Bacteremia Sepsis
Conditions
Keywords
Rapid identification, Positive blood culture, Electrochemical profiling, Machine learning, Artificial intelligence, Bacteremia, Database
Brief summary
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.
Interventions
Patients 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
Sponsors
Study design
Eligibility
Inclusion criteria
* 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
Exclusion criteria
* 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| List of samples with an electrochemical profile | From enrollment until the end of measurment of an electrochemical fingerprint in the blood cultures from the patient spiked with bacterial strains, assessed within up to one week after blood culture sampling | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Identification performance | End of the study (18 months) | Identification performance of electrochemical profiling of the growing bacteria followed by machine learning analysis |
Countries
France