Personalized Swiss Sepsis Study (PSSS_digital)

February 28, 2025 updated by: University Hospital, Basel, Switzerland

Personalized Swiss Sepsis Study: With Machine Learning and Computational Modelling Towards Personalized Sepsis Management - Discovery of Digital Biomarkers

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 Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

17500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Switzerland
      • Basel, Switzerland, 4031
        • Recruiting
        • Clinical Microbiology, University Hospital Basel
        • Contact:
      • Basel, Switzerland, 4031
        • Recruiting
        • Infectious Diseases and Hospital Epidemiology, University Hospital Basel
        • Contact:
          • Manuel Battegay, Prof Dr med
      • Basel, Switzerland, 4031
        • Recruiting
        • Medical Intensive Care Unit; University Hospital Basel
        • Contact:
          • Stephan Marsch, Prof. Dr. MD
      • Basel, Switzerland, 4031
        • Recruiting
        • Surgical Intensive Care Unit, University Hospital Basel
        • Contact:
          • Martin Siegemund, Prof. Dr. MD
      • Bern, Switzerland, 3001
        • Recruiting
        • Institute for Infectious Diseases, University of Bern
        • Contact:
          • Stephen Leib, Prof. Dr. MD
      • Bern, Switzerland, 3010
        • Recruiting
        • Division Infectious Diseases, University Hospital Bern
        • Contact:
          • Hansjakob Furrer, Prof.Dr.med
      • Bern, Switzerland, 3010
        • Recruiting
        • Intensive Care Medicine, University Hospital Bern
        • Contact:
          • Stephan Jakob, Prof. Dr. MD
      • Geneva, Switzerland, 1205
        • Recruiting
        • Division Bacteriology Laboratory, University Hospital Geneva
        • Contact:
          • Jacques Schrenzel, Prof Dr med
      • Geneva, Switzerland, 1205
        • Recruiting
        • Division Infectious Diseases, University Hospital Geneva
        • Contact:
          • Laurent Kaiser, Prof. Dr. MD
      • Geneva, Switzerland, 1205
        • Recruiting
        • Intensive Care Medicine, University Hospital Geneva
        • Contact:
          • Jérôme Pugin, Prof. Dr. MD
      • Lausanne, Switzerland, 1011
        • Recruiting
        • Division Intensive Care Medicine, University Hospital Lausanne
        • Contact:
          • Philippe Eckert, Prof. Dr. MD
      • Lausanne, Switzerland, 1011
        • Recruiting
        • Institute of Microbiology, University Hospital Lausanne
        • Contact:
          • Gilbert Greub, Prof. Dr. MD
      • Lausanne, Switzerland, 1011
        • Recruiting
        • Service Infectious Diseases, University Hospital Lausanne
        • Contact:
          • Thierry Calandra, Prof. Dr. MD
      • Zürich, Switzerland, 8006
        • Not yet recruiting
        • Institute for Medical Microbiology, University Hospital Zurich
        • Contact:
          • Reinhard Zbinden, Prof. Dr. MD
      • Zürich, Switzerland, 8091
        • Not yet recruiting
        • Division Infectious Diseases, University Hospital Zurich
        • Contact:
          • Annelies Zinkernagel, Prof. Dr. MD
      • Zürich, Switzerland, 8091
        • Not yet recruiting
        • Institute for Intensive Medicine, University Hospital Zurich
        • Contact:
          • Reto Schüpbach, Prof. Dr. MD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

ICU patients on a Swiss University Hospital ICU from 01.08.2019

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
patients with sepsis (cases)
patients who developed or were admitted with sepsis to the ICU (cases)
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
patients without sepsis (controls)
patients who did not develop sepsis (controls).
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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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)
time- series data collected from hospital entry until maximum 12 months after hospital exit (no exact time point specified)
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)
time- series data collected from hospital entry until maximum 12 months after hospital exit (no exact time point specified)
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)
time- series data collected from hospital entry until hospital exit; an average of 1 month (no exact time point specified)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University Hospital, Basel, Switzerland

Collaborators

Swiss Personalized Health Network (SPHN)
Personalized Health and Related Technologies (PHRT) initiative of ETH Zürich

Investigators

  • Principal Investigator: Adrian Egli, PD Dr., Clinical Microbiology, University Hospital Basel

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 15, 2019

Primary Completion (Actual)

May 31, 2022

Study Completion (Estimated)

December 1, 2025

Study Registration Dates

First Submitted

October 15, 2019

First Submitted That Met QC Criteria

October 16, 2019

First Posted (Actual)

October 17, 2019

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

February 28, 2025

Last Verified

February 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2019-01088; qu18Egli2

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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