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
Adult patients (aged ≥18 years)
Admitted to the Nephrology, Oncology or ICU wards
Diagnosis of sepsis, pneumonia and/or UTI
Empirical antibiotics prescribed
informed consent obtained > 48 hours since the infection onset
beta-lactam allergy
infection syndrome other than sepsis, pneumonia or UTI
confirmed no-bacterial infection