Objective:To construct a frailty risk prediction model for elderly patients with CHD based on machine learning, to address the limitations of existing tools and provide evidence-based support for clinical practice.Methods:A retrospective study was conducted on elderly CHD patients hospitalized at the Heart Center of the First Affiliated Hospital of Henan University of Chinese Medicine from September 2023 to March 2024. Meta-analysis and expert meetings were used to identify the risk factors for frailty in elderly CHD patients. Three machine learning algorithms, Logistic Regression, Random Forest, and Support Vector Machine, were used to construct predictive models using R 4.3.1 software. The predictive performance of the models was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC).Results:A total of 490 elderly CHD patients were included, with 267 males and 223 females, and an age of (71.02 ± 7.73) years. Among them, 160 patients (32.7%) developed frailty. Of the constructed models, the Random Forest model demonstrated the best predictive performance, with an accuracy of 0.703, recall of 0.629, and F1 score of 0.741, while the AUC was 0.811 (95% CI 0.762-0.850). Conclusions:The Random Forest model exhibited good predictive performance in assessing frailty risk in elderly CHD patients, with high accuracy and reliability. Future external validation studies can further assess its applicability and stability in different populations.

Objective:To construct a frailty risk prediction model for elderly patients with CHD based on machine learning, to address the limitations of existing tools and provide evidence-based support for clinical practice.Methods:A retrospective study was conducted on elderly CHD patients hospitalized at the Heart Center of the First Affiliated Hospital of Henan University of Chinese Medicine from September 2023 to March 2024. Meta-analysis and expert meetings were used to identify the risk factors for frailty in elderly CHD patients. Three machine learning algorithms, Logistic Regression, Random Forest, and Support Vector Machine, were used to construct predictive models using R 4.3.1 software. The predictive performance of the models was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC).Results:A total of 490 elderly CHD patients were included, with 267 males and 223 females, and an age of (71.02 ± 7.73) years. Among them, 160 patients (32.7%) developed frailty. Of the constructed models, the Random Forest model demonstrated the best predictive performance, with an accuracy of 0.703, recall of 0.629, and F1 score of 0.741, while the AUC was 0.811 (95% CI 0.762-0.850). Conclusions:The Random Forest model exhibited good predictive performance in assessing frailty risk in elderly CHD patients, with high accuracy and reliability. Future external validation studies can further assess its applicability and stability in different populations.

This paper reviews the definition and current situation of virtual reality, the application conditions, intervention mechanisms, effectiveness evaluation indicators, application forms and effects, shortcomings and prospects of virtual reality intervention in smoking cessation, in order to provide guidance and basis for the clinical practice and nursing of virtual reality intervention in smoking cessation in China.

Virtual reality is a computer-generated immersive interactive 3D technology that enables real-time interaction between users and virtual environments. It has been applied by scholars in the treatment and nursing of patients with Down syndrome and has become one of the non-pharmacological intervention methods for Down syndrome. In view of this, this paper reviews the definition and classification of virtual reality technology, the mechanism, application status, shortcomings, and prospects of virtual reality technology applied to patients with Down syndrome, in order to provide theoretical basis and practical guidance for the clinical application and nursing of virtual reality technology in patients with Down syndrome.