This multi-center study is to focus on patients with sepsis in Intensive Care Units (ICUs) in order to better understand the complex host-pathogen interaction and clinical heterogeneity associated with sepsis. Understanding this heterogeneity may allow the development of novel diagnostic approaches. Data from patients will be analyzed using state-of-the art analytical algorithms for biomarker discovery including machine learning and multidimensional mathematical modelling to explore the large datasets generated. In order to discover digital biomarkers for the study endpoints a case-control study design will be used to compare data patterns from patients with sepsis (cases) and those without sepsis (controls).
Study Type
OBSERVATIONAL
Enrollment
17,500
compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to reliably determine sepsis
compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to to predict sepsis-related mortality
Clinical Microbiology, University Hospital Basel
Basel, Switzerland
RECRUITINGInfectious Diseases and Hospital Epidemiology, University Hospital Basel
Basel, Switzerland
RECRUITINGMedical Intensive Care Unit; University Hospital Basel
Basel, Switzerland
RECRUITINGSurgical Intensive Care Unit, University Hospital Basel
Basel, Switzerland
RECRUITINGInstitute for Infectious Diseases, University of Bern
Bern, Switzerland
RECRUITINGDivision Infectious Diseases, University Hospital Bern
Bern, Switzerland
RECRUITINGIntensive Care Medicine, University Hospital Bern
Bern, Switzerland
RECRUITINGDivision Bacteriology Laboratory, University Hospital Geneva
Geneva, Switzerland
RECRUITINGDivision Infectious Diseases, University Hospital Geneva
Geneva, Switzerland
RECRUITINGIntensive Care Medicine, University Hospital Geneva
Geneva, Switzerland
RECRUITING...and 6 more locations
sepsis-related mortality (sensitivity)
Algorithm to predict sepsis-related mortality (sensitivity)
Time frame: time- series data collected from hospital entry until maximum 12 months after hospital exit (no exact time point specified)
sepsis-related mortality (specificity)
Algorithm to predict sepsis-related mortality (specificity)
Time frame: time- series data collected from hospital entry until maximum 12 months after hospital exit (no exact time point specified)
Determination of sepsis
Algorithm to determine sepsis at an early stage (at least 12 hours before classical definitions)
Time frame: time- series data collected from hospital entry until hospital exit; an average of 1 month (no exact time point specified)
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