VAP Identification by AI (AI-VAP)

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

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 .

Study Overview

• Status: Completed
• Conditions: Ventilator Associated Pneumonia ( VAP)

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.

• Study Type: Observational
• Enrollment (Actual): 76

Contacts and Locations

Study Locations

• Switzerland
  • Ticino
    • Lugano, Ticino, Switzerland, 6900
      • Ospedale civico Lugano

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

All consecutive adult patients admitted to our ICU requiring invasive respiratory support for at least 48 hours will be enrolled

Description

Inclusion Criteria:

• adult patients admitted to our ICU requiring invasive respiratory support for at least 48 hours

Exclusion Criteria:

• previuos pneumonia

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Early identification of VAP	Sensitivity, specificity, AUROC and AUPRC	From July 2023 to Mars 2025

Collaborators and Investigators

• Sponsor: Ente Ospedaliero Cantonale, Bellinzona

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2023
• Primary Completion (Actual): March 1, 2025
• Study Completion (Actual): June 15, 2025

Study Registration Dates

• First Submitted: April 1, 2025
• First Submitted That Met QC Criteria: April 1, 2025
• First Posted (Actual): April 8, 2025

Study Record Updates

• Last Update Posted (Actual): August 24, 2025
• Last Update Submitted That Met QC Criteria: August 22, 2025
• Last Verified: August 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Healthcare-Associated Pneumonia; Pathologic Processes; Disease Attributes; Respiratory Tract Infections; Infections; Respiratory Tract Diseases; Lung Diseases; Cross Infection; Iatrogenic Disease; Pneumonia; Pneumonia, Ventilator-Associated
• Other Study ID Numbers: 2022-00624 CE 4085

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No