Prediction of Pressure Injury Risk in ICU Using Data Mining

Overview

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.

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