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NCT Number: NCT06853301

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

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.

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Key information

Conditions

Bacteremia Sepsis Bacteremia Bacterial Infections Bacterial Infections and Mycoses Infections Inflammation Pathologic Processes Pathological Conditions, Signs and Symptoms Sepsis Systemic Inflammatory Response Syndrome

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Grenoble University Hospital, Grenoble, France

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Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Blood culture sampling

Other

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

Primary outcomes

  1. List of samples with an electrochemical profile

    Time frame: 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 outcomes

  1. Identification performance

    Time frame: End of the study (18 months)

    Identification performance of electrochemical profiling of the growing bacteria followed by machine learning analysis

Study contacts

Contact information is provided by the study sponsor or research team.

Yvan CASPAR, PharmD, PhD

CONTACT

[email protected]

+33 476765479

Sponsors and collaborators

Lead sponsor

University Hospital, Grenoble

Other

Collaborators

  • CEA - Leti

Registry information

Official study title

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

Acronym: E-MOC

Important dates

Study start
2026
Primary completion
2028
Study completion
2028
First posted
Feb 28, 2025
Registry last updated
Jul 22, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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