In the context of a bacteremia, although significant progress has been made in speeding up pathogen identification once a blood culture bottle turns positive, few cost-effective solutions have been proposed to improve the earlier stages of the process-specifically, from blood collection to bottle positivity. The investigators propose that transport time could be leveraged to grow and identify bacteria, enabling faster access to actionable results through innovative technologies. This project aims to develop a bacterial identification database by analyzing the electrochemical profile of bacteria growing within the blood culture bottle, using machine learning.
patient requiring a blood culture sample as standard of care procedure
body weight > 50 Kg
Patient for whom the collection of 2 to 4 additional blood culture bottles is feasible, depending on venous access
patient who has not objected to participation in the project
Patient protected under the French Public Health Code (pregnant or breastfeeding women, patients under guardianship or curatorship, hospitalized under constraint, or deprived of liberty)
patients with ongoing antibiotic treatment at the time of sampling
Single group
Blood culture sampling
Other interventionPatients with blood culture sampling as standard of care. Two to four additional blood culture bottles sampled that will be spiked with known bacterial species to determine their electrochemical profiles
List of samples with an electrochemical profile
List of samples (bacterial strain and corresponding pseudonymized blood culture) for which an electrochemical profile of the growing bacteria within the blood culture bottle was successfully obtained
Identification performance
Identification performance of electrochemical profiling of the growing bacteria followed by machine learning analysis
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University Hospital, Grenoble
Lead sponsor
CEA - Leti
Collaborator
