Early Identification of Bloodstream Infection Bacteria Using Machine Learning and Electrochemical Profiling of Blood Culture Bottles

What this Trial Is About

Researchers are investigating a new way to identify bacteria that cause bloodstream infections, specifically bacteremia. The study focuses on improving the early stages of the blood culture process, from collecting blood to detecting bacterial growth in culture bottles. By using machine learning to analyze the electrochemical signals of bacteria as they grow, the study aims to speed up pathogen identification and provide quicker, useful results for treating infections. Participants will have blood samples taken as part of their standard care. In addition to the usual blood culture bottle, two to four extra bottles will be collected and spiked with known bacteria to create an electrochemical profile database. This process will help develop a system that can recognize bacteria based on their electrical signals while still inside the culture bottle. During the study, researchers will monitor these blood culture bottles, measuring their electrochemical fingerprints up to one week after sampling. The main outcome is to collect a list of these profiles. The study will also assess how well the identification method works by the end of the research period. The total participation duration is linked to the time needed for these electrochemical measurements and performance evaluations.

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