RISK PRINCIPE – Risk prediction for infection control and treatment in hospitals

From a medical perspective, nosocomial, or hospital-acquired, infections are one of the greatest medical burdens in high-income countries. In Germany, these infections are associated with an estimated 16,000 deaths per year. Prevention of these infections is one of the most important issues in patient safety. In addition to the negative consequences that nosocomial infections have for individual patients, these infections also lead to higher healthcare expenditures. Often, the infections are treated with antibiotics, which in turn can contribute to antimicrobial resistance, especially if their use is poorly targeted.

A multidisciplinary approach to infection prevention is therefore required to contain nosocomial infections. Surveillance, the accurate documentation and monitoring of infections, is one of the key components of prevention in individual hospitals. Based on the information obtained, healthcare professionals and policy makers can identify areas at risk and initiate targeted interventions.

However, current survey and prevention measures are overly resource-intensive (time, staff), universal rather than individually risk-adapted, and usually limited to specific wards and patient populations. Therefore, identifying the best possible combination of the following is a requirement: Reduction of infection risk as well as optimal distribution of resources.

RISK PRINCIPE Piktogramm — In summary, RISK PRINCIPE addresses the goal of developing and implementing (semi-)automated surveillance and data-based risk prediction for bloodstream infections with subsequent visualization for more effective and efficient infection prevention.

RISK PRINCIPE can improve the quality of patient care by helping to identify high-risk areas and patients, reducing the time required for surveillance and increasing responsiveness.

This will be tested using hospital onset bacteremia (HOBs) as an example. To achieve this goal, different data sources will be evaluated to create a risk profile, which will then be tested.

Two complexes of tasks are to be implemented:

Use of patient data to create a patient- or patient group-specific risk profile that can be transferred to a computer-assisted application.
Design and validation of a (semi-)automated surveillance system including visualization.

Preliminary work and experience from the MII consortia will be used.

The project builds on a strategic alliance between IT, surveillance and infection prevention that serves as a fundamental basis for sustainable collaboration over time. In addition to 13 university hospitals, the RKI, the National Reference Center for Surveillance of Nosocomial Infections and the "Aktionsbündnis Patientensicherheit" are also involved in the project.

The project started on July 1, 2023.

Management team:

Prof. Dr. med. Simone Scheithauer

Project coordination
University Medical Center Göttingen
Institute for Hospital Hygiene and Infectiology
37075 Göttingen

Tel.: +49 551 3962091
Tel.: +49 551 3962093
E-Mail: krankenhaushygiene.leitung@med.uni-goettingen.de
E-Mail: simone.scheithauer@med.uni-goettingen.de

Univ.-Prof. Dr. med. Mathias Pletz

Project Co-Coordination
University Hospital Jena
Institute of Infectious Diseases and Infection Control
Am Klinikum 1
07747 Jena

Tel.: +49 3641 9324650
E-Mail: mathias.pletz@med.uni-jena.de

Univ.-Prof. Dr. André Scherag

Project Co-Coordination
University Hospital Jena
Institute of Medical Statistics, Computer and Data Sciences (IMSID)
Bachstraße 18
07743 Jena

Tel.: +49 3641 9396954
E-Mail: andre.scherag@med.uni-jena.de

Prof. André Scherag © Universitätsklinikum Jena — Prof. Dr. André Scherag

Prof. Dr. med. Dr.-Ing. Michael Marschollek

Project Co-Coordination
Peter L. Reichertz Institute for Medical Informatics
of the TU Braunschweig and Medizinische Hochschule Hannover
Karl-Wiechert-Allee 3
30625 Hannover

Tel.: +49 511 5325295
E-Mail: michael.marschollek@plri.de

RISK PRINCIPE – Risk prediction for infection control and treatment in hospitals

Two complexes of tasks are to be implemented:

Partners:

Management team: