Personalized Swiss Sepsis Study

Overview

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).

Full Title of Study: “Personalized Swiss Sepsis Study: With Machine Learning and Computational Modelling Towards Personalized Sepsis Management – Discovery of Digital Biomarkers”

Study Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: June 2022

Interventions

  • Other: compare data patterns by data-driven algorithms to determine sepsis
    • compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to reliably determine sepsis
  • Other: compare data patterns by data-driven algorithms to predict sepsis-related mortality
    • compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to to predict sepsis-related mortality

Arms, Groups and Cohorts

  • patients with sepsis (cases)
    • patients who developed or were admitted with sepsis to the ICU (cases)
  • patients without sepsis (controls)
    • patients who did not develop sepsis (controls).

Clinical Trial Outcome Measures

Primary Measures

  • 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)
    • Algorithm to predict sepsis-related mortality (sensitivity)
  • 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)
    • Algorithm to predict sepsis-related mortality (specificity)
  • Determination of sepsis
    • Time Frame: time- series data collected from hospital entry until hospital exit; an average of 1 month (no exact time point specified)
    • Algorithm to determine sepsis at an early stage (at least 12 hours before classical definitions)

Participating in This Clinical Trial

Inclusion Criteria

  • Patients admitted to an ICU on a Swiss University Hospital. – Patients expected to stay at least 24h on the ICU Inclusion Criteria (cases) – Present at admission to ICU or subsequent development of sepsis 3.0 criteria Inclusion Criteria (controls) – Patients not fulfilling sepsis definition during the ICU stay Exclusion Criteria:

  • Decline of general consent or any other negative statement against using data for research. – Patients with a clear elective stay on the ICUs.

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • University Hospital, Basel, Switzerland
  • Collaborator
    • Swiss Personalized Health Network (SPHN)
  • Provider of Information About this Clinical Study
    • Sponsor
  • Overall Official(s)
    • Adrian Egli, PD Dr., Principal Investigator, Clinical Microbiology, University Hospital Basel
  • Overall Contact(s)
    • Adrian Egli, PD Dr., +41 61 556 5749, adrian.egli@usb.ch

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