SepsisPSSS_digitalNCT04130789
Personalized Swiss Sepsis Study
An observational study of Sepsis, run by University Hospital, Basel, Switzerland at 16 sites in Switzerland.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Sepsis
- What is being tested?
- compare data patterns by data-driven algorithms to determine sepsis and compare data patterns by data-driven algorithms to predict sepsis-related mortality
- How many people
- 17,500 participants
- Where
- 16 sitesSwitzerland
- Study dates
- 15 Nov 2019to Dec 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: ICU patients on a Swiss University Hospital ICU from 01.08.2019
Who may be able to join
- Patients admitted to an ICU on a Swiss University Hospital.
- Patients expected to stay at least 24h on the ICU
- Present at admission to ICU or subsequent development of sepsis 3.0 criteria
- Patients not fulfilling sepsis definition during the ICU stay
Who cannot join
- Decline of general consent or any other negative statement against using data for research.
- Patients with a clear elective stay on the ICUs.
Trial locations and contacts
16 sites in 1 country. Contact the study team or a site near you to ask about taking part.
16 sites in 1 country
Switzerland16 sites, 16 recruiting
- Recruiting
Clinical Microbiology, University Hospital Basel
Basel, 4031
Adrian Egli, PD Dr. med, +41 61 556 5749, adrian.egli@usb.ch
- Recruiting
Infectious Diseases and Hospital Epidemiology, University Hospital Basel
Basel, 4031
Manuel Battegay, Prof Dr med
- Recruiting
Medical Intensive Care Unit; University Hospital Basel
Basel, 4031
Stephan Marsch, Prof. Dr. MD
- Recruiting
Surgical Intensive Care Unit, University Hospital Basel
Basel, 4031
Martin Siegemund, Prof. Dr. MD
- Recruiting
Institute for Infectious Diseases, University of Bern
Bern, 3001
Stephen Leib, Prof. Dr. MD
- Recruiting
Division Infectious Diseases, University Hospital Bern
Bern, 3010
Hansjakob Furrer, Prof.Dr.med
- Recruiting
Intensive Care Medicine, University Hospital Bern
Bern, 3010
Stephan Jakob, Prof. Dr. MD
- Recruiting
Division Bacteriology Laboratory, University Hospital Geneva
Geneva, 1205
Jacques Schrenzel, Prof Dr med
- Recruiting
Division Infectious Diseases, University Hospital Geneva
Geneva, 1205
Laurent Kaiser, Prof. Dr. MD
- Recruiting
Intensive Care Medicine, University Hospital Geneva
Geneva, 1205
Jérôme Pugin, Prof. Dr. MD
- Recruiting
Division Intensive Care Medicine, University Hospital Lausanne
Lausanne, 1011
Philippe Eckert, Prof. Dr. MD
- Recruiting
Institute of Microbiology, University Hospital Lausanne
Lausanne, 1011
Gilbert Greub, Prof. Dr. MD
- Recruiting
Service Infectious Diseases, University Hospital Lausanne
Lausanne, 1011
Thierry Calandra, Prof. Dr. MD
- Not yet recruiting
Institute for Medical Microbiology, University Hospital Zurich
Zurich, 8006
Reinhard Zbinden, Prof. Dr. MD
- Not yet recruiting
Division Infectious Diseases, University Hospital Zurich
Zurich, 8091
Annelies Zinkernagel, Prof. Dr. MD
- Not yet recruiting
Institute for Intensive Medicine, University Hospital Zurich
Zurich, 8091
Reto Schüpbach, Prof. Dr. MD
About this study
Official title: 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).
- Conditions
- Sepsis
- Being tested
- compare data patterns by data-driven algorithms to determine sepsis, compare data patterns by data-driven algorithms to predict sepsis-related mortality
- Keywords
- clinical data warehouse (CDWH), machine learning, data-driven algorithms, multi-dimensional modelling
- Medical subject headings
- Sepsis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
compare data patterns by data-driven algorithms to determine sepsisOther
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 mortalityOther
compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to to predict sepsis-related mortality
| Study group | Role | What they receive |
|---|---|---|
| patients with sepsis (cases) | patients who developed or were admitted with sepsis to the ICU (cases) Receives: Other: compare data patterns by data-driven algorithms to determine sepsis; Other: compare data patterns by data-driven algorithms to predict sepsis-related mortality | |
| patients without sepsis (controls) | patients who did not develop sepsis (controls). Receives: Other: compare data patterns by data-driven algorithms to determine sepsis; Other: compare data patterns by data-driven algorithms to predict sepsis-related mortality |
What is being measured
The primary outcome is the main question the study is designed to answer.
sepsis-related mortality (sensitivity)
Algorithm to predict sepsis-related mortality (sensitivity)
Measured: time- series data collected from hospital entry until maximum 12 months after hospital exit (no exact time point specified)
sepsis-related mortality (specificity)
Algorithm to predict sepsis-related mortality (specificity)
Measured: time- series data collected from hospital entry until maximum 12 months after hospital exit (no exact time point specified)
Determination of sepsis
Algorithm to determine sepsis at an early stage (at least 12 hours before classical definitions)
Measured: time- series data collected from hospital entry until hospital exit; an average of 1 month (no exact time point specified)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Oct 2019
- Study start15 Nov 2019
- Primary completion31 May 2022
- Study completionDec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SepsisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 17,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University Hospital, Basel, Switzerland
- Sponsor type
- University, hospital or other
- Collaborators
- Swiss Personalized Health Network (SPHN), Personalized Health and Related Technologies (PHRT) initiative of ETH Zürich
- Responsible party
- Sponsor
- Investigators
- Adrian Egli, PD Dr., Principal investigator, Clinical Microbiology, University Hospital Basel
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04130789
- Sponsor's protocol ID
- 2019-01088; qu18Egli2
- Other IDs
- 2019
- Acronym
- PSSS_digital
- Status last verified
- Feb 2025
- First submitted
- 15 Oct 2019
- Last updated
- 5 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Personalized Swiss Sepsis Study: With Machine Learning and Computational Modelling Towards Personalized Sepsis Management - Discovery of Digital Biomarkers
- ClinicalTrials.gov ID
- NCT04130789
- Acronym / sponsor ID
- PSSS_digital / 2019-01088; qu18Egli2
- Conditions
- Sepsis
- Keywords
- clinical data warehouse (CDWH), machine learning, data-driven algorithms, multi-dimensional modelling
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 17,500 participants (estimated)
- Lead sponsor
- University Hospital, Basel, SwitzerlandUniversity, hospital or other
- Collaborators
- Swiss Personalized Health Network (SPHN), Personalized Health and Related Technologies (PHRT) initiative of ETH Zürich
- Study start
- 15 Nov 2019 (actual)
- Primary completion
- 31 May 2022 (actual)
- Study completion
- Dec 2025 (estimated)
- First posted
- 17 Oct 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| patients with sepsis (cases) | – | Other: compare data patterns by data-driven algorithms to determine sepsis, Other: compare data patterns by data-driven algorithms to predict sepsis-related mortalitypatients who developed or were admitted with sepsis to the ICU (cases) |
| patients without sepsis (controls) | – | Other: compare data patterns by data-driven algorithms to determine sepsis, Other: compare data patterns by data-driven algorithms to predict sepsis-related mortalitypatients who did not develop sepsis (controls). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- ICU patients on a Swiss University Hospital ICU from 01.08.2019
Key inclusion criteria (4 listed)
- Patients admitted to an ICU on a Swiss University Hospital.
- Patients expected to stay at least 24h on the ICU
- Present at admission to ICU or subsequent development of sepsis 3.0 criteria
- Patients not fulfilling sepsis definition during the ICU stay
Key exclusion criteria (2 listed)
- Decline of general consent or any other negative statement against using data for research.
- Patients with a clear elective stay on the ICUs.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| compare data patterns by data-driven algorithms to determine sepsiscompare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to reliably determine sepsis | Other | patients with sepsis (cases), patients without sepsis (controls) |
| compare data patterns by data-driven algorithms to predict sepsis-related mortalitycompare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to to predict sepsis-related mortality | Other | patients with sepsis (cases), patients without sepsis (controls) |
Primary outcome
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)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Mar 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Adrian Egli, PD Dr. principal investigator
Clinical Microbiology, University Hospital Basel
16 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sepsis, run by University Hospital, Basel, Switzerland at 16 sites in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 5 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 16 sites in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Adrian Egli, PD Dr. (+41 61 556 5749, adrian.egli@usb.ch), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Mar 2025.