Publications

Journal of Clinical Microbiology, 52(5), 1 May 2014, pp 1741-44, doi: 10.1128/JCM.03614-13
Intensive Care Medicine, 40(4), 1 April 2014, pp 564–571, doi: 10.1007/s00134-014-3225-8
Expert Opinion on Therapeutic Targets, 18(8), 1 August 2014, pp 851-61, doi: 10.1517/14728222.2014.925881

Scientific approach – Work plan

Overall strategy

The R-GNOSIS project is built on five clinical work packages (WP2-6), all interacting with work packages on microbiology (WP7) and mathematical modelling (WP8).

Separate horizontal work packages will lead and deliver high-quality project management, (WP1) data management (WP9) and transformational dissemination (WP10).

The scientific contents of the clinical work packages will be hypothesis-driven, with clear and ambitious outcomes that matter to the health and well-being of the people in Europe and beyond. Each primary outcome addresses “reduction of infections with or spread of MDR-GNB”.

R-GNOSIS will perform five pivotal international clinical intervention studies, each yielding a clear-cut solution, readily implementable in clinical practice, if proven effective. Each study will be innovative; because of technology used and/or because of its hypothesis-driven clinical approach.

The five clinical studies will investigate the following interventions:

  • A Point-Of-Care-Testing guided management strategy to improve appropriate antibiotic prescription for uncomplicated UTI in primary care
  • Gut decolonization in outpatients with intestinal carriage of MDR-GNB
  • A “test and prescribe” strategy, based on rapid diagnostic testing of faeces for MDR-GNB to optimize antibiotic prophylaxis in colo-rectal surgery
  • Contact Isolation of patients with ESBL-producing Enterobacteriaceae in general hospital wards
  • Three Decolonization strategies in ICUs

Seven laboratories across Europe will perform microbiological analyses, as well as unique quantitative experiments. All information will be integrated by three groups of mathematical modellers into highly innovative models to better understand and predict future trends and effects of interventions.