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

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

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Key information

Conditions

Pneumonia - Bacterial Bacterial Infections Bacterial Infections and Mycoses Bloodstream Infection Infections Inflammation Lung Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Pneumonia Pneumonia, Bacterial Respiratory Tract Diseases Respiratory Tract Infections Sepsis Systemic Inflammatory Response Syndrome Urinary Tract Infection Bacterial

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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

Primary outcomes

Clinical success

Time frame: 30-Day

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

Study contacts

Contact information is provided by the study sponsor or research team.

Sofía De la Villa

CONTACT

[email protected]

34912868453

Sponsors and collaborators

Lead sponsor

Instituto de Investigación Sanitaria Gregorio Marañón

Other

Registry information

Official study title

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

Important dates

Study start: 2026
Primary completion: 2027
Study completion: 2027
First posted: Aug 13, 2026
Registry last updated: Aug 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

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