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Recruiting NCT04130789

Personalized Swiss Sepsis Study

— Observational Sepsis

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: compare data patterns by data-driven algorithms to determine sepsis, compare data patterns by data-driven algorithms to predict sepsis-related mortality.
Who it may be relevant to
Registry conditions: Sepsis. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Switzerland
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

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

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).

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

Primary outcome 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)]
  • 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 [Time frame: time- series data collected from hospital entry until hospital exit; an average of 1 month (no exact time point specified)]

Eligibility criteria

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Case-control

Study locations

Switzerland · 16 centers
  • Clinical Microbiology, University Hospital Basel — Basel
  • Infectious Diseases and Hospital Epidemiology, University Hospital Basel — Basel
  • Medical Intensive Care Unit; University Hospital Basel — Basel
  • Surgical Intensive Care Unit, University Hospital Basel — Basel
  • Institute for Infectious Diseases, University of Bern — Bern
  • Division Infectious Diseases, University Hospital Bern — Bern
  • Intensive Care Medicine, University Hospital Bern — Bern
  • Division Bacteriology Laboratory, University Hospital Geneva — Geneva
  • … and 8 more centers

Identifiers

NCT: NCT04130789 · 2019-01088; qu18Egli2

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗