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

NCT07762378 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 486

Last updated 2026-08-13

No results posted yet for this study

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

Interventions

OTHER

Machine 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 & Collaborators

  • Instituto de Investigación Sanitaria Gregorio Marañón

    lead OTHER

Study Design

Allocation
NON_RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Model
SEQUENTIAL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-09-30
Primary Completion
2027-08-31
Completion
2027-09-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07762378 on ClinicalTrials.gov