Pneumonia - BacterialNCT07762378
Clinical Impact of a Machine Learning Decision Support System for Empirical Antibiotic Therapy
A study testing Machine Learning Decision Support System in people with Pneumonia - Bacterial and Urinary Tract Infection Bacterial, run by Instituto de Investigación Sanitaria Gregorio Marañón.
Trial summary
The goal of this quasi-experimental study is to analyze if a Machine Learning Clinical Decision Support System can improve the empirical antibiotic treatment in patients with pneumonia, urinary tract infection and / or sepsis. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pneumonia - Bacterial, Urinary Tract Infection Bacterial, Bloodstream Infection
- What is being tested?
- Machine Learning Decision Support System
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 486 participants
- Where
- No sites listed yet
- Study dates
- Sep 2026to Sep 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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
Who may be able to join
- Adult patients (aged ≥18 years)
- Admitted to the Nephrology, Oncology or ICU wards
- Diagnosis of sepsis, pneumonia and/or UTI
- Empirical antibiotics prescribed
Who cannot join
- informed consent obtained > 48 hours since the infection onset
- beta-lactam allergy
- infection syndrome other than sepsis, pneumonia or UTI
- confirmed no-bacterial infection
- death within the first 48 hours of inclusion or imminent risk of death at time of the inclusion
- pregnancy and/or breastfeeding
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Clinical Impact of a Machine Learning Decision Support System for Empirical Antibiotic Therapy: A Prospective Quasi-Experimental Study
The goal of this quasi-experimental study is to analyze if a Machine Learning Clinical Decision Support System can improve the empirical antibiotic treatment in patients with pneumonia, urinary tract infection and / or sepsis.
The main questions it aims to answer are:
Read more
- Primary outcome: clinical success defined as clinical cure (resolution of all signs and symptoms related to infection); no complications until day 30 (recurrence, or development of adverse events- AEs-); no new acquisition of MDROs; and survival at day 30.
- Secondary outcomes: a subgroup analysis of the primary outcome according to the department participants, infectious syndrome, severity of the infection assessed by the SOFA score, and in microbiological confirmed infections. In microbiological confirmed infections, desirability of Outcome Ranking (DOOR) for the Management of Antimicrobial Therapy (MAT) according to the beta-lactam classification
Researchers will compare a pre-intervention group with a post-intervention to see if improve in the DOOR MAT score
Participants in the post-intervention group will:
- Received empirical antibiotic therapy prescribed by their treating physicians according to the machine-learning recommendations
- Being tested
- Machine Learning Decision Support System
- Keywords
- antibiotics, empiric therapy, clinical decision support system, multidrug resistant microorganism, antimicrobial stewardship
- Medical subject headings
- Pneumonia, Bacterial, Sepsis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine Learning Decision Support SystemOther
iAST® (Pragmatech AI Solutions) is a medical device designed to assist the antibiotic prescription, currently approved by the European Medicines Agency. It used complex algorithms to accurately predict the most likely recommended antibiotics for providing coverage for specific aerobic bacteria before definitive microbiological results, bacterial identification and antibiotic susceptibility testing, were known
| Study group | Role | What they receive |
|---|---|---|
| Pre-interventional | No intervention | Hospital standard practices for diagnosis and/or treatment applied |
| Interventional | Experimental | Hospital standard practices + use of a CDSS tool Receives: Other: Machine Learning Decision Support System |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clinical success
resolution of all signs and symptoms related to infection with no complications and survival
Measured: 30-Day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Aug 2026
- Study startSep 2026 (estimated)
- Primary completionAug 2027 (estimated)
- Study completionSep 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pneumonia - BacterialStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Non-randomised
- Intervention model
- Sequential
- Masking
- None (open label)
- Enrolment
- 486 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Instituto de Investigación Sanitaria Gregorio Marañón
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sofia De La Villa, Instituto de Investigación Sanitaria Gregorio Marañón
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07762378
- Sponsor's protocol ID
- MICRO.HGUGM.2026-003
- Status last verified
- Aug 2026
- First submitted
- 5 Aug 2026
- Last updated
- 13 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 13 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Impact of a Machine Learning Decision Support System for Empirical Antibiotic Therapy: A Prospective Quasi-Experimental Study
- ClinicalTrials.gov ID
- NCT07762378
- Acronym / sponsor ID
- MICRO.HGUGM.2026-003
- Keywords
- antibiotics, empiric therapy, clinical decision support system, multidrug resistant microorganism, antimicrobial stewardship
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 486 participants (estimated)
- Lead sponsor
- Instituto de Investigación Sanitaria Gregorio MarañónUniversity, hospital or other
- Study start
- Sep 2026 (estimated)
- Primary completion
- Aug 2027 (estimated)
- Study completion
- Sep 2027 (estimated)
- First posted
- 13 Aug 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.
The goal of this quasi-experimental study is to analyze if a Machine Learning Clinical Decision Support System can improve the empirical antibiotic treatment in patients with pneumonia, urinary tract infection and / or sepsis.
The main questions it aims to answer are:
- Primary outcome: clinical success defined as clinical cure (resolution of all signs and symptoms related to infection); no complications until day 30 (recurrence, or development of adverse events- AEs-); no new acquisition of MDROs; and survival at day 30.
- Secondary outcomes: a subgroup analysis of the primary outcome according to the department participants, infectious syndrome, severity of the infection assessed by the SOFA score, and in microbiological confirmed infections. In microbiological confirmed infections, desirability of Outcome Ranking (DOOR) for the Management of Antimicrobial Therapy (MAT) according to the beta-lactam classification
Researchers will compare a pre-intervention group with a post-intervention to see if improve in the DOOR MAT score
Participants in the post-intervention group will:
- Received empirical antibiotic therapy prescribed by their treating physicians according to the machine-learning recommendations
Study design
- Primary purpose
- Treatment
- Allocation
- Non-randomised
- Intervention model
- Sequential
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Pre-interventional | No intervention | –Hospital standard practices for diagnosis and/or treatment applied |
| Interventional | Experimental | Other: Machine Learning Decision Support SystemHospital standard practices + use of a CDSS tool |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Adult patients (aged ≥18 years)
- Admitted to the Nephrology, Oncology or ICU wards
- Diagnosis of sepsis, pneumonia and/or UTI
- Empirical antibiotics prescribed
Key exclusion criteria (6 listed)
- informed consent obtained > 48 hours since the infection onset
- beta-lactam allergy
- infection syndrome other than sepsis, pneumonia or UTI
- confirmed no-bacterial infection
- death within the first 48 hours of inclusion or imminent risk of death at time of the inclusion
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 |
|---|---|---|
| Machine Learning Decision Support SystemiAST® (Pragmatech AI Solutions) is a medical device designed to assist the antibiotic prescription, currently approved by the European Medicines Agency. It used complex algorithms to accurately predict the most likely recommended antibiotics for providing coverage for specific aerobic bacteria before definitive microbiological results, bacterial identification and antibiotic susceptibility testing, were known | Other | Interventional |
Primary outcome
Clinical success
Time frame: 30-Day
resolution of all signs and symptoms related to infection with no complications and survival
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Aug 2026.
- Individual participant dataSharing planned: Undecided
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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.
Questions about this trial
What is this clinical trial studying?
A study testing Machine Learning Decision Support System in people with Pneumonia - Bacterial and Urinary Tract Infection Bacterial, run by Instituto de Investigación Sanitaria Gregorio Marañón.
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 4 inclusion and 6 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: opening soon, as of the sponsor's last update on 13 Aug 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Sofía De la Villa (34912868453, sofiadela.villa@salud.madrid.org), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinical success (time frame: 30-Day). 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 13 Aug 2026.