• Jill Cox, Marilyn Schallom, and Christy Jung.
• Jill Cox is an associate clinical professor at Rutgers University School of Nursing, Newark, New Jersey, and an advanced practice nurse and certified wound, ostomy, and continence nurse at Englewood Health, Englewood, New Jersey. Marilyn Schallom is a clinical nurse specialist and research scientist in the Department of Research for Patient Care Services, Barnes-Jewish Hospital, St Louis, Missouri. Christy Jung is a research analyst in the Office of Institutional Research and Assessment, Rutgers University School of Nursing.
• Am. J. Crit. Care. 2020 May 1; 29 (3): 204-213.

BackgroundCritically ill patients have a variety of unique risk factors for pressure injury. Identification of these risk factors is essential to prevent pressure injury in this population.ObjectiveTo identify factors predicting the development of pressure injury in critical care patients using a large data set from the PhysioNet MIMIC-III (Medical Information Mart for Intensive Care) clinical database.MethodsData for 1460 patients were extracted from the database. Variables that were significant in bivariate analyses were used in a final logistic regression model. A final set of significant variables from the logistic regression was used to develop a decision tree model.ResultsIn regression analysis, cardiovascular disease, peripheral vascular disease, pneumonia or influenza, cardiovascular surgery, hemodialysis, norepinephrine administration, hypotension, septic shock, moderate to severe malnutrition, sex, age, and Braden Scale score on admission to the intensive care unit were all predictive of pressure injury. Decision tree analysis revealed that patients who received norepinephrine, were older than 65 years, had a length of stay of 10 days or less, and had a Braden Scale score of 15 or less had a 63.6% risk of pressure injury.ConclusionDetermining pressure injury risk in critically ill patients is complex and challenging. One common pathophysiological factor is impaired tissue oxygenation and perfusion, which may be nonmodifiable. Improved risk quantification is needed and may be realized in the near future by leveraging the clinical information available in the electronic medical record through the power of predictive analytics.Copyright© 2020 American Association of Critical-Care Nurses.

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