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

NCT06853301 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 200

Last updated 2026-07-22

No results posted yet for this 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.

Conditions

  • Bacteremia Sepsis

Interventions

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

Sponsors & Collaborators

  • CEA - Leti

    collaborator UNKNOWN

  • University Hospital, Grenoble

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-07-13
Primary Completion
2028-01-31
Completion
2028-01-31

Countries

  • France

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06853301 on ClinicalTrials.gov