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RESCUING: 1st Ever Publication

The research and results coming from COMBACTE can be mined for years to come. In these series, we look back on the very first scientific papers per project and study produced in our consortium. COMBACTE-MAGNET's RESCUING was a retrospective observational study to assess the clinical management and outcomes of hospitalized patients with complicated urinary tract infection in countries with high prevalence of multidrug resistant gram-negative bacteria.

The first ever paper RESCUING published was its study protocol in 2016 in BMJ Open. Read the abstract below.


The emergence of multidrug resistant (MDR) Gram-negative bacteria (GNB), including carbapenemase-producing strains, has become a major therapeutic challenge. These MDR isolates are often involved in complicated urinary tract infection (cUTI), and are associated with poor clinical outcomes. The study has been designed to gain insight into the epidemiology, clinical management, outcome and healthcare cost of patients with cUTI, especially in countries with high prevalence of MDR GNB.

This multinational and multicentre observational, retrospective study will identify cases from 1 January 2013 to 31 December 2014 in order to collect data on patients with cUTI as a cause of hospital admission, and patients who develop cUTI during their hospital stay. The primary end point will be treatment failure defined as the presence of any of the following criteria: (1) signs or symptoms of cUTI present at diagnosis that have not improved by days 5–7 with appropriate antibiotic therapy, (2) new cUTI-related symptoms that have developed within 30 days of diagnosis, (3) urine culture taken within 30 days of diagnosis, either during or after completion of therapy, that grows ≥104 colony-forming unit/mL of the original pathogen and (4) death irrespective of cause within 30 days of the cUTI diagnosis.

1000 patients afford a power of 0.83 (α=0.05) to detect an absolute difference of 10% in the treatment failure rate between MDR bacteria and other pathogens. This should allow for the introduction of about 20 independent risk factors (or their interaction) in a logistic regression model looking at risk factors for failure.


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