Prediction of Pressure Injury Risk in ICU Using Data Mining

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.

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