Conectar
To explore the factors influencing the intention of patients with coronary heart disease to undergo cardiac rehabilitation.
This is a qualitative content analysis study.
Semi structured, face-to-face interviews were conducted in the Department of Cardiology at a tertiary Grade-A hospital in Baoding, China, from January to March 2025. To ensure sample diversity, purposeful sampling was employed. The interview guide was based on the Reasoned Action Approach theory, literature review, and team deliberations. Data were analysed using deductive content analysis.
Twenty patients with coronary heart disease participated in the interviews (average age 57.9 years; 10 males, 10 females; 0–360 months disease course). Nine themes were identified from the three dimensions of RAA attitudes, perceived norms, and perceived behavioural control, reflecting patients' attitudes regarding cardiac rehabilitation (rehabilitation is beneficial, safety concerns, and non-essential treatment strategy); the impact of external factors on cardiac rehabilitation in patients (lack of professional recommendations, lack of awareness among friends and family); and barriers and facilitators in the implementation of cardiac rehabilitation (limited resources, insufficient self-efficacy, responsibility-driven, and individualised needs are challenging to fulfil).
To enhance the cardiac rehabilitation intentions of patients with coronary heart disease, healthcare providers should comprehensively assess influencing factors from the patient's perspective. Tailored interventions should focus on cognitive restructuring, support system enhancement, and patient empowerment.
This study highlights factors influencing patients' cardiac rehabilitation intentions. Nurses, equipped with relevant knowledge and skills, can provide systematic cardiac rehabilitation education during hospitalisation, thereby enhancing intentions and improving participation in cardiac rehabilitation.
This study adheres to the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist guidelines.
Patients with coronary heart disease participated in the interviews and provided essential insights for this study.
To develop and validate a machine learning-based risk prediction model for delirium in older inpatients.
A prospective cohort study.
A prospective cohort study was conducted. Eighteen clinical features were prospectively collected from electronic medical records during hospitalisation to inform the model. Four machine learning algorithms were employed to develop and validate risk prediction models. The performance of all models in the training and test sets was evaluated using a combination of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, Brier score, and other metrics before selecting the best model for SHAP interpretation.
A total of 973 older inpatient data were utilised for model construction and validation. The AUC of four machine learning models in the training and test sets ranged from 0.869 to 0.992; the accuracy ranged from 0.931 to 0.962; and the sensitivity ranged from 0.564 to 0.997. Compared to other models, the Random Forest model exhibited the best overall performance with an AUC of 0.908 (95% CI, 0.848, 0.968), an accuracy of 0.935, a sensitivity of 0.992, and a Brier score of 0.053.
The machine learning model we developed and validated for predicting delirium in older inpatients demonstrated excellent predictive performance. This model has the potential to assist healthcare professionals in early diagnosis and support informed clinical decision-making.
By identifying patients at risk of delirium early, healthcare professionals can implement preventive measures and timely interventions, potentially reducing the incidence and severity of delirium. The model's ability to support informed clinical decision-making can lead to more personalised and effective care strategies, ultimately benefiting both patients and healthcare providers.
This study was reported in accordance with the TRIPOD statement.
No patient or public contribution.
FreshRSS 