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

Condition(s) studied

Investigational drug(s) / intervention(s)

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

Study 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.

Eligibility

Primary outcome measure(s)

• 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

Trial sites (2)

More University Hospital, Grenoble trials in France