Predictive Analytics in Lower Respiratory Tract Infections (VA-LRTIs)

• Conditions: Ventilator-Associated Event
• Interventions: Other: Diagnostic and prognostic models for VA-LRTIs

• Registration Number: NCT04727957
• Lead Sponsor: University of Oulu

Brief Summary

To determine the accuracy and generalizability of VA-LRTI algorithm to detect and predict three high-incidence and high-impact VAEs from electronic health records data: 1) ventilator-associated event, 2) ventilator-associated pneumonia, and 3) ventilator-associated tracheobronchitis.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2021-01-05
• Study First Submitted QC: 2021-01-26
• Study First Posted: 2021-01-27
• Status Verified: 2021-04
• Last Update Submitted: 2021-04-12
• Last Update Posted: 2021-04-15
• Study Start: 2021-06-01
• Primary Completion: 2021-12-31
• Study Completion: 2021-12-31

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 30000

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Dataset for development and testing	Diagnostic and prognostic models for VA-LRTIs	-
Dataset for external validation	Diagnostic and prognostic models for VA-LRTIs	-

Primary Outcome Measures

Name	Time	Method
Number of participants with VA-LRTI as assessed by risk-calculator	1.6.-31.12.2021	Prediction model to be used at the moment of diagnosis and algorithms to be used prior IMV to predict the risk of VA-LRTI.

Secondary Outcome Measures

Name	Time	Method
Rate of in-hospital mortality	1.6.-31.12.2021	Prediction model to be used at the moment of diagnosis to predict the risk of mortality in VA-LRTIs.

Trial Locations

Locations (1)

Univeristy of Oulu; 🇫🇮 Oulu, Finland