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Inhibition of the neonatal Fc receptor (FcRn) is a new and promising therapeutic option for treatment of generalized Myasthenia Gravis (gMG). Among available compounds, Efgartigimod (EFG) was recently approved for gMG with positive anti-AChR antibodies. We report our real-world experience with EFG in AChR+ as well as in non-AChR gMG since November 2021. We treated 17 patients with AChR+ gMG and 13 with non-AChR gMG (5 MuSK, 2 LRP4 and 6 triple negative, confirmed by live CBA). EFG was administered according to the GENERATIVE protocol (consisting of a Fixed period of 2 treatment cycles of 4 infusions given at weekly intervals, followed by a Flexible period during which EFG was given in case of clinical worsening) starting from November 2021. Outcomes were evaluated by means of the MG-ADL (MG Activity of Daily Living), QMG (Quantitative MG) and MGC (MG Composite Score) rating scales. Clinically meaningful improvement was observed with the scales adopted at each cycle in AChR+ gMG. 88% of them needed 3 treatment cycles, 71% 4 cycles, and 59% 5 or more cycles. 5 patients (29%) needed retreatment every 4 weeks. Positive results were found in non-AChR gMG (mean score reductions for MG-ADL -5.5; MGC -8.5 and QMG -4.84). The number of cycles/year was 3.9±0.9. The interval between cycles was 8.9±3.8 weeks. MG-ADL improvement greater than 5 points was recorded in 49% of patients. During treatment with EFG none of our MG patients has been hospitalized or needed treatment with plasmaexchange or immunoglobulins (compared to 65% before EFG). EFG was well tolerated since no major side effects or infusion related reactions occurred. EFG was effective in AChR+ as well as non-AChR gMG and modified significantly the course of the disease. Our experience strengthens the role of FcRn inhibition as a new effective tool also in the management of MG without anti-AChR antibodies, as demonstrated by RCT in AChR-associated MG. [ABSTRACT FROM AUTHOR]

Background and Aim: Bone as an endocrine organ regulates metabolic processes independently of mineral metabolism through the production/release of proteins collectively named 'osteokines'. Relevant connections were reported between the insulin/glucose system, calcification of the atherosclerotic plaque, and several osteokines. We aimed to test the hypothesis that the osteokine network could be involved in beta-cell function, insulin sensitivity, and vascular damage in a cohort of people with newly diagnosed type 2 diabetes (T2D)., Subjects and Methods: In 794 drug-naive, GADA-negative, newly-diagnosed T2D patients (mean ± SD age: 59 ± 9.8 years; BMI: 29.3 ± 5.3 kg/m 2 ; HbA1c: 6.6 ± 1.3%) we assessed: plasma concentration of osteocalcin (OCN), osteopontin (OPN), RANKL, and its putative decoy receptor osteoprotegerin (OPG); insulin sensitivity (SI) by hyperinsulinemic euglycemic clamp; beta cell function (BCF), estimated by OGTT minimal modelling and expressed as derivative (DC) and proportional (PC) control. Echo-doppler of carotid and lower limb arteries were also performed in 708 and 701 subjects, respectively., Results: OCN, RANKL and OPG were significantly associated with PC (p < 0.02); OCN was positively related to DC (p = 0.018). OPG was associated with lower IS (p < 0.001). Finally, the higher RANKL levels, the greater was the severity of atherosclerosis in common carotid artery (p < 0.001). Increased OPG and OPN concentrations were related to subclinical atherosclerosis in peripheral arteries of lower limbs (p = 0.023 and p = 0.047, respectively)., Conclusion: These data suggest that, in patients with newly diagnosed T2D, the osteokine network crosstalks with the glucose/insulin system and may play a role in modulating the atherosclerotic process., (© 2024 The Author(s). Diabetes/Metabolism Research and Reviews published by John Wiley & Sons Ltd.)

Automation of surveillance of infectious diseases-where algorithms are applied to routine care data to replace manual decisions-likely reduces workload and improves quality of surveillance. However, various barriers limit large-scale implementation of automated surveillance (AS). Current implementation strategies for AS in surveillance networks include central implementation (i.e. collecting all data centrally, and central algorithm application for case ascertainment) or local implementation (i.e. local algorithm application and sharing surveillance results with the network coordinating center). In this perspective, we explore whether current challenges can be solved by federated AS. In federated AS, scripts for analyses are developed centrally and applied locally. We focus on the potential of federated AS in the context of healthcare associated infections (AS-HAI) and of severe acute respiratory illness (AS-SARI). AS-HAI and AS-SARI have common and specific requirements, but both would benefit from decreased local surveillance burden, alignment of AS and increased central and local oversight, and improved access to data while preserving privacy. Federated AS combines some benefits of a centrally implemented system, such as standardization and alignment of an easily scalable methodology, with some of the benefits of a locally implemented system including (near) real-time access to data and flexibility in algorithms, meeting different information needs and improving sustainability, and allowance of a broader range of clinically relevant case-definitions. From a global perspective, it can promote the development of automated surveillance where it is not currently possible and foster international collaboration.The necessary transformation of source data likely will place a significant burden on healthcare facilities. However, this may be outweighed by the potential benefits: improved comparability of surveillance results, flexibility and reuse of data for multiple purposes. Governance and stakeholder agreement to address accuracy, accountability, transparency, digital literacy, and data protection, warrants clear attention to create acceptance of the methodology. In conclusion, federated automated surveillance seems a potential solution for current barriers of large-scale implementation of AS-HAI and AS-SARI. Prerequisites for successful implementation include validation of results and evaluation requirements of network participants to govern understanding and acceptance of the methodology., (© 2024. The Author(s).)

The motivation behind this research is to perform a privacy-preserving analysis of data located at remote sites and in different jurisdictions with no possibility of sharing individual-level information. Here, we present key findings from requirements analysis and a resulting federated data analysis workflow built using open-source research software, where patient-level information is securely stored and never exposed during the analysis process. We present additional improvements to further strengthen the security of the workflow. We emphasize and showcase the use of data harmonization in the analysis. The data analysis is done using the R language for statistical computing and DataSHIELD libraries for non-disclosive analysis of sensitive data. The workflow was validated against two data analysis scenarios, confirming the results obtained with a centralized analysis approach. The clinical datasets are part of the large Pan-European SARS-Cov-2 cohort, collected and managed by the ORCHESTRA project. We demonstrate the viability of establishing a cross-border federated data analysis framework and conducting an analysis without exposing patient-level information, achieving results equivalent to centralized non-secure analysis. However, it is vital to ensure requirements associated with data harmonization, anonymization and IT infrastructure to maintain availability, usability and data security.

Background: Cerebrovascular accidents (CVA) represent a major complication in diabetes (DM). Real-life evidence as to whether modern management of CVA and DM have softened this relationship is limited. Therefore, we estimated prevalence and impact of DM on in-hospital survival and complications in a contemporary cohort of subjects with CVA., Methods: We retrospectively evaluated the records of 937 patients admitted for CVA at the Stroke Unit of Verona University Hospital during a 3-year period. Pre-existing or de novo DM was ascertained by prior diagnosis, glucose-lowering therapy at admission/discharge or admittance plasma glucose ≥ 200 mg/dL. Multiple regressions were applied to test DM as predictor of in-hospital mortality, complications (composite of infections, cardio- and cerebrovascular complications, major bleeding and pulmonary complications), duration and costs of hospitalization., Results: Diabetes prevalence was 21%, of which 22% de novo diagnoses. Compared to non-DM, diabetic individuals were older and carried an increased burden of cardiovascular risk factors. Compared to known DM, de novo DM individuals were younger, had higher admittance plasma glucose and poorer cardiovascular comorbidities. Overall, DM versus non-DM individuals did not show significantly increased risk of death (14.0 vs. 9.3%; crude-OR 1.59 95% CI 0.99-2.56). Controlling for confounders did not improve significance. DM resulted independent predictor for in-hospital complications (36.2% vs. 26.9%; adj-OR 1.49, 1.04-2.13), but not for duration and costs of hospitalization., Conclusion: DM frequently occurs in patients admitted for stroke and carries an excess burden of adverse in-hospital complications, urgently calling for strategies to anticipate DM diagnosis and tailored treatment in high-risk individuals., (© 2024. Springer-Verlag Italia S.r.l., part of Springer Nature.)

Background: It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets., Objective: This study's objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials., Methods: We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs., Results: Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date., Conclusions: The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element's (variable's) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by "wrapping" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium's Clinical Data Acquisition Standards Harmonization Model., (©Caroline Stellmach, Sina Marie Hopff, Thomas Jaenisch, Susana Marina Nunes de Miranda, Eugenia Rinaldi, The NAPKON, LEOSS, ORCHESTRA, and ReCoDID Working Groups. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.06.2024.)

Background: The ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR)., Methods: The three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation., Results: The resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2., Conclusions: Considering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Background and Purpose: Inhibition of the neonatal Fc receptor (FcRn) for IgG is a promising new therapeutic strategy for antibody-mediated disorders. We report our real-life experience with efgartigimod (EFG) in 19 patients with generalized myasthenia gravis (gMG) along a clinical follow-up of 14 months., Methods: EFG was administered according to the GENERATIVE protocol (consisting of a Fixed period of two treatment cycles [given 1 month apart] of four infusions at weekly intervals, followed by a Flexible period of re-cycling in case of worsening). Eight patients were positive for acetylcholine receptor antibody, four for muscle-specific tyrosine kinase antibody, and two for lipoprotein-related protein 4 antibody, and five were classified as triple negative. Efficacy of EFG was assessed by the Myasthenia Gravis Activities of Daily Living, Myasthenia Gravis Composite, and Quantitative Myasthenia Gravis scales., Results: Fifty-three percent of patients needed three treatment cycles, 26% needed four, and 21% needed five along the 14-month clinical follow-up. Meaningful improvement was observed at the end of each cycle with the clinical scores adopted. EFG had a dramatic effect on disease course, as during the year before treatment eight of 19 patients (42%) were hospitalized, and 15 of 19 (79%) needed treatment with plasma exchange or immunoglobulins; three of 19 (16%) were admitted to the intensive care unit. During EFG, none of the patients was hospitalized and only one patient required plasma exchange and intravenous immunoglobulins. No major side effects or infusion-related reactions occurred., Conclusions: We observed that EFG was safe and modified significantly the course of the disease along a 14-month follow-up. Our experience strengthens the role of FcRn inhibition as an effective new tool for long-term treatment of gMG., (© 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)