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

April 12, 2021 updated by: Miia M Jansson, PhD, University of Oulu

Predictive Analytics in Ventilator-Associated Events

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 Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Anticipated)

30000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

16 years to 100 years (Child, Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Adult mixed medical-surgical ICU patients

Description

All invasively ventilated ICU patient (aged 18 or over)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Dataset for development and testing
Other: Diagnostic and prognostic models for VA-LRTIs
Diagnostic and prognostic models for VA-LRTIs
Dataset for external validation
Other: Diagnostic and prognostic models for VA-LRTIs
Diagnostic and prognostic models for VA-LRTIs

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of participants with VA-LRTI as assessed by risk-calculator
Time Frame: 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.
1.6.-31.12.2021

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Rate of in-hospital mortality
Time Frame: 1.6.-31.12.2021
Prediction model to be used at the moment of diagnosis to predict the risk of mortality in VA-LRTIs.
1.6.-31.12.2021

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Oulu

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

June 1, 2021

Primary Completion (Anticipated)

December 31, 2021

Study Completion (Anticipated)

December 31, 2021

Study Registration Dates

First Submitted

January 5, 2021

First Submitted That Met QC Criteria

January 26, 2021

First Posted (Actual)

January 27, 2021

Study Record Updates

Last Update Posted (Actual)

April 15, 2021

Last Update Submitted That Met QC Criteria

April 12, 2021

Last Verified

April 1, 2021

More Information

Terms related to this study

Other Study ID Numbers

  • University of Oulu

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

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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