The application of braden scale and rough set theory for pressure injury risk in elderly male population
1Taizhou Hospital of Zhejiang Province aﬃliated to Wenzhou Medical University, Taizhou, P. R. China
2Institute for Hospital Management, Tsing Hua University, Shenzhen Campus, P. R. China
3Institute of Public Health & Emergency Management, Taizhou University, Taizhou, P. R. China
DOI: 10.31083/jomh.2021.022 Vol.17,Issue 4,September 2021 pp.156-165
Submitted: 18 December 2020 Accepted: 19 February 2021
Published: 30 September 2021
† These authors contributed equally.
Background: The elderly with a limited body or bedridden are prone to pressure injury, and the Braden scale is often used as a risk assessment tool. However, few studies have explained the relationship between risk factors and risk levels using machine learning methods from Braden clinical observation data. Additionally, nearly half of the elderly over 75 years old in China are men.
Purpose: This study aimed to establish a pressure injury risk prediction model for elderly male patients using a machine learning method based on hospital clinical data. It further analyses the importance of risk factors and risk levels.
Methods: This study's Braden observation data were obtained from the electronic medical records of elderly male patients from 27 October 2019 to 1 November 2020 in the case hospital. Rough set theory was used to identify the perception patterns between risk factors and risk levels based on the data.
Results: The importance of rough set theory showed that sensory perception and nutrition are key risk factors for identifying elderly male inpatients. Therefore, nurses should pay special attention to the measurement scores of these two risk factors. Moreover, this method also revealed conditions/decision rules for different risk levels. Among elderly male inpatients at risk of severe pressure injury, 42% of the observation data showed that their physical condition is completely limited in sensory perception, possibly insuﬃcient nutrition, friction and shearing problems, and bedridden activities.
Conclusion: This model can effectively identify the critical risk factors and decision rules for different risk levels for pressure injury in elderly male inpatients. This allows nurses to focus on patients at a high risk of possible pressure injury in the future without increasing their workload. This study also provides a way to solve the problem that the Braden scale shows insuﬃcient predictive validity and poor accuracy in identifying patients with different pressure injury risk levels, so it cannot fully reﬂect patients' characteristics.
Pressure injury risk; Braden scale; Predictive modeling; Data mining; Rough set theory
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