Artificial Intelligence With Determination of Central Venous Catheter Line Associated Infection Risk

NCT05914571 | Unknown

• 1 other identifier: OTCELEBİ
• Study Type: observational
• Target: 2,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

The goal of this methodological, retrospective and prospective study is to; it is a tool to develop a risk estimator tool to detect risk gaps in individuals using artificial intelligence technology that is dangerous for those with CVC in adult intensive care patients, to test risk level estimation frameworks and to evaluate outcomes in the clinic. In our study, it is also our aim to protect, to present the security measures to prevent the risk of CVC with an artificial intelligence model, in an evidence-based way. The main question\[s\]it aims to answer are:

• Can the risk of CVC-related infection be determined in adult intensive care patients using artificial intelligence?
• To what degree of accuracy can the risk of CVC-associated infection be determined in adult intensive care patients using artificial intelligence?
• What are the nursing practices that can reduce the risk of CVC-related infections? Methodology to develop an artificial intelligence-based CVC-associated infection risk level determination algorithm, retrospective using data from Electronic Health Records (EHR) patient data and manual patient files between January 2018 and December 2022 to create the algorithm and test the model accuracy, and the development stages of the model After the completion of the model, up-to-date data were collected for the use of the model and it was planned to be done prospectively.

Conditions

Artificial Intelligence; Central Venous Catheter Associated Bloodstream Infection

Keywords

nursing; artificial intelligence; Central Venous Catheter Associated Bloodstream Infection

Outcome Measures

Primary Outcomes (1)

• risk of central venous catheter infection

  january 2018 - december 2022

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients admitted to the GICU and meeting the research criteria will be included in the study.

You may qualify if:

• Received at least 48 hours of treatment in the GICU,
• Age ≥ 18,
• CVC inserted,
• No existing infection before hospitalization, patient data will be included in the dataset for designing and training the artificial intelligence model.

You may not qualify if:

• Age \<18,
• Those receiving immunosuppressive therapy,
• Those with multiple organ failure,
• Patients undergoing organ transplantation,
• Patients with a diagnosis of chronic kidney failure, will not be included in the dataset.

Sponsors & Collaborators

• Saglik Bilimleri Universitesi: lead

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 13, 2023
• First Posted: June 22, 2023
• Study Start: July 1, 2023
• Primary Completion: December 1, 2023
• Study Completion: December 1, 2024
• Last Updated: June 22, 2023

Record last verified: 2023-06

Data Sharing

• IPD Sharing: Will not share