Big data and machine learning helped develop a model for predicting risk for pressure injuries in critical care patients, according to new research published in the November issue of American Journal of Critical Care (AJCC).
The research team examined five years of data on patients admitted to the adult surgical or surgical cardiovascular intensive care units at the University of Utah Hospital in Salt Lake City. Among the sample of 6,376 patients, hospital-acquired pressure injuries of stage 1 or greater developed in 516 patients, and injuries of stage 2 or greater developed in 257 patients.
With these two outcome variables identified, the researchers used machine learning to effectively and efficiently look at the large amount of clinical data readily available in the patient records and examine the relationships among the available predictor variables. They used a technique called random forest, which is relatively unaffected by moderate correlations among variables, an important characteristic because correlations among clinical variables are common in health research.
The researchers believe their study is the only one in which machine learning was used to predict development of pressure injuries in critical care patients. “Current risk-assessment tools classify most critical are patients as high risk for developing pressure injuries and therefore do not provide a way to differentiate among critical care patients in terms of pressure injury risk,” said principal investigator Jenny Alderden, PhD, assistant professor in the School of Nursing, Boise State University. “Eventually, our model may offer additional insight to clinicians as they develop a plan of care for patients at highest risk and identify those who would benefit most from interventions that are not financially feasible for every patient.”
Among the variables that were most important according to the model’s mean decrease in accuracy was time required for surgery, an element that has not been well studied as a potential contributor to risk for pressure injury. Body mass index, hemoglobin level, creatinine level and age were also ranked as important variables on the basis of the model’s mean decrease in accuracy. The mean decrease in accuracy, which reflects complex relationships among variables, is assessed by temporarily removing a variable from the analysis and evaluating the change in model performance.
Eventually, the model could help identify which patients are at the greatest risk for developing pressure injuries and who would benefit from interventions such as specialty beds or more frequent skin inspection. The next step will be to validate and evaluate the model in a new sample of patients.
Source: American Association of Critical-Care Nurses