Clinical Trials Directory

Trials / Completed

CompletedNCT06917521

VAP Identification by AI

EARLY IDENTIFICATION OF VENTILATOR ASSOCIATED PNEUMONIA USING MACHINE LEARNING TECHNIQUES: A PROSPECTIVE COHORT

Status
Completed
Phase
Study type
Observational
Enrollment
76 (actual)
Sponsor
Ente Ospedaliero Cantonale, Bellinzona · Academic / Other
Sex
All
Age
18 Years
Healthy volunteers
Not accepted

Summary

Ventilator-associated pneumonia (VAP) is the most frequent infection in the intensive care setting. For VAP there is currently no reliable diagnostic criteria. We aimed with the present study, using data from the mechanical ventilator to identify early this infection using artificial intelligence methods .

Detailed description

Ventilator-associated pneumonia (VAP) is defined as a hospital-acquired pneumonia occurring in patients submitted to invasive mechanical ventilation (MV) for at least 48 hours. VAP represents the most prevalent nosocomial infection in the intensive care setting. VAP is burdened by prolonged duration of MV and hospital length of stay and consequently increases hospital costs. Moreover, mortality and antibiotic use are also significantly affected. Unfortunately, there is currently no valid, accurate diagnostic criteria of VAP because even the most widely used ones are neither sensitive nor specific.. The insufficient sensitivity of these criteria to rule out VAP carries the risk of antibiotic overuse with the consequently emerging of antibiotic resistance and superinfections. On the other hand, the insufficient specificity to rule in VAP carries the risk of delayed administration of antimicrobial therapy leading to increased mortality. Ventilator-associated event surveillance failed to accurately identify VAP, too . The purpose of the present study is to develop different AI-algorithms using data continuously recorded form the mechanical ventilator in supporting clinicians for the early detection of VAP. An accurate AI-algorithm for early VAP identification has the potential to reduce morbidity, mortality, exposure to broad-spectrum and/or unnecessary antibiotics and finally to reduce costs.

Conditions

Timeline

Start date
2023-07-01
Primary completion
2025-03-01
Completion
2025-06-15
First posted
2025-04-08
Last updated
2025-08-24

Locations

1 site across 1 country: Switzerland

Source: ClinicalTrials.gov record NCT06917521. Inclusion in this directory is not an endorsement.