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Institute of Medical Biometry and Statistics (IMBI)

Overview of selected projects

COMBACTE-MAGNET - Combatting Bacterial Resistance in Europe - Molecules against Gram-Negative Infections

COMBACTE-Magnet is a consortium within the joint research project COMBACTE of the European Federation of Pharmaceutical Industries and Associations (EFPIA), the Innovative Medicine Initiative (IMI) and the European Union (EU).

Focusing on critically ill patients treated in intensive-care units, this project aims to find better options to deal with the threat of ICU-associated infections caused by gram-negative bacteria such as Pseudomonas aeruginosa.

In collaboration with clinicians, epidemiologists and pharmacists, our group develops mathematical models to describe the burden of ICU-associated infections. Accounting for challenges such as the time-dependency of exposure and outcome as well as the competing risks setting, we provide innovative models that allow an unbiased quantification of effects on mortality, length of ICU stay, costs and treatment.

Principal Investigator: Martin Wolkewitz

Contributor: Derek Hazard, Maja von Cube, Susanne Weber, Klaus Kaier


Prevalence studies to measure exposure to health-associated infections (HAIs) have a long tradition in the field of infection prevention and control. The pioneering study "Study on the Efficacy of Nosocomial Infection Control" (SENIC), initiated by the US Center for Disease Control and Prevention (CDC) in the 1970s, has clearly demonstrated the benefits of HAI surveillance. The HAI prevalence at this time was estimated at about 5.2%. In 1981, the World Health Organization (WHO) convened a Consultative Group to monitor, control and prevent HAIs. In particular, the group recommended conducting HAI prevalence studies to assess the burden of the problem in different parts of the world.

More recently, the European Center for Disease Prevention and Control (ECDC) and the CDC have carried out large point prevalence surveys in Europe and the United States. Most local, regional and national surveys used point prevalence methodology, i.e. only HAIs active on the day of the survey will be considered.

The "Swissnoso PPS 2017" project is a prevalence survey of HAIs in Switzerland

  • to obtain representative data on HAIs in acute care hospitals
  • to obtain data on antibiotic use in acute care hospitals
  • to estimate the attributable mortality due to HAIs
  • to estimate the costs of healthcare-associated infections

Principal Investigator: Martin Wolkewitz

Contributor: Sam Doerken

COMBACTE-NET - Combatting Bacterial Resistance in Europe: Networks to improve clinical trials for antibacterials

COMBACTE-Net is a consortium within the joint research project COMBACTE of the European Federation of Pharmaceutical Industries and Associations (EFPIA), the Innovative Medicine Initiative (IMI) and the European Union (EU).

The aim of COMBACTE-Net is to create strong clinical, laboratory and research networks across Europe. Part of the work of COMBACTE-Net is to perform clinical studies. Two of those studies are the prospective observational studies ASPIRE-ICU and ASPIRE-SSI (Advanced understanding of Staphylococcus aureus and Pseudomonas aeruginosa Infections in EuRopE). ASPIRE-ICU investigates the incidence of S. aureus and P. aeruginosa intensive care unit (ICU) pneumonia, and ventilator-associated pneumonia. The aim is to assess the association between patient-related and contextual factors. ASPIRE-SSI investigates the incidence of S. aureus surgical site infection (SSI). The aim is to identify risk factors and develop a robust S. aureus SSI prediction. The settings considered in both studies pose different statistical challenges, such as competing risks. The task of our group is to support the study teams in terms of consulting and analysis.

Principal Investigator: Martin Wolkewitz

Contributor: Susanne Weber, Derek Hazard

Causal modelling to support medical decision making in the treatment of multidrug resistant infections

Inappropriate treatment of infections, even if only for a few days, may have severe consequences for the patients. In 2017, the WHO - for the first time ever - published a top priority list of pathogens for which novel treatments are urgently needed. Currently, antibiotics are prescribed according to the susceptibility of the pathogen causing the infection. However, medical decision making becomes increasingly complicated. The spread of multidrug resistant pathogens leads to loss of efficacy of the available standard therapies. Moreover, severely ill patients with infections due to (multi)resistant pathogens often receive a complex combination of antibiotics resulting in limited treatment options. For example, in a recent study, we found no evidence for the efficacy of early adequate treatment of ventilator associated pneumonia caused by Pseudomonas aeruginosa among invasively ventilated patients (sample size = 465). Other studies lead to similar dissatisfying conclusions. Evidence for optimal medical decision making is urgently needed. An attractive solution is the identification of personalized interventions that account not only for the type of pathogen, but also for the dynamically changing antibiotic treatment of the patient, the patient’s treatment response and the course of disease over time.

Dynamic treatment regimens (DTRs) can be regarded as a decision tree aiding the clinician to optimize the treatment for an individual patient over the course of time. In contrast to static treatments such as the prescription of a specific antibiotic for a specific pathogen, DTRs account for treatment response and the evolution of patient characteristics by allowing for an adaption of the treatment over the course of time. Causal inference techniques for evidence-based DTRs have become a major field of research in Statistics with main application to chronic diseases. The idea to adapt these methods to identify tailored antibiotic treatments of specific types of hospital acquired infections has been proposed only recently. However, applying these techniques to data on hospitalized patients bears major statistical challenges due to the time dynamics of the data situation and multiple possible outcomes (e.g. death, discharge alive, clinical cure) which result in a competing risks setting. Solid specialized statistical expertise and close collaboration with clinicians is indispensable to identify novel therapies and defeat multidrug resistance.  

In this research project statistical inference techniques are adapted and advanced to identify optimal DTRs for infected hospitalized patients.  

Principal Investigator: Maja von Cube

Contributor: Martin Wolkewitz, Siegbert Rieg