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NCT Number: NCT04130789

Personalized Swiss Sepsis Study

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).

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Key information

Conditions

Sepsis Infections Inflammation Pathologic Processes Pathological Conditions, Signs and Symptoms Systemic Inflammatory Response Syndrome

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Clinical Microbiology, University Hospital Basel, Basel, Switzerland

Loading trial locations.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

compare data patterns by data-driven algorithms to determine sepsis

Other

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

Other

compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to to predict sepsis-related mortality

Primary outcomes

  1. 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)

  2. 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)

  3. 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)

Study contacts

Contact information is provided by the study sponsor or research team.

Adrian Egli, PD Dr.

CONTACT

[email protected]

+41 61 556 5749

Sponsors and collaborators

Lead sponsor

University Hospital, Basel, Switzerland

Other

Collaborators

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

Registry information

Official study title

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

Acronym: PSSS_digital

Important dates

Study start
2019
Primary completion
2022
Study completion
2025
First posted
Oct 17, 2019
Registry last updated
Mar 5, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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