We work in interdisciplinary fashion in the fields of epidemiology, genetics, microbiology, molecular biology, intensive care, statistics, economics, bioinformatics and innovation. We collaborate closely with hospitals and use data from one of the world's largest sepsis registries to find solutions for diagnosis, treatment and follow-up of sepsis patients. This is translational research – "from bed to bench and back again" – that will benefit patients. You can read more about our four main research areas below.

Clinical research and innovation

One challenge with sepsis is that patients respond very differently. The symptoms can be different; how fast the syndrome develops and how severe it is varies from patient to patient. We use clinical data to find subgroups of patients who are at differing risk of sepsis and who may respond differently to treatment. Several clinical trials have tested new and promising treatments for sepsis, but to date none has resulted in new treatment.

Clinical data such as images (X-ray and CT), blood tests and urine production are important for diagnosing sepsis, but they are not adequate to accurately stratify patients. The fall of 2019 saw the launch of a new biobank for collecting samples for omics analysis. We will investigate the genome, transcriptome, metabolome and microbiome of ICU patients in Norwegian hospitals. We will use this data to identify subgroups of sepsis patients, which will be useful in future diagnostics.
In collaboration with Cimon Medical AS, we are testing whether measuring microcirculation with ultrasound can detect sepsis at an early stage. Diagnosing sepsis early and accurately is crucial for successful treatment. Currently no diagnostic tools for early detection of sepsis are available, but changes in microcirculation are an early signal that the patient is developing sepsis. With a new, non-invasive ultrasound probe and adaptive microcirculatory signal analysis, we can detect sepsis early. This technology enables patients to be treated with antibiotics sooner and thus prevents severe sepsis and late effects.

Epidemiology

Riskfactors

The risk of getting serious infections or sepsis varies based on genetics and lifestyle factors. We use the world's largest genotype cohort for sepsis, followed over 25 years, to find risk factors for sepsis. Among other things, we have shown that smoking, overweight and low iron levels increase the risk of sepsis.

Genetics

It is well known that genetics play an important role in diseases such as cancer. But our genes actually play a greater role in the risk of getting a serious infection or of developing sepsis. We therefore use genome-wide association studies (called GWAS) to find mutations that increase the risk of sepsis. We then incorporate this information into functional and clinical studies.

Microbiology

Sepsis is often seen only as the immune system's overreaction to infection. Research into the role of microbes in sepsis development been undervalued. We work with material from the Mid-Norway Sepsis Register, which has 5000 clinical isolates of microbes from patients with positive blood culture. The register ranks among the world's largest sepsis collections of clinical and genetic information, as well as of bacterial isolates. We link genetic and functional characteristics from the microbe and the patient. This provides us with new knowledge about the interaction between humans and microbes in the fight against sepsis.

Bioinformatics and artificial intelligence

-

Machine learning and artificial intelligence

We use statistical analysis and machine learning on data from the Nord-Trøndelag Health Study (HUNT) to understand genetic predisposition to developing sepsis. We want to understand if there are differences in the genome between people who have had sepsis and those who have not. This is difficult to study due to the many areas in the genome that affect the risk of sepsis and interact with each other. In addition, we do not know in advance where to start looking. We use statistics and machine learning to find the hidden pattern of genes and the interactions between them.