Exploration of pressure injury risk in adult inpatients: An integrated Braden scale and rough set approach

Objective: This study aimed to develop an interpretive model with decision rules to assess the risk level of pressure injuries in adult inpatients and identify the critical risk factors associated with these injuries.Methods: The rough set approach was used to identify the critical risk factors associated with pressure injuries and demonstrate their behavioral patterns. The study focused on adult inpatients aged 18 or above who remained in bed for at least 24 hours after admission. The data was extracted from a nursing electronic medical record system of a hospital in Zhejiang Province, China, from 27 October 2019 to 1 November 2020.Results: The critical risk factors associated with pressure injuries in adult inpatients were identified as Sensory perception, Nutrition, and Friction and shear. A prediction model with 89 decision rules was established and demonstrated reliable predictive capabilities. Nursing staff should focus more on high-risk and severe-risk rules (Rules 11 to 18) to reduce the likelihood of potential high-risk pressure injuries.Conclusions: The prediction model established by the rough set approach can be used to identify the critical risk factors of pressure injuries and has good explanatory ability, which can complement and improve the predictive accuracy of the Braden Scale. The decision-making rules can help nurses improve work efficiency. Implications for Clinical Practice: Explanatory analysis can explain most inpatients' potential risk patterns and corresponding critical risk factors. Data-driven research models and results can help nurses understand patients' potential risks better. Additionally, these insights can be valuable in nursing education, aiding new nurses in comprehending and addressing the potential risks patients face.

Automated summary

This study developed an interpretive model using the rough set approach to assess the risk level of pressure injuries in adult inpatients and identified critical risk factors associated with these injuries. The prediction model established demonstrated reliable predictive capabilities and can help nurses improve work efficiency by focusing on high-risk and severe-risk rules.