Predictive Modeling in the Intensive Care Unit

Project Description

Central Venous Catheters (CVCs) are indispensable in the ICU (Intensive Care Unit) for administration of high risk medication and parenteral nutrition. But the use of these catheters can lead to deadly ifections in the bloodstream. Such infections constitute a high burden by increased morbidity, prolonged hospitalization, and increased health care expenditure. Even if optimal precautions (such as sterile CVC insertion, daily control of the CVC insertion site) are taken, these infections are still common, with an incidence of 1,1 per 1000 catheter days in the Netherlands. Different risk factors for development of catherters infections have been established, however it is not clearly understood which other factors may play a role. While there is debate about the most appropriate definition of a CVC infection, a straightforward registry with clear diagnostic criteria has been established in the Netherlands since 2010. Early prediction of ICU patients developing CVC infections could help healthcare professionals to take early precautionary measures such as removing the CVC before bloodstream infection occurs. It could also help to leave a CVC inserted if a low probability of CVC infection is found. It is for this purpose that we wish to develop and implement an algorithm predicting CVC infection risk in ICU patients in real-time.

People

Ruud van Kaam

Ruud van Kaam

PhD student

Intensive Care Medicine, Radboudumc

Tim Frenzel

Tim Frenzel

Intensivist

Intensive Care Medicine, Radboudumc

Jeroen Schouten

Jeroen Schouten

Intensivist

Intensive Care Medicine, Radboudumc

Marcel van Gerven

Marcel van Gerven

Professor

Artificial Intelligence, Radboud University

Hans van der Hoeven

Hans van der Hoeven

Professor

Intensive Care Medicine, Radboudumc

Luca Ambrogioni

Luca Ambrogioni

Assistant professor

Artificial Intelligence, Radboud University