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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05383963
Other study ID # QUICK-SEPSIS
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date July 15, 2022
Est. completion date December 2027

Study information

Verified date January 2024
Source Charite University, Berlin, Germany
Contact Claudia Spies, MD, Prof.
Phone +49 30 450 55 11 02
Email claudia.spies@charite.de
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Retrospective observational study to develop a Machine Learning Algorithm to evaluate parameters collected from routine data for the diagnosis of sepsis and septic shock and their influence on time to diagnosis and patient outcome.


Description:

Retrospective routine data from the medical records of the department of anesthesiology and operative intensive care from 01. 01. 2007 to 31. 12. 2021 are analyzed in digital form. 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)


Recruitment information / eligibility

Status Recruiting
Enrollment 10000
Est. completion date December 2027
Est. primary completion date December 2024
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - age >= 18 years - ICU stay of > 24 hours Exclusion Criteria: - none

Study Design


Related Conditions & MeSH terms


Locations

Country Name City State
Germany Department of Anesthesiology and Operative Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin Berlin

Sponsors (1)

Lead Sponsor Collaborator
Charite University, Berlin, Germany

Country where clinical trial is conducted

Germany, 

Outcome

Type Measure Description Time frame Safety issue
Primary Sepsis/septic shock Development of a machine learning algorithm (MLA) for the prediction of sepsis/septic shock from hospital routine data. 01.01.2007 -31.12.2021
Secondary Predictive accuracy Evaluation of the predictive accuracy (= predictive value) of the respective sepsis diagnostic algorithm (i.e. comparison of the concepts SIRS and Sepsis-3) 01.01.2007 -31.12.2021
Secondary Diagnostic accuracy Identification of additional variables for diagnostic accuracy (laboratory values, clinical parameters and vital-sign monitor parameters and other relevant health data 01.01.2007 -31.12.2021
Secondary Performance indicators Evaluation of performance indicators of clinical routine processes (Intensive care quality indicators) 01.01.2007 -31.12.2021
Secondary Case costs Case costs related to hospitalization 01.01.2007 -31.12.2021
Secondary Revenues Revenues related to hospitalization 01.01.2007 -31.12.2021
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