BACTERIUM: Study for a Machine-learning-based Model to Predict Bloodstream Infections
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Infection, Bloodstream
- Sponsor
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS
- Enrollment
- 5000
- Locations
- 1
- Primary Endpoint
- Rate of appropriate antibiotic therapy in patients with bloodstream infections
- Status
- Recruiting
- Last Updated
- last year
Overview
Brief Summary
An increase of healthcare-associated infections caused by multidrug- resistant organisms (MRDO) is currently observed. One of the main causes of the emergence of a MDRO infection is an overuse of antibiotics. Therefore, saving useless antibiotic treatment is currently a priority from a public health point of view. The evaluation of the risk of having a bloodstream infection will allow both activating faster treatment decisions (when the risk is significantly high) or to save useless resources in terms of diagnostic tests and treatments, also limiting the potential for side effects (when the risk is significantly low).
Investigators
Eligibility Criteria
Inclusion Criteria
- •adult patients hospitalized at the Gemelli Polyclinic Foundation
- •having at least one performed blood culture
- •starting an antibiotic therapy
Exclusion Criteria
- •blood cultures with contaminants
- •\<18 years old
- •died after less than 48 hours from blood cultures positivity
Outcomes
Primary Outcomes
Rate of appropriate antibiotic therapy in patients with bloodstream infections
Time Frame: 24 months
1) number of patients with bloodstream infections with appropriate antibiotic therapy before and after the application of the predictive model "Bacterium"
Blood cultures
Time Frame: 24 months
number of blood cultures done before and after the application of the predictive model "Bacterium"