Using Nursing Information and Data Mining to Explore the Factors That Predict Pressure Injuries for Patients at the End of Life

This study investigated the association between patient characteristics and the occurrence of pressure injuries for patients at the end of life. A retrospective study was conducted using data collected from 2062 patients at the end of life between January 2007 and October 2015. In addition to demographic data and pressure injury risk assessment scale scores, injury history, disease type, and length of hospitalization were revealed as the major independent variables for predicting the occurrence of pressure injuries. Both χ ² tests and t tests were employed for binary variable analysis, and logistic regression was used to conduct multivariate analysis. Classification models were formulated through decision tree analysis, backpropagation neural network, and support vector machine algorithms. The rules obtained using the decision tree algorithm were analyzed and interpreted. The accuracy rate, sensitivity, and specificity of the decision tree, backpropagation neural network, and support vector machine algorithms were 77.15%, 79.54%, and 74.76%; 78.12%, 81.37%, and 74.85%; and 79.32%, 81.03%, and 78.75%, respectively. The predictive factors, ranked in order of importance, were history of pressure injuries, without cancer, excretion, activity/mobility, and skin condition/circulation. These were the primary shared risk factors among the four models used in this study.