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Recruitment failure
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| Name | Class |
|---|---|
| Swiss Personalized Health Network (SPHN) | UNKNOWN |
| Personalized Health and Related Technologies (PHRT) initiative of ETH Zürich | UNKNOWN |
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This multi-center observational case-control study in Intensive Care Unit (ICU) patients is to identify novel biomarkers allowing to recognize severe community acquired pneumonia (sCAP) -associated sepsis at an earlier stage and predict sepsis-related mortality. Patients with sCAP (cases) will be profoundly characterized over time regarding the development of sepsis and compared with control patients. The mechanisms and influencing factors on the clinical course will be explored with most modern -omics technologies allowing a detailed characterisation. These data will be analysed using machine learning algorithms and multi-dimensional mathematical models.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| patients with severe community acquired pneumonia (cases) | Cases: Patients with severe community acquired pneumonia with required ICU admission. |
| |
| patients without pneumonia or sepsis (controls) | Controls: Clinical phenotype of inflammation not due to suspected sepsis; patients with fever >38°C, C reactive Protein (CRP) >100mg/L, no infection focus expected in ≥ 24h. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Detection of sepsis | Sepsis detection based on new discovered digital biomarkers will be compared to classical sepsis-3 criteria (with an increase of the sequential organ failure assessment (SOFA) score of 2 or larger score points). | within 7 days after study inclusion |
| Sepsis related mortality | Prediction of sepsis related mortality (with >80% sensitivity and specificity at least 24h prior to event) | within 7 days after study inclusion |
| Time to sepsis detection (minutes after Intensive Care Unit (ICU) admission) | Time to sepsis detection (minutes after ICU admission) based on machine learning | within 7 days after study inclusion |
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Inclusion Criteria:
Exclusion Criteria:
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Adult patients fulfilling the WHO-definition of a severe community acquired pneumonia (sCAP, cases) and patients with an inflammatory phenotype (controls) requiring intensive care medicine.
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| Name | Affiliation | Role |
|---|---|---|
| Adrian Egli, PD Dr. | Clinical Microbiology, University Hospital Basel | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Clinical Bacteriology and Mycology, University Hospital Basel | Basel | 4031 | Switzerland | |||
| Infectious Diseases and Hospital Epidemiology, University Hospital Basel |
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All samples will be stored in a biobank on sepsis. In particular serum and DNA samples of hosts will be stored for at least 10 years to answer subsequent research questions.
| 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 |
|
| Basel |
| 4031 |
| Switzerland |
| Intensive Care Unit; University Hospital Basel | Basel | 4031 | Switzerland |
| Institute for Infectious Diseases, University of Bern | Bern | 3001 | Switzerland |
| Infectious Diseases and Hospital Epidemiology, University Hospital Bern | Bern | 3010 | Switzerland |
| Intensive Care Unit, University Hospital Bern | Bern | 3010 | Switzerland |
| Clinical Bacteriology, University Hospital Geneva | Geneva | 1205 | Switzerland |
| Infectious Diseases and Hospital Epidemiology, University Hospital Geneva | Geneva | 1205 | Switzerland |
| Intensive Care Unit, University Hospital Geneva | Geneva | 1205 | Switzerland |
| Clinical Microbiology, University Hospital Lausanne | Lausanne | 1011 | Switzerland |
| Infectious Diseases and Hospital Epidemiology , University Hospital Lausanne | Lausanne | 1011 | Switzerland |
| Intensive Care Unit, University Hospital Lausanne | Lausanne | 1011 | Switzerland |
| Infectious Diseases and Hospital Epidemiology, University Hospital Zurich | Zurich | 8091 | Switzerland |
| Institute for Medical Microbiology, University Hospital Zurich | Zurich | 8091 | Switzerland |
| Intensive Care Unit, University Hospital Zurich | Zurich | 8091 | Switzerland |
| ID | Term |
|---|---|
| D018805 | Sepsis |
| D000098968 | Community-Acquired Pneumonia |
| D001424 | Bacterial Infections |
| ID | Term |
|---|---|
| D007239 | Infections |
| D018746 | Systemic Inflammatory Response Syndrome |
| D007249 | Inflammation |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D017714 | Community-Acquired Infections |
| D011014 | Pneumonia |
| D012141 | Respiratory Tract Infections |
| D012140 | Respiratory Tract Diseases |
| D001423 | Bacterial Infections and Mycoses |
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