Personalized Swiss Sepsis Study

Sepsis Clinical Trial

— PSSS_digital  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04130789
• Other study ID #: 2018-01088; qu18Egli2
• Status: Recruiting
• Start date: November 15, 2019
• Est. completion date: June 2023

Study information

• Verified date: August 2021
• Source: University Hospital, Basel, Switzerland
• Contact: Adrian Egli, PD Dr.
• Phone: +41 61 556 5749
• Email: adrian.egli[@]usb.ch
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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).

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 17500
• Est. completion date: June 2023
• Est. primary completion date: June 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Sepsis
• Toxemia

Intervention

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
• 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

Locations

Country	Name	City	State
Switzerland	Clinical Microbiology, University Hospital Basel	Basel
Switzerland	Infectious Diseases and Hospital Epidemiology, University Hospital Basel	Basel
Switzerland	Medical Intensive Care Unit; University Hospital Basel	Basel
Switzerland	Surgical Intensive Care Unit, University Hospital Basel	Basel
Switzerland	Division Infectious Diseases, University Hospital Bern	Bern
Switzerland	Institute for Infectious Diseases, University of Bern	Bern
Switzerland	Intensive Care Medicine, University Hospital Bern	Bern
Switzerland	Division Bacteriology Laboratory, University Hospital Geneva	Geneva
Switzerland	Division Infectious Diseases, University Hospital Geneva	Geneva
Switzerland	Intensive Care Medicine, University Hospital Geneva	Geneva
Switzerland	Division Intensive Care Medicine, University Hospital Lausanne	Lausanne
Switzerland	Institute of Microbiology, University Hospital Lausanne	Lausanne
Switzerland	Service Infectious Diseases, University Hospital Lausanne	Lausanne
Switzerland	Division Infectious Diseases, University Hospital Zurich	Zürich
Switzerland	Institute for Intensive Medicine, University Hospital Zurich	Zürich
Switzerland	Institute for Medical Microbiology, University Hospital Zurich	Zürich

Sponsors (3)

Lead Sponsor	Collaborator
University Hospital, Basel, Switzerland	Personalized Health and Related Technologies (PHRT) initiative of ETH Zürich, Swiss Personalized Health Network (SPHN)

Country where clinical trial is conducted

Switzerland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	sepsis-related mortality (sensitivity)	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)
Primary	sepsis-related mortality (specificity)	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)
Primary	Determination of sepsis	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)