND

Phase I of the Korean Undiagnosed Diseases Program (KUDP), performed for 3 years, has been completed. The Phase I program aimed to solve the problem of undiagnosed patients throughout the country and develop infrastructure, including a data management system and functional core laboratory, for long-term translational research. Herein, we share the clinical experiences of the Phase I program and introduce the activities of the functional core laboratory and data management system.

RESULTS

During the program (2018-2020), 458 patients were enrolled and classified into 3 groups according to the following criteria: (I) those with a specific clinical assessment which can be verified by direct testing (32 patients); (II) those with a disease group with genetic and phenotypic heterogeneity (353 patients); and (III) those with atypical presentations or diseases unknown to date (73 patients). All patients underwent individualized diagnostic processes based on the decision of an expert consortium. Confirmative diagnoses were obtained for 242 patients (52.8%). The diagnostic yield was different for each group: 81.3% for Group I, 53.3% for Group II, and 38.4% for Group III. Diagnoses were made by next-generation sequencing for 204 patients (84.3%) and other genetic testing for 35 patients (14.5%). Three patients (1.2%) were diagnosed with nongenetic disorders. The KUDP functional core laboratory, with a group of experts, organized a streamlined research pipeline covering various resources, including animal models, stem cells, structural modeling and metabolic and biochemical approaches. Regular data review was performed to screen for candidate genes among undiagnosed patients, and six different genes were identified for functional research. We also developed a web-based database system that supports clinical cohort management and provides a matchmaker exchange protocol based on a matchbox, likely to reinforce the nationwide clinical network and further international collaboration.

CONCLUSIONS

The KUDP evaluated the unmet needs of undiagnosed patients and established infrastructure for a data-sharing system and future functional research. The advancement of the KUDP may lead to sustainable bench-to-bedside research in Korea and contribute to ongoing international collaboration.

The Interdisciplinary Diagnosis of Rare Diseases

BACKGROUND

Patients with rare diseases often undergo a diagnostic odyssey that can last many years until the diagnosis is definitively established. To improve the diagnosis and treatment of these patients, the German National Task Force for Patients With Rare Diseases (Nationales Aktionsbündnis für Menschen mit Seltenen Erkrankungen, NAMSE) has recommended the creation of Rare Disease Centers (RDCs).

METHODS

As part of the joint Translate-NAMSE project, sponsored by the G-BA Innovation Fund (G-BA, German Federal Joint Committee), we investigated the performance of RDCs in establishing the diagnosis of patients suspected to have a rare disease. The results of interdisciplinary case conferences and of exome diagnostic tests were analyzed in a prospective, multicenter observational study.

RESULTS

A total of 5652 patients (of whom 3619 were under 18 years old, and 2033 were at least 18 years old) from 10 RDCs who did not yet have a definitive diagnosis of a rare disease were included in the study. On average, those who were under 18 years old had been symptomatic for 4.5 years without receiving a diagnosis in a standard care setting; the analogous figure for adult patients was 8.2 years. Over the course of this project (2017-2021), 1682 patients (30%) received a definitive diagnosis. 193 had a common disease, 88 had a psychosomatic disease (only in patients who were at least 18 years old), and 1401 had a rare disease. 14 850 case conferences were conducted. 1599 exome analyses led to 506 definitive genetic diagnoses (32%).

CONCLUSION

A diagnostic evaluation with the aid of interdisciplinary case conferences and the opportunity for exome analysis can be of benefit to people with rare diseases who have not received a definitive diagnosis in a standard care setting. Further improvement of the diagnosis rate can come from whole-genome analysis and from the introduction of an international registry.

The ongoing French BaMaRa-BNDMR cohort: implementation and deployment of a nationwide information system on rare disease

BACKGROUND

BaMaRa allows the secure collection and deidentified centralization of medical data from all patients followed-up in a rare disease expert network in France, based on a minimum data set (SDM-MR). The present article describes BaMaRa information system implementation and development across the whole national territory as well as data access requests through BNDMR, the data warehouse which centralizes all BaMaRa data, during the 2015-2020 period.

MATERIALS AND METHODS

SDM-MR is made up of 60 interoperable items and is routinely collected through BaMaRa in rare disease centers as part of care and discharged into BNDMR after deidentification and data reconciliation. Data access is regulated by a scientific committee.

RESULTS

In total, 668 002 affected patients had an SDM-MR recorded in BNDMR by the end of 2020 with a mean value of 3.4 activities per patients. Data access was provided for 66 projects.

CONCLUSION

The BaMaRa-BNDMR infrastructure provides an administrative and epidemiological resources for rare diseases in France.