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

NCT07762378 | Not Yet Recruiting

• 1 other identifier: MICRO.HGUGM.2026-003
• Study Type: interventional
• Target: 486
• Locations: 0 countries
• Sites: N/A

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

Conditions

Pneumonia - Bacterial; Urinary Tract Infection Bacterial; Bloodstream Infection

Keywords

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

Outcome Measures

Primary Outcomes (1)

• Clinical success

  resolution of all signs and symptoms related to infection with no complications and survival

  30-Day

Study Arms (2)

Pre-interventional

NO INTERVENTION

Hospital standard practices for diagnosis and/or treatment applied

Interventional

EXPERIMENTAL

Hospital standard practices + use of a CDSS tool

Other: Machine Learning Decision Support System

Interventions

Machine Learning Decision Support System OTHER

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

Interventional

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

• informed consent obtained \> 48 hours since the infection onset
• beta-lactam allergy
• infection syndrome other than sepsis, pneumonia or UTI
• confirmed no-bacterial infection
• pregnancy and/or breastfeeding

Sponsors & Collaborators

• Instituto de Investigación Sanitaria Gregorio Marañón: lead

MeSH Terms

Conditions

Pneumonia, Bacterial; Sepsis

Condition Hierarchy (Ancestors)

Bacterial Infections; Bacterial Infections and Mycoses; Infections; Pneumonia; Respiratory Tract Infections; Lung Diseases; Respiratory Tract Diseases; Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Sofía De la Villa

CONTACT

34912868453; sofiadela.villa@salud.madrid.org

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: SEQUENTIAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: August 5, 2026
• First Posted: August 13, 2026
• Study Start: September 1, 2026
• Primary Completion (Estimated): August 1, 2027
• Study Completion (Estimated): September 1, 2027
• Last Updated: August 13, 2026

Record last verified: 2026-08