Abstract: Objective To explore the effect of a specialized nurse-led therapeutic communication model on medication adherence of patients with fragility fractures after discharge from the hospital. Methods: In this study, 114 fragility fracture patients admitted to the orthopedic ward of a tertiary hospital in Guangdong Province from December 2022 to July 2023 were selected as the study subjects. The control group received routine inpatient health education. For the intervention group, specialized nurses implemented a structured therapeutic communication model during hospitalization. This included: 1. Establishing trust with patients through relational communication. 2. Conducting a comprehensive health assessment using evaluative communication. 3. Providing personalized health education based on the assessment results (therapeutic communication). 4. Feeding back the assessment results to physicians to assist in formulating personalized treatment plans. Additionally, specialized nurses conducted telephone follow-ups at 1 and 3 months post-discharge. Each follow-up involved: Re-establishing trust with patients (relational communication), assessing bone health maintenance and medication adherence (evaluative communication). Providing tailored health guidance to address identified issues (therapeutic communication). Results: The intervention group, which received the specialized nurse-led therapeutic communication model, showed significantly greater improvement in medication adherence at 1 and 3 months post-discharge compared to the control group. Medication adherence improved progressively over time in the intervention group. Conclusion: The specialized nurse-led therapeutic communication model significantly enhances medication adherence in fragility fracture patients post-discharge, outperforming traditional health education and communication approaches.
Abstract: Objective To explore the effect of a specialized nurse-led therapeutic communication model on medication adherence of patients with fragility fractures after discharge from the hospital. Methods: In this study, 114 fragility fracture patients admitted to the orthopedic ward of a tertiary hospital in Guangdong Province from December 2022 to July 202...Show More
Abstract: Objective: To develop a clinical prediction model for nonadherence to biologic therapy in patients with ulcerative colitis (UC) using machine learning techniques, and to assess its predictive accuracy to guide clinical interventions. Methods: A total of 221 UC patients who initiated biologic therapy between December 2023 and February 2025 at our hospital were included in this study. Data on 15 variables, such as age, sex, disease duration, and other clinical factors, were collected. Medication adherence was measured using the proportion of days covered (PDC), with a PDC >80% considered indicative of good adherence. The Support Vector Machine (SVM) and Boruta algorithms were employed to identify key predictors. A multivariate logistic regression model was developed using the intersection of factors identified by both algorithms. Model performance was evaluated using the C-index, ROC curve, calibration curve, decision curve analysis, and validation with the K-nearest neighbor (KNN) algorithm. Results: Of the 221 patients, 82 (37.1%) were categorized into the nonadherence group. Following factor selection, insurance status, depression level, education level, disease activity, and age were identified as significant predictors of nonadherence. The model demonstrated a C-index of 0.779, and the ROC curve showed an area under the curve (AUC) of 0.78. Calibration curve analysis revealed good model consistency, and KNN validation yielded high precision with an AUC of 0.9978 and a PR AUC of 0.9963. Conclusion: The developed prediction model for medication nonadherence in UC patients demonstrates robust predictive and calibration capabilities. This model may aid healthcare professionals in identifying high-risk patients and supporting timely clinical interventions.
Abstract: Objective: To develop a clinical prediction model for nonadherence to biologic therapy in patients with ulcerative colitis (UC) using machine learning techniques, and to assess its predictive accuracy to guide clinical interventions. Methods: A total of 221 UC patients who initiated biologic therapy between December 2023 and February 2025 at our ho...Show More