Datasets used for the Task 5.3 in the Project "Decision-Making Systems for the Management of Hospital-Acquired Infections and Antimicrobial Resistance (MDR-CDSS)

This is the dataset needed to run the tool that predicts the resistance profile of genomics assemblies of ESKAPEE pathogens. This research has been co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, and Greece 2.0, under the call 'RESEARCH – CREATE – INNOVATE' (ID 16971), with project id: TAEDK-06179. Project Abstract The intensive use of antimicrobial agents has facilitated the emergence of a variety of resistance mechanisms in bacteria. The selection and prevalence of resistant bacterial clones is particularly high in the nosocomial environment, while resistance rates in Greece are amongst the highest in Europe. The consequences of this problem must be assessed both medically and economically, since the patients infected with antibiotic-resistant microorganisms are in increased risk to receive an inappropriate therapy and to have a longer course of disease or a fatal outcome. The available surveillance data show that the infection control policies used so far have failed to address the ever-increasing problem. Scientific observations have shown that changes in hospital microflora are affected by multiple factors. However, the mechanisms that govern them are not yet fully elucidated. Containment of hospital infections and their successful treatment require the detailed characterization of resistant pathogens, host/pathogen interactions and biodiversity of patients' microflora (intestinal and oral cavity flora) as well as knowledge of the microbial flora of each hospital and the transfer of patients between hospital units. Therefore, the implementation of an integrated approach that takes into account multiple factors through innovative computational methodologies, and capable of indicating the therapeutic approaches with the highest probability of success, is imperative. The realization of a decision support system could lead to the reduction of antimicrobial resistance. In this proposal, the assessment of clinical, biochemical, microbiological and genetic data and the use of specialized software for real-time mapping of microbial flora in hospitals will provide higher accuracy in predicting the efficacy of the treatment and choosing the most appropriate therapeutic regimen. In this project we would attempt: a) to identify in real-time the risk factors for hospital infections for each hospital, b) to guide empirical treatment, and c) to assist therapeutic decisions through predictions based on clinical, epidemiological, microbiological and genomic data. The strategic objectives of the proposal are: (a) the development of a monitoring system for the active surveillance of hospitals and facilities and (b) the development of a decision support system for the adaptation of therapeutic protocols. The products to be developed in the proposed project are an 'Intelligent Electronic Decision Support System' and a 'Hospital Monitoring System'. The proposed products will constitute a software suite for monitoring resistant bacterial strains in the hospital environment, identifying reliable biomarkers to track the course of infection and timely adaptation of treatment.