Prospective Validation of GRADY: A Machine Learning Model for Early Sepsis and Bacteremia Detection in ICU Patients
Recruiting now
Conditions studied: Bacteremia, Sepsis Bacterial
In brief
This study aims to prospectively validate the GRADY prediction models, which use machine learning algorithms to estimate the risk of gram-negative bacteremia and sepsis in intensive care unit (ICU) patients based on routinely collected vital signs and laboratory data. Sepsis, a life-threatening condition associated with high ICU mortality, requires early diagnosis and treatment-yet current diagnostic methods relying on blood cultures are time-consuming. Existing scoring systems such as SOFA, SIRS, and NEWS2 often lack sufficient sensitivity and specificity in early sepsis detection. Unlike traditional tools, the GRADY models seek to provide earlier and more accurate risk stratification. This study will compare the clinical performance of GRADY models against standard scoring systems and explore their integration as early warning tools to support rapid intervention and improve outcomes in critical care.
Key facts
- Study ID
- NCT07126106
- Run by
- Sisli Hamidiye Etfal Training and Research Hospital
- People needed
- 55
- Starts
- 2025-02-01
- Expected to finish
- 2026-01-01
- Last updated by the study team
- 2025-08-17
Who can join
Age: 18 and older. Sex: any. Healthy volunteers: not accepted.
You may qualify if…
- Patients aged 18 years or older
- ICU stay of 48 hours or longer
- Patients from whom blood cultures were obtained during routine monitoring
- Signed informed consent form
You may not qualify if…
- Patients younger than 18 years
- ICU stay shorter than 48 hours
- Patients without blood cultures
Where it is running
- Sisli etfal research and training hospital — Seyrantepe, Istanbul, Turkey (Türkiye) (enrolling)
Full record on ClinicalTrials.gov
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