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Prediction of Pressure Injury Risk in ICU Using Data Mining

NCT ID: NCT07306143

Last Updated: 2025-12-29

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2026-01-01

Study Completion Date

2026-06-01

Brief Summary

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This study is a retrospective record review conducted among adult patients hospitalized in the intensive care unit of a tertiary hospital between October 10, 2020, and October 10, 2025. The aim of the study is to predict the risk of pressure injury development using demographic, clinical, laboratory, and nursing care-related variables by applying multiple data mining algorithms. No intervention, treatment, or patient contact will occur. All data will be extracted from existing electronic and paper-based medical records and will be fully anonymized prior to analysis. The study poses no risk to participants and will be conducted with approval from the institutional review board or ethics committee.

Detailed Description

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This observational study uses a retrospective cohort design to analyze the clinical, demographic, laboratory, and nursing documentation records of adult intensive care unit (ICU) patients hospitalized between October 10, 2020, and October 10, 2025. The purpose of the study is to identify factors associated with the development of pressure injury and to compare the predictive performance of multiple data mining and machine learning algorithms, including logistic regression, decision trees, random forest, support vector machines, and gradient boosting models.

Data collection will involve reviewing archived ICU records, patient files, and nursing observation forms. No new data will be collected directly from patients, and no medical interventions or prospective follow-up will be performed. All extracted data will be fully anonymized prior to analysis. The study will be conducted in accordance with ethical principles and has been approved by the Bolu Abant Izzet Baysal University Non-Interventional Clinical Research Ethics Committee.

The expected outcome of this study is to identify the most accurate predictive model for pressure injury risk and to support clinical decision-making processes by contributing to early prevention strategies in the ICU.

Conditions

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Pressure Injury

Keywords

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Pressure injury risk Data mining Machine learning Retrospective study Risk prediction

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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ICU Patient Cohort

Adult patients who will be hospitalized in the intensive care unit between October 10, 2020 and October 10, 2025. No interventions will be applied, and all data will be obtained from existing medical records.

No intervention

Intervention Type OTHER

This is a retrospective observational study. No interventions will be applied. All data will be obtained from existing medical records.

Interventions

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No intervention

This is a retrospective observational study. No interventions will be applied. All data will be obtained from existing medical records.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Patients hospitalized in the intensive care unit between October 10, 2020, and \* October 10, 2025
* Adult patients aged 18 years and older
* Length of intensive care unit stay of at least 24 hours
* Availability of complete and accessible electronic or paper-based medical records

Exclusion Criteria

* Patients younger than 18 years
* Intensive care unit stay shorter than 24 hours Incomplete, missing, or inconsistent electronic or paper-based medical records
* Presence of a pressure injury diagnosed before or at the time of intensive care unit admission
* Inability to extract pressure injury-related data from medical records
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Abant Izzet Baysal University

OTHER

Sponsor Role lead

Responsible Party

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Saadet Can Çiçek

Associate Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Saadet Can Çiçek, Assoc. Prof., PhD

Role: PRINCIPAL_INVESTIGATOR

Abant Izzet Baysal University

Locations

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Bolu Izzet Baysal State Hospital

Bolu, Bolu, Turkey (Türkiye)

Site Status

Countries

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Turkey (Türkiye)

Central Contacts

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Saadet Can Çiçek, Assoc. Prof., PhD

Role: CONTACT

Phone: +905062846936

Email: [email protected]

Facility Contacts

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Nurcan Tekinalp, PhD Candidate

Role: primary

Saadet Can Çiçek, Assoc. Prof., PhD

Role: backup

Other Identifiers

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AIBU-SBF-NSY-2025

Identifier Type: -

Identifier Source: org_study_id