Purpose
The purposes of this study were to develop a prediction model for pressure injury using a machine learning algorithm and to integrate it into clinical practice.
Methods
This was a retrospective study of tertiary hospitals in Seoul, Korea. It analyzed patients in 12 departments where many pressure injuries occurred, including 8 general wards and 4 intensive care units from January 2018 to May 2022. In total, 182 variables were included in the model development. A pressure injury prediction model was developed using the gradient boosting algorithm, logistic regression, and decision tree methods, and it was compared to the Braden scale.
Results
Among the 1,389,660 general ward cases, there were 451 cases of pressure injuries, and among 139,897 intensive care unit cases, there were 297 cases of pressure injuries. Among the tested prediction models, the gradient boosting algorithm showed the highest predictive performance. The area under the receiver operating characteristic curve of the gradient boosting algorithm's pressure injury prediction model in the general ward and intensive care unit was 0.86 (95% confidence interval, 0.83~0.89) and 0.83 (95% confidence interval, 0.79~0.87), respectively. This model was integrated into the electronic health record system to show each patient's probability for pressure injury occurrence, and the risk factors calculated every hour.
Conclusion
The prediction model developed using the gradient boosting algorithm exhibited higher performance than the Braden scale. A clinical decision support system that automatically assesses pressure injury risk allows nurses to focus on patients at high risk for pressure injuries without increasing their workload.

Purpose
Sarcopenia is significantly associated with frailty, readmission, and mortality in patients with heart failure. This review aims to provide an overview of the literature on sarcopenia in individuals with heart failure.
Methods
A comprehensive literature review was conducted regarding the current state of knowledge on assessment tools for the diagnosis, prognosis, and optimal management of sarcopenia in patients with heart failure.
Results
Sarcopenia can be defined as the loss of muscle mass with low muscle strength and/or poor physical performance. Sarcopenia has been officially listed as a disease in the eighth revision of the Korean Classification of Diseases in 2021. The causes of sarcopenia in patients with heart failure are multifactorial, including chronic inflammation, hormonal imbalances, nutritional deficiencies, low muscle blood flow, and endothelial dysfunction. The management of sarcopenia is primarily focused on exercise and/or nutritional management because there is no specific pharmacological therapy to treat sarcopenia.
Conclusion
Healthcare professionals should be aware of the significance of early detection and timely management of sarcopenia to avoid physical disability, long-term institutional care, and mortality in individuals with heart failure. Clinical trials are required to evaluate the effectiveness of interventions including exercise and nutrition, alone or in combination, on sarcopenia in patients with heart failure. In addition, more research is required to identify multidimensional risk factors and diagnostic biomarkers for sarcopenia.