Evaluation of Parameters Collected From Routine Data for the Diagnosis of Sepsis and Septic Shock and Their Influence on Time to Diagnosis and Patient Outcome
NCT ID: NCT05383963
Last Updated: 2025-12-01
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
10000 participants
OBSERVATIONAL
2022-07-15
2027-12-31
Brief Summary
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Detailed Description
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The first step is the development of a machine learning algorithm (MLA). This MLA will be validated and analyzed for his predictive value with regard to early diagnosis of sepsis/septic shock depending on the conceptual value of detection variables (Sepsis-3 vs. SIRS). Further analysis will focus on improvement of accuracy for the MLA and the effect of these detection variables on quality of treatment processes and also on economic consequences like cost and revenue.
Timeline:
1. Conception and development of the ML Algorithm (6 months)
2. Identification and diagnostic validation of sepsis patients (6 months)
3. Secondary analyses (36 months)
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
* ICU stay of \> 24 hours
Exclusion Criteria
18 Years
ALL
No
Sponsors
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Charite University, Berlin, Germany
OTHER
Responsible Party
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Claudia Spies
Head of the Department of Anesthesiology and Operative Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin
Principal Investigators
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Claudia Spies, MD, Prof.
Role: STUDY_DIRECTOR
Charite University, Berlin, Germany
Locations
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Department of Anesthesiology and Operative Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin
Berlin, , Germany
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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QUICK-SEPSIS
Identifier Type: -
Identifier Source: org_study_id
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