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Clinical Impact of a Machine Learning Decision Support System for Empirical Antibiotic Therapy

Clinical Impact of a Machine Learning Decision Support System for Empirical Antibiotic Therapy: A Prospective Quasi-Experimental Study

Status
Not yet recruiting
Phases
Unknown
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT07762378
Enrollment
486
Registered
2026-08-13
Start date
2026-09-01
Completion date
2027-09-01
Last updated
2026-08-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Bloodstream Infection, Pneumonia - Bacterial, Urinary Tract Infection Bacterial

Keywords

antibiotics, empiric therapy, clinical decision support system, multidrug resistant microorganism, antimicrobial stewardship

Brief 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. 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

Interventions

OTHERMachine Learning Decision Support System

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

Sponsors

Instituto de Investigación Sanitaria Gregorio Marañón
Lead SponsorOTHER

Study design

Allocation
NON_RANDOMIZED
Intervention model
SEQUENTIAL
Primary purpose
TREATMENT
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Adult patients (aged ≥18 years) * Admitted to the Nephrology, Oncology or ICU wards * Diagnosis of sepsis, pneumonia and/or UTI * Empirical antibiotics prescribed

Exclusion criteria

* 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 * inclusion in a clinical trial of antimicrobial treatment

Design outcomes

Primary

MeasureTime frameDescription
Clinical success30-Dayresolution of all signs and symptoms related to infection with no complications and survival

Contacts

CONTACTSofía De la Villa
sofiadela.villa@salud.madrid.org34912868453

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Aug 14, 2026