Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening.

BACKGROUND: Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. METHODS: In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. RESULTS: In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. CONCLUSIONS: The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.

Are the European reference networks for rare diseases ready to embrace machine learning? A mixed-methods study.

BACKGROUND: The delay in diagnosis for rare disease (RD) patients is often longer than for patients with common diseases. Machine learning (ML) technologies have the potential to speed up and increase the precision of diagnosis in this population group. We aim to explore the expectations and experiences of the members of the European Reference Networks (ERNs) for RDs with those technologies and their potential for application. METHODS: We used a mixed-methods approach with an online survey followed by a focus group discussion. Our study targeted primarily medical professionals but also other individuals affiliated with any of the 24 ERNs. RESULTS: The online survey yielded 423 responses from ERN members. Participants reported a limited degree of knowledge of and experience with ML technologies. They considered improved diagnostic accuracy the most important potential benefit, closely followed by the synthesis of clinical information, and indicated the lack of training in these new technologies, which hinders adoption and implementation in routine care. Most respondents supported the option that ML should be an optional but recommended part of the diagnostic process for RDs. Most ERN members saw the use of ML limited to specialised units only in the next 5 years, where those technologies should be funded by public sources. Focus group discussions concluded that the potential of ML technologies is substantial and confirmed that the technologies will have an important impact on healthcare and RDs in particular. As ML technologies are not the core competency of health care professionals, participants deemed a close collaboration with developers necessary to ensure that results are valid and reliable. However, based on our results, we call for more research to understand other stakeholders' opinions and expectations, including the views of patient organisations. CONCLUSIONS: We found enthusiasm to implement and apply ML technologies, especially diagnostic tools in the field of RDs, despite the perceived lack of experience. Early dialogue and collaboration between health care professionals, developers, industry, policymakers, and patient associations seem to be crucial to building trust, improving performance, and ultimately increasing the willingness to accept diagnostics based on ML technologies.

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.

Socio-Economic Burden of Myasthenia Gravis: A Cost-of-Illness Study in Bulgaria.

Background: Myasthenia gravis (MG) is a chronic autoimmune disorder, which is characterized by fatigable muscle weakness with frequent ocular signs and/or generalized muscle fatigue, and occasionally associated with thymoma. MG patients and their families face a significant socio-economic burden. This population is often experiencing unemployment, unwilling job transfers and decreased income. Objective: This study aimed to estimate the annual costs from a societal perspective in a triple dimension of direct health care costs, direct non-health care costs (formal and informal care) and labor productivity losses in MG patients from Bulgaria, as well as to identify the main clinical and demographical cost drivers. Methods: A bottom-up, cross-sectional, cost-of-illness analysis of 54 adult MG patients was carried out in 2020. To collect data on demographic characteristics, health resource utilization, informal care and productivity losses, questionnaires were administered to and completed by patients. Results and Conclusion: Median annual costs of MG in Bulgaria were 4,047 EUR per patient. Direct costs slightly outweighed indirect costs, with drugs cost item having the biggest monetary impact. Despite the zero-inflated median, hospitalizations also influenced the direct costs by an estimated amount of 1,512 EUR in the 3rd quartile. Social services and professional caregiver costs were found to be almost missing, with the vast majority of patients reporting reliance on informal caregivers. Severe generalized disease, disease crises, and recurrent infections were confirmed as statistically significant cost driving factors. There were no severe generalized MG patients in the bottom quartile of the total costs distribution. It should be noted that in both cases of crises or infections, the overall increase in the total costs was mainly due to higher indirect costs observed. Reliance on family members as informal caregivers is routine among Bulgarian MG patients. This phenomenon is likely due to the lack of access to appropriate social services. Moreover, it is directly related with higher disease burden and significant inequalities. There is a need for further research on MG in Bulgaria in order to design targeted health policies that meet the needs and expectations of these patients.

Arterialized cephalic vein as a femoropopliteal bypass graft: A case report.

The overall therapeutic strategy in patients with diabetes mellitus and peripheral arterial disease affecting several arterial segments is problematic. Usually, the greater saphenous vein (GSV) is the gold standard for bypass. However, alternative venous grafts may be necessary when the GSV is not available. The superficial veins of the arm are not suitable for bypass operations in most cases because of anatomical variations, inadequate diameter or very thin walls. However, arterialization of the cephalic vein can dilate and strengthen the walls, thus avoiding postoperative complications. The present patient, whose case was followed for 20 months postoperatively, suffered both from diabetes and peripheral arterial disease in a setting of gangrene and a lack of the GSV. The treament of the patient and the problems it incurred are discussed. The advantages of preoperative temporary arteriovenous shunting to improve the quality of the cephalic vein as an alternative autogenous graft are analyzed. Currently, the patient has excellent postoperative results, with healing of the ulcers and preserved peripheral pulses.