Identifying Risk Factors for Pressure Injury in Adult Critical Care Patients

Jill Cox, PhD, RN, APN-C, CWOCN; Marilyn Schallom, PhD, RN, CCRN, CCNS; Christy Jung, MA


Am J Crit Care. 2020;29(3):204-213. 

In This Article

Abstract and Introduction


Background: Critically 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.

Objective: To 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.

Methods: Data 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.

Results: In 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.

Conclusion: Determining 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.


Intensive care units are steeped in technology aimed at saving the lives of critically ill and injured patients. As a result, today patients are surviving illnesses and injuries that just a decade ago might have been fatal. However, survival may come with unintended clinical consequences such as the development of a pressure injury. Hospital-acquired pressure injury in the critical care population remains a serious health care concern, with reported prevalence rates from 12% to 24.5%; these are the highest rates found among all health care settings.[1] Health care costs attributed to pressure injury across all settings in the United States are now estimated to be $26.8 billion annually.[2]

Although substantial empirical evidence indicates that implementing evidence-based prevention practices can reduce the rate of pressure injury,[3,4] use of these strategies has not eliminated all pressure injuries, especially in critically ill patients. In fact, between 2013 and 2016, the rate of hospital-acquired pressure injuries reportedly increased from 3.6 to 4.8 in 10 000 hospital encounters.[5] Therefore, it is likely that some acquired pressure injuries are inevitable despite the best efforts of caregivers to prevent them.

The first step in preventing pressure injuries is to identify the corresponding risk factors. Although current validated pressure injury risk assessment scales such as the Braden Scale[6] address global risk factors for pressure injury, many critical care patients are exposed to a variety of factors not accounted for in formal risk assessments. Patient age, length of stay (LOS) in the intensive care unit (ICU), diabetes mellitus, cardiovascular disease, use of vasopressor agents, hypotension, sedation, and mechanical ventilation have all been cited in recent systematic reviews of the critical care literature as risk factors that need more attention in this population.[7–9]

Strengthening the evidence base with regard to these risk factors will improve our ability to differentiate avoidable from unavoidable pressure injuries. The purpose of this study was to identify factors predictive 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.[10]