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Pneumonia Bacterial / Bloodstream Infection / Urinary Tract Infection Bacterial · Interventional Study
ClinicalTrials.gov · August 13, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a trial registration for an unstarted quasi-experimental study comparing empirical antibiotic prescriptions before and after implementation of a machine-learning decision support system in three infection types. No results are posted. The non-randomized pre-post design carries substantial risk of bias and cannot establish causality, though the clinical outcome measures (30-day cure, complications, survival) are relevant.
Interventional, Non Randomized, Sequential, Open label, Treatment purpose. Pneumonia - Bacterial, Urinary Tract Infection Bacterial, Bloodstream Infection; age from 18 Years. Intervention: Interventional. Compared with: Pre-interventional — No Intervention. n = 486. España (implied by lead sponsor institution).
This is a trial registration for an unstarted quasi-experimental study comparing empirical antibiotic prescriptions before and after implementation of a machine-learning decision support system in three infection types. No results are posted. The non-randomized pre-post design carries substantial risk of bias and cannot establish causality, though the clinical outcome measures (30-day cure, complications, survival) are relevant.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Results from this trial are not yet available. Once completed, the quasi-experimental design will provide observational data on whether machine-learning-guided empirical antibiotics improve clinical outcomes, but cannot establish causal efficacy due to lack of randomization and concurrent control.
This is a trial registration for a quasi-experimental study not yet recruiting, with no results posted; the design and primary outcome are stated but no efficacy data exist to evaluate.
As stated by the source record.
Quoted from the source exactly as published.
Results from this trial are not yet available. Once completed, the quasi-experimental design will provide observational data on whether machine-learning-guided empirical antibiotics improve clinical outcomes, but cannot establish causal efficacy due to lack of randomization and concurrent control.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no key findings. That is a gap in the analysis, not a judgement about the study.
Registry record from ClinicalTrials.gov (NCT07762378). This is a study registration, not published results. Lead sponsor: Instituto de Investigación Sanitaria Gregorio Marañón. Recruitment status: NOT_YET_RECRUITING. Phase: NA. Study type: INTERVENTIONAL. Enrollment: 486 participants (ESTIMATED). Conditions: Pneumonia - Bacterial, Urinary Tract Infection Bacterial, Bloodstream Infection. Interventions: OTHER: Machine Learning Decision Support System. Primary outcome measures: Clinical success , 30-Day. 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
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