Personalized Swiss Sepsis Study

Study Overview
=================
Brief 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).

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

Conditions
-----------------
Sepsis

Intervention / Treatment
-----------------
* Other: compare data patterns by data-driven algorithms to determine sepsis
* Other: compare data patterns by data-driven algorithms to predict sepsis-related mortality


Participation Criteria
=================
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.

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| patients with sepsis (cases)<br>patients who developed or were admitted with sepsis to the ICU (cases) | Other: compare data patterns by data-driven algorithms to determine sepsis<br>* compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to reliably determine sepsis<br>Other: compare data patterns by data-driven algorithms to predict sepsis-related mortality<br>* compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to to predict sepsis-related mortality<br>|
| patients without sepsis (controls)<br>patients who did not develop sepsis (controls). | Other: compare data patterns by data-driven algorithms to determine sepsis<br>* compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to reliably determine sepsis<br>Other: compare data patterns by data-driven algorithms to predict sepsis-related mortality<br>* compare data patterns by data-driven algorithms including machine learning and multi-dimensional modelling to to predict sepsis-related mortality<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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) | 
| 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) | 
| 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) | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
clinical data warehouse (CDWH), machine learning, data-driven algorithms, multi-dimensional modelling