BacteremiaOPTIBactNCT07894237
Predicting the Optimal Empirical Antibiotic Therapy for Bacteremia Using an Artificial Intelligence-based Model
An observational study of Bacteremia, run by Nantes University Hospital.
Trial summary
Bacteremia, a common infection associated with high morbidity and mortality, requires prompt and appropriate antibiotic treatment. Numerous studies have shown that the appropriate use of antibiotics helps improve the prognosis of patients, reduce the selection of pathogenic organisms, the emergence of antibiotic… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Bacteremia
- How many people
- 8,765 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2017to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: primary care clinic at Nantes hospital
Who may be able to join
- Adult patients (aged 18 or older at the time of blood collection) who had a positive blood culture at Nantes University Hospital between January 1, 2017, and December 31, 2023, except:
- Patients with a special confidentiality status (e.g., individuals in custody, under legal guardianship, etc.) or who have objected to the reuse of their data collected as part of their care.
- Patients who have not received individual notification regarding the reuse of their data collected as part of their care for research purposes, namely patients enrolled between January 1, 2017, and April 2, 2018, who were not re-admitted to Nantes University Hospital between April 3, 2018, and December 31, 2023.
Who cannot join
- Excluded are positive blood cultures
- monomicrobial or polymicrobial, positive with mycobacteria, yeasts, Nocardia spp., Legionella pneumophila
- Contaminated samples and false positives
About this study
Bacteremia, a common infection associated with high morbidity and mortality, requires prompt and appropriate antibiotic treatment. Numerous studies have shown that the appropriate use of antibiotics helps improve the prognosis of patients, reduce the selection of pathogenic organisms, the emergence of antibiotic resistance, adverse treatment effects, and healthcare costs.
The Nantes University Hospital Center has operated a Biomedical Data Warehouse (BDW) since 2018. The growing availability of data through BDWs thus enables the development of decision-support tools based on machine learning.
Read more
The primary objective of the OPTIBact project is to predict the optimal empirical antibiotic for hospitalized patients with bacteremia using a machine learning-based predictive model using data from the Nantes University Hospital's BDW.
As secondary objectives, the study aims:
- To compare the performance of the predictive model for the optimal empirical antibiotic against standard patient care
- To estimate the expected benefit of the predictive model compared to real-world practice in terms of clinical outcomes
- To predict the optimal dosage of empirical antibiotic therapy
- To compare the performance of the predictive model for optimal empirical antibiotic therapy dosing with standard patient care
- To describe inpatient patients in Nantes University Hospital who received optimal antibiotic therapy and those who did not
- To Identify predictors of antibiotic resistance in bacteria isolated from blood cultures, stratified by pathogen type.
Read the detailed description
In the OPTIBACT project, we used data extracted from the Nantes University Hospital BDW, to predict the optimal antibiotic therapy for hospitalized patients with bacteremia using a machine learning-based predictive model.
- Conditions
- Bacteremia
- Being tested
- No Intervention: Observational Cohort
- Keywords
- Bacteremia, machine-learning clinical decision support system, antibiotic
- Medical subject headings
- Bacteremia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No Intervention: Observational CohortOther
No Intervention: Observational Cohort
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the predictive model (molecule)
Accuracy of the predictive model for the empirical antibiotic and its 95% confidence interval (95% CI), estimated using the validation set. Accuracy is defined as the proportion of bacteremia episodes for which the antibiotic predicted by the model is optimal, relative to the total number of episodes in the validation set.
Measured: From descriptive analysis to the development of the predictive model: 6 months
Secondary outcomes (6)
Comparison of the performances estimated for the predictive model for optimal empirical antibiotic (molecule) with real-world estimates
Comparison of the accuracy, positive and negative predictive values, sensitivity, and specificity estimated for the predictive model for the antibiotic with real-world estimates on the validation sample (overlap of 95% CI).
Measured: From the development of the predictive model: an average of one year
Expected advantages of the predictive model compared to real-world clinical outcome
Expected advantages of the predictive model compared to real-world clinical outcome: the time , in days, between the date of the positive blood culture and the initiation of optimal empirical antibiotic therapy.
Measured: From the development of the predictive model: an average of one year
Accuracy of the predictive model (dosage)
Accuracy of the predictive model for empirical antibiotic dosage and its 95% CI, estimated using the validation sample.
Measured: From the development of the predictive model: an average of one year
Comparison of the performances of the predictive model for optimal empirical antibiotic dosing with standard patient care
Comparison of the accuracy, positive and negative predictive values, sensitivity, and specificity estimated for the dosage prediction model with real-world estimates on the validation sample (overlap of 95% CI), across the entire sample and in subgroup analyses (major pathogen types).
Measured: From the development of the predictive model: an average of one year
Description of clinical outcomes of two groups of patients who received optimal antibiotic therapy and those who did not.
The following clinical outcomes will be described for two groups of patients, who received optimal antibiotic therapy and those who did not:
- Time from the date of the positive blood culture to the initiation of optimal empirical antibiotic therapy, in days,
- admission to the intensive care unit within 72 hours,
- length of stay, in days,
- readmissions,
- in-hospital mortality and mortality at day 30,
- resistance rates of the various pathogens identified in blood cultures,
- sources of infection,
- signs of severity,
- biological pre-treatment parameters,
- Treatments received.
Measured: From the development of the predictive model: an average of one year
To identify patients and episode characteristics independently associated with resistance among patients with a positive blood culture, stratified by pathogen type.
To identify patients and episode characteristics independently associated with resistance among patients with a positive blood culture, stratified by pathogen type.
The factors studied will be prior antibiotic exposure, previous colonisation or infection with resistant bacteria, recent hospitalisation, long term care residence, hospital - VS community acquired infection, unit, age, sex.
Measured: From the development of the predictive model: an average of one year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Oct 2026
- Study start1 Jan 2017
- Primary completion31 Dec 2023
- Study completion31 Dec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for BacteremiaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 8,765 participants (actual)
Sponsor and investigators
- Lead sponsor
- Nantes University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Maëlle LE GOFF, MD, Principal investigator, Nantes University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07894237
- Sponsor's protocol ID
- RC25_0341
- Other IDs
- DAtAE_2024_8
- Acronym
- OPTIBact
- Status last verified
- Oct 2026
- First submitted
- 17 Aug 2026
- Last updated
- 26 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: No there is not a plan to make IPD available
Study at a glance
From the registry record, last updated by the sponsor on 26 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting the Optimal Empirical Antibiotic Therapy for Bacteremia Using an Artificial Intelligence-based Model
- ClinicalTrials.gov ID
- NCT07894237
- Acronym / sponsor ID
- OPTIBact / RC25_0341
- Conditions
- Bacteremia
- Keywords
- Bacteremia, machine-learning clinical decision support system, antibiotic
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 8,765 participants (actual)
- Lead sponsor
- Nantes University HospitalUniversity, hospital or other
- Study start
- 1 Jan 2017 (actual)
- Primary completion
- 31 Dec 2023 (actual)
- Study completion
- 31 Dec 2023 (actual)
- First posted
- 26 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Bacteremia, a common infection associated with high morbidity and mortality, requires prompt and appropriate antibiotic treatment. Numerous studies have shown that the appropriate use of antibiotics helps improve the prognosis of patients, reduce the selection of pathogenic organisms, the emergence of antibiotic resistance, adverse treatment effects, and healthcare costs.
The Nantes University Hospital Center has operated a Biomedical Data Warehouse (BDW) since 2018. The growing availability of data through BDWs thus enables the development of decision-support tools based on machine learning.
The primary objective of the OPTIBact project is to predict the optimal empirical antibiotic for hospitalized patients with bacteremia using a machine learning-based predictive model using data from the Nantes University Hospital's BDW.
As secondary objectives, the study aims:
- To compare the performance of the predictive model for the optimal empirical antibiotic against standard patient care
- To estimate the expected benefit of the predictive model compared to real-world practice in terms of clinical outcomes
- To predict the optimal dosage of empirical antibiotic therapy
- To compare the performance of the predictive model for optimal empirical antibiotic therapy dosing with standard patient care
- To describe inpatient patients in Nantes University Hospital who received optimal antibiotic therapy and those who did not
- To Identify predictors of antibiotic resistance in bacteria isolated from blood cultures, stratified by pathogen type.
Detailed description
In the OPTIBACT project, we used data extracted from the Nantes University Hospital BDW, to predict the optimal antibiotic therapy for hospitalized patients with bacteremia using a machine learning-based predictive model.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- primary care clinic at Nantes hospital
Key inclusion criteria (3 listed)
- Adult patients (aged 18 or older at the time of blood collection) who had a positive blood culture at Nantes University Hospital between January 1, 2017, and December 31, 2023, except:
- Patients with a special confidentiality status (e.g., individuals in custody, under legal guardianship, etc.) or who have objected to the reuse of their data collected as part of their care.
- Patients who have not received individual notification regarding the reuse of their data collected as part of their care for research purposes, namely patients enrolled between January 1, 2017, and April 2, 2018, who were not re-admitted to Nantes University Hospital between April 3, 2018, and December 31, 2023.
Key exclusion criteria (3 listed)
- Excluded are positive blood cultures
- monomicrobial or polymicrobial, positive with mycobacteria, yeasts, Nocardia spp., Legionella pneumophila
- Contaminated samples and false positives
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No Intervention: Observational CohortNo Intervention: Observational Cohort | Other | – |
Primary outcome
Accuracy of the predictive model (molecule)
Time frame: From descriptive analysis to the development of the predictive model: 6 months
Accuracy of the predictive model for the empirical antibiotic and its 95% confidence interval (95% CI), estimated using the validation set. Accuracy is defined as the proportion of bacteremia episodes for which the antibiotic predicted by the model is optimal, relative to the total number of episodes in the validation set.
6 secondary outcomes
Comparison of the performances estimated for the predictive model for optimal empirical antibiotic (molecule) with real-world estimates
Time frame: From the development of the predictive model: an average of one year
Expected advantages of the predictive model compared to real-world clinical outcome
Time frame: From the development of the predictive model: an average of one year
Accuracy of the predictive model (dosage)
Time frame: From the development of the predictive model: an average of one year
Comparison of the performances of the predictive model for optimal empirical antibiotic dosing with standard patient care
Time frame: From the development of the predictive model: an average of one year
Description of clinical outcomes of two groups of patients who received optimal antibiotic therapy and those who did not.
Time frame: From the development of the predictive model: an average of one year
To identify patients and episode characteristics independently associated with resistance among patients with a positive blood culture, stratified by pathogen type.
Time frame: From the development of the predictive model: an average of one year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Oct 2026.
- Individual participant dataSharing planned: No. No there is not a plan to make IPD available
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Bacteremia
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Maëlle LE GOFF, MD principal investigator
Nantes University Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Bacteremia, run by Nantes University Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the predictive model (molecule) (time frame: From descriptive analysis to the development of the predictive model: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Oct 2026.