NCT06853301

Machine Learning Assisted Electrochemical Profiling to Provide Early Identification of Bloodstream Infections Pathogens

Towards a Smart Blood Culture Bottle: Machine Learning Assisted Electrochemical Profiling to Provide Early In-situ Identification of Bloodstream Infections Pathogens

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 200 (estimated)
• Sponsor: University Hospital, Grenoble · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Bacteremia Sepsis

Interventions

Type	Name	Description
OTHER	Blood culture sampling	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

Timeline

• Start date: 2025-04-01
• Primary completion: 2025-04-01
• Completion: 2026-08-01
• First posted: 2025-02-28
• Last updated: 2025-03-26

Locations

2 sites across 1 country: France