The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary.

The sharing and documentation of cardiovascular research data are essential for efficient use and reuse of data, thereby aiding scientific transparency, accelerating the progress of cardiovascular research and healthcare, and contributing to the reproducibility of research results. However, challenges remain. This position paper, written on behalf of and approved by the German Cardiac Society and German Centre for Cardiovascular Research, summarizes our current understanding of the challenges in cardiovascular research data management (RDM). These challenges include lack of time, awareness, incentives, and funding for implementing effective RDM; lack of standardization in RDM processes; a need to better identify meaningful and actionable data among the increasing volume and complexity of data being acquired; and a lack of understanding of the legal aspects of data sharing. While several tools exist to increase the degree to which data are findable, accessible, interoperable, and reusable (FAIR), more work is needed to lower the threshold for effective RDM not just in cardiovascular research but in all biomedical research, with data sharing and reuse being factored in at every stage of the scientific process. A culture of open science with FAIR research data should be fostered through education and training of early-career and established research professionals. Ultimately, FAIR RDM requires permanent, long-term effort at all levels. If outcomes can be shown to be superior and to promote better (and better value) science, modern RDM will make a positive difference to cardiovascular science and practice. The full position paper is available in the supplementary materials.

Needs assessment towards research data management at the Medical Faculty of the University of Freiburg - Data of the BE-KONFORM study.

In order to investigate employees' needs of the Medical Faculty of the University of Freiburg regarding research data management, the BE-KONFORM study was performed in a two-step approach. First, guideline-based qualitative video interviews with four researchers were performed to identify key constructs of relevance. Second, a standardized online survey was conducted from 1st to 15th of November 2020 based on e-mail invitation by the dean and a faculty newsletter. The questionnaire was provided bilingual (English and German) using a backward-forward translation method, no reminders and incentives were used to increase the response rate. The online survey was programmed in REDCap and was accessible via online link. The target population were members of the Medical Faculty (listed in the newsletter mailing list) regardless of the type of working contract signed. The final dataset contains 236 complete cases (90% German and 10% English). The study includes a randomised module asking for data publication (group A) or not (group B). 113 cases were randomized into group A and 99% of them consented to the publication of the collected research data in anonymized form (n=112). The dataset comprised questions about work-related characteristics (professional status, working experience, scientific field of work), data management-related items (definition of research data management, type of data used, type of storage used for saving data, use of electronic laboratory notebooks), experience and attitudes towards data publication in data repositories, as well as needs and preferences regarding research data management support. The produced data offers the possibility to connect with other data collected in this field in other contexts (faculties or universities).

Netboost: Boosting-Supported Network Analysis Improves High-Dimensional Omics Prediction in Acute Myeloid Leukemia and Huntington's Disease.

State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features play an important role in certain diseases. We present Netboost, a three-step dimension reduction technique. First, a boosting-based filter is combined with the topological overlap measure to identify the essential edges of the network. Second, sparse hierarchical clustering is applied on the selected edges to identify modules and finally module information is aggregated by the first principal components. We demonstrate the application of the newly developed Netboost in combination with CoxBoost for survival prediction of DNA methylation and gene expression data from 180 acute myeloid leukemia (AML) patients and show, based on cross-validated prediction error curve estimates, its prediction superiority over variable selection on the full dataset as well as over an alternative clustering approach. The identified signature related to chromatin modifying enzymes was replicated in an independent dataset, the phase II AMLSG 12-09 study. In a second application we combine Netboost with Random Forest classification and improve the disease classification error in RNA-sequencing data of Huntington's disease mice. Netboost is a freely available Bioconductor R package for dimension reduction and hypothesis generation in high-dimensional omics applications.

Systemic Immunomodulating Therapies for Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis: A Systematic Review and Meta-analysis.

Importance: Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) are rare but severe adverse reactions with high mortality. There is no evidence-based treatment, but various systemic immunomodulating therapies are used. Objectives: To provide an overview on possible immunomodulating treatments for SJS/TEN and estimate their effects on mortality compared with supportive care. Data Sources: A literature search was performed in December 2012 for articles published in MEDLINE, MEDLINE Daily, MEDLINE Inprocess, Web of Science, EMBASE, Scopus, and the Cochrane Library (Central) from January 1990 through December 2012, and updated in December 2015, in the English, French, Spanish, and German languages looking for treatment proposals for SJS/TEN. Other sources were screened manually. Study Selection: Initially, 157 randomized and nonrandomized studies on therapies (systemic immunomodulating therapies or supportive care) for SJS/TEN were selected. Data Extraction and Synthesis: Relevant data were extracted from articles. Authors were contacted for further information. Finally, 96 studies with sufficient information regarding eligibility and adequate quality scores were considered in the data synthesis. All steps were performed independently by 2 investigators. Meta-analyses on aggregated study data (random-effects model) and individual patient data (IPD) (logistic regression adjusted for confounders) were performed to assess therapeutic efficacy. In the analysis of IPD, 2 regression models, stratified and unstratified by study, were fitted. Main Outcomes and Measures: Therapy effects on mortality were expressed in terms of odds ratios (ORs) with 95% CIs. Results: Overall, 96 studies (3248 patients) were included. Applied therapies were supportive care or systemic immunomodulating therapies, including glucocorticosteroids, intravenous immunoglobulins, cyclosporine, plasmapheresis, thalidomide, cyclophosphamide, hemoperfusion, tumor necrosis factor inhibitors, and granulocyte colony-stimulating factors. Glucocorticosteroids were associated with a survival benefit for patients in all 3 analyses but were statistically significant in only one (aggregated data: OR, 0.5; 95%% CI, 0.3-1.01; IPD, unstratified: OR, 0.7; 95% CI, 0.5-0.97; IPD, stratified: OR, 0.8; 95% CI, 0.4-1.3). Despite the low patient size, cyclosporine was associated with a promising significant result in the only feasible analysis of IPD (unstratified model) (OR, 0.1; 95% CI, 0.0-0.4). No beneficial findings were observed for other therapies, including intravenous immunoglobulins. Conclusions and Relevance: Although all analyses, including the unstratified model, had limitations, glucocorticosteroids and cyclosporine were the most promising systemic immunomodulating therapies for SJS/TEN. Further evaluation in prospective studies is required. However, this work provides a comprehensive overview on proposed systemic immunomodulating treatments for SJS/TEN, which is of great relevance for treating physicians.