Trial · NCT07894237
Predicting the Optimal Empirical Antibiotic Therapy for Bacteremia Using an Artificial Intelligence-based Model
NCT07894237, Completed, Nantes University Hospital
3 sourcestrials-2026-11-12-0.5.0
Cite this record
ConductScience Library. Predicting the Optimal Empirical Antibiotic Therapy for Bacteremia Using an Artificial Intelligence-based Model. Trials, record NCT07894237. Release trials-2026-11-12-0.5.0, 12 Nov 2026. https://conductscience.org/library/trials/NCT07894237
On this page
Trial
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
- Title
- Predicting the Optimal Empirical Antibiotic Therapy for Bacteremia Using an Artificial Intelligence-based Model
- NCT number
- NCT07894237
- Status
- Completed
- Enrollment
- 8,765 participants, actual
- Lead sponsor
- Nantes University Hospital
- Conditions
- Bacteremia
- Interventions
- No Intervention: Observational Cohort
- First posted
- 2026-10-26
- Last updated
- 2026-10-26
Source ClinicalTrials.gov
Registered outcomes7
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description |
|---|---|---|---|
| Primary | Accuracy of the predictive model (molecule) | 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. |
| Secondary | Comparison of the performances estimated for the predictive model for optimal empirical antibiotic (molecule) with real-world estimates | From the development of the predictive model: an average of one year | 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). |
| Secondary | Expected advantages of the predictive model compared to real-world clinical outcome | From the development of the predictive model: an average of one year | 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. |
| Secondary | Accuracy of the predictive model (dosage) | From the development of the predictive model: an average of one year | Accuracy of the predictive model for empirical antibiotic dosage and its 95% CI, estimated using the validation sample. |
| Secondary | Comparison of the performances of the predictive model for optimal empirical antibiotic dosing with standard patient care | From the development of the predictive model: an average of one year | 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). |
| Secondary | Description of clinical outcomes of two groups of patients who received optimal antibiotic therapy and those who did not. | From the development of the predictive model: an average of one year | 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. |
| Secondary | To identify patients and episode characteristics independently associated with resistance among patients with a positive blood culture, stratified by pathogen type. | 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. 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. |
Source ClinicalTrials.gov
Outcomes0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
None in this release.
Results by group0
Each measurement the trial posted, group by group, in the words and numbers it posted.
None in this release.
Participant flow0
How many started, completed or left each group, period by period.
None in this release.
Adverse events0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
None in this release.
Adverse event totals0
The totals the trial posted for each group.
None in this release.
Papers0
The papers the trial lists on ClinicalTrials.gov.
None in this release.
Grants0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
None in this release.
Trial comparisons0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
None in this release.
Tests0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
None in this release.
Data dates
The day each source was copied for this release.
Sources 3
- ClinicalTrials.gov's posted results, through CTTI's AACTFreely available. Acknowledgement requested
- ClinicalTrials.govNo license stated
- NIH RePORTER's clinical studies and project filesPublic domain
Each source's terms
- ClinicalTrials.gov's posted results, through CTTI's AACTClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299
- ClinicalTrials.govnone stated
- NIH RePORTER's clinical studies and project filesPublic domain (NIH RePORTER)
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
For software
Read this trial from software.
The record and each of its lists are JSON, twenty rows a page. No key.
GET https://conductscience.org/api/trials/v1/NCT07894237
GET https://conductscience.org/api/trials/v1/NCT07894237/registered