The Project

About this project


Sepsis is a highly complex, life-threatening syndrome that develops when the bodies’ immune response to an infection causes injury to its own tissues and organs. The course and outcome of sepsis is highly heterogeneous and depends on complex host-pathogen interactions, the pathogen, and timing of diagnosis and effective treatment. Patients at risk for sepsis may benefit from personalized diagnostic assessment and treatment strategies. Multiple attempts have failed to develop classical biomarkers from e.g. serum to diagnose sepsis – also due to the heterogeneity of presentation. Digital biomarkers in sepsis may offer a different approach, as the collection of very large datasets allows to integrate time-series data of directly measured physiological parameters. Within an integrative approach, digital biomarkers may be combined with -omics biomarkers (e.g. metabolomics, metagenomic, whole genome sequencing of bacteria, immune phenotyping) and form hybrid biomarkers with higher sensitivity and specificity. The use of novel machine-learning based algorithms to explore such datasets may help to better understand the data and discover patterns within the data linked to particular clinical phenotypes. These algorithms may potentially allow to guide tailored personalized treatment strategies in sepsis. The aim of the project is to build a network for data-driven and -omics technology-based research to (i) recognize sepsis at an earlier stage and (ii) predict risk of mortality.

Data acquisition, processing and quality assurance:

In this driver project funded by the Swiss Personalized Health Network (SPHN) and Personalized Health Related Technologies (PHRT), each University Hospital (Basel, Bern, Geneva, Lausanne and Zurich) and University of Bern and Zurich will provide data from critically ill patients hospitalized in intensive care units (ICUs).

The locally generated data is collected from various hospital information systems (IS) such as clinical and laboratory IS, devices such as ventilators and dialysis machines, ICU monitors, and treatment-related data, e.g. drug doses and transferred and stored in local clinical data warehouses (CDWH). In order to follow Sepsis 3.0 criteria, we regularly tag patient with suspected infection and collect information in the clinical IS. This will allow to compute Sepsis-3 criteria and compare data patterns to this international accepted definition of sepsis. The data is structured and interoperable between centres following SNOMED CT and LOINC ontologies. Data is then transferred in a resource description framework (RDF) format after encoding and encryption via the BioMedIT nodes to the ETH domain for further analysis. The exchanged follows secured routes of BioMedIT to the ETH Domain for quality control and analysis. A shared data model will be used for machine-learning based analysis.

The clinical outcomes of our study include: (i) prediction of sepsis with new markers in comparison to Sepsis-3 criteria and (ii) prediction of in-hospital mortality.

Figure 1

Figure 1. SPHN/PHRT framework for the Sepsis driver project. All centers locally collect data from different primary sources in clinical data warehouses. The is high interoperable and standardized following ontologies. The exchanged follows secured routes of BioMedIT to the ETH Domain for quality control and analysis.


There are currently two prospective observational trials conducted within this project.
Study 1:

‘Digital biomarker study: includes every patient admitted to an ICU. Following specific inclusion/exclusion criteria, we include sepsis (cases) and non-sepsis (controls) upon entry of the ICU. Of all patients with an ICU admission also available pre/post-ICU data will be integrated into the data analysis. We will compare data patterns between cases and controls in regards of the previously defined outcomes. For this study we have obtained ethical approval to collect data from 15,000 ICU patients from participating centres over the next few years. In addition, a data transfer and use agreement as well as a consortium agreement have been generated. The legal process to allow data sharing was complex and involved a multiple round assessment and discussion of the legal framework with various stakeholders.

A list of clinical, physiological and laboratory variables, covering more than 500 attributes of interest was generated and linked to ontologies such as time points, application route and dosage of specific drugs, etc.

At February 2021 we have collected data from almost 11’140 patients from the participating ICUs. Data transfer and exchange protocols have been set up with all CDWH, BioMedIT, and ETH domain teams and continuously improved.
Study 2:

‘-omics biomarker study: includes a subgroup of the patients from the ‘digital biomarker project’. In all five hospitals a total of 300 patients with severe community acquired pneumonia (cases) and 100 patients with a severe systemic inflammation (controls) will be included. We will collect additional patient material such as serum, whole blood for DNA and RNA, respiratory and stool material, immune cells and bacterial isolates. These samples will be in-depth characterized using metabolomics, metagenomics, whole genome sequencing and immune phenotyping technologies. Digital and -omics data will then be merged and analysed to form hybrid biomarkers. Again, case and control patients will be compared in regards of the previously defined outcomes.

This study has not yet started. The ethical proposal has been submitted and decision is pending. Study preparation are progressing at all centers. In regards of the sample management different biobank information management software (BIMS) systems have been established in the various centers.