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

University of Oulu30,000 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

30,000

Conditions

Ventilator-Associated Event

Interventions

Diagnostic and prognostic models for VA-LRTIsOTHER

Diagnostic and prognostic models for VA-LRTIs

Eligibility

Sex: ALLMin age: 16 YearsMax age: 100 YearsHealthy volunteers:

Medical Language ↔ Plain English

All invasively ventilated ICU patient (aged 18 or over)

Locations (1)

Univeristy of Oulu

Oulu, Finland

Outcomes

Primary Outcomes

Number of participants with VA-LRTI as assessed by risk-calculator

Prediction model to be used at the moment of diagnosis and algorithms to be used prior IMV to predict the risk of VA-LRTI.

Time frame: 1.6.-31.12.2021

Secondary Outcomes

Rate of in-hospital mortality

Prediction model to be used at the moment of diagnosis to predict the risk of mortality in VA-LRTIs.

Time frame: 1.6.-31.12.2021

Central Contacts

Miia Jansson, PhD

CONTACT

+358 50 470 12 62 miia.jansson@oulu.fi

Data from ClinicalTrials.gov

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