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

Li, Hsiu-Lan; Lin, Shih-Wei, PhD; Hwang, Yi-Ting, PhD

CIN: Computers, Informatics, Nursing: March 2019 - Volume 37 - Issue 3 - p 133–141
doi: 10.1097/CIN.0000000000000489
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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 χ2 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.

Author Affiliations: Graduate Institute of Business and Management, Chang Gung University, Taoyuan City (Ms Li); Department of Nursing, En Chu Kong Hospital, New Taipei City (Ms Li); Department of Information Management, Chang Gung University, Taoyuan City (Dr Lin); Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan City (Dr Lin); Department of Industrial Engineering and Management, Ming Chi University of Technology, Taipei (Dr Lin); and Department of Statistics, National Taipei University, New Taipei City (Dr Hwang).

En Chu Kong Hospital provided financial support for this study (ECKP10201). The second author was supported by the Ministry of Science and Technology of the Republic of China (Taiwan) and the Linkou Chang Gung Memorial Hospital through research grants MOST105-2410-H-182-009-MY2/MOST106-2632-H-182-001 and CMRPD3G0011/CARPD3B0012, respectively.

The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.

Corresponding author: Shih-Wei Lin, PhD, 259 Wen-Hwa 1st Road, Kwei-Shan Tao-Yuan, Taiwan, 333, ROC (swlin@mail.cgu.edu.tw).

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