Early Detection of Deep Tissue Pressure Injury in Intensive Care Using Hemodynamics-Based Machine Learning: A Retrospective Cohort Study

Abstract

This study examines factors associated with deep tissue pressure injury and develops interpretable machine learning models for early risk prediction in adult ICU patients. This retrospective observational cohort study included 336 adult intensive care unit patients, of whom 211 developed deep tissue pressure injury and 125 remained pressure injury-free. For patients who developed deep tissue pressure injury, haemodynamic, laboratory and nursing variables from the 24 h before injury onset were analysed using a physiological time-at-risk framework. For pressure injury-free patients, corresponding variables from the first 24 h after intensive care unit admission were used. Six supervised machine learning classifiers were developed and internally validated using cross-validation and hyperparameter optimization. All models showed good predictive performance. Extreme gradient boosting achieved the highest discriminative ability, with an area under the receiver operating characteristic curve of 0.976. The most influential predictors across feature selection methods were low-molecular-weight heparin use, norepinephrine duration, lower blood pressure values, immobility, nutritional risk, antiplatelet therapy and chronic disease profile. Routinely documented nursing and haemodynamic indicators obtained within a clinically relevant 24-h risk window can support accurate early risk stratification for deep tissue pressure injury in adult intensive care unit patients. A physiology-informed and interpretable machine learning approach may improve recognition of patients at imminent risk.