Kun Li

1. The global health care crisis has been exacerbated by critical nursing shortages (Kelly et al., 2026; Ren et al., 2024), which has led to an urgent demand for innovative solutions to bridge care...

Objectives: To develop and validate a nomogram for predicting hospitalization-associated disability in older patients with acute heart failure. Design: A single-center cohort study. Setting and participants: This study was carried out in the Cardiovascular Disease Center of a large tertiary-care hospital in China. Between December 2024 and February 2025, 480 older patients with acute heart failure were enrolled. Methods: Hospitalization-associated disability was defined as a decline of 5 points or more in the Barthel Index from admission to discharge. Predictor screening involved univariable logistic regression, Spearman's correlation, and Least Absolute Shrinkage and Selection Operator regression. Variables retained were entered into a multivariable logistic regression model, and significant predictors were used to construct a nomogram for predicting hospitalization-associated disability. Model performance was assessed in terms of discrimination, calibration, and clinical utility. Results: The incidence of hospitalization-associated disability was 41.88%. A 12-variable nomogram was developed, incorporating age, ejection fraction, emergency admission, comorbidity burden, cognitive function, nutritional risk, pre-admission activities of daily living, pre-admission instrumental activities of daily living, physical mobility, sleep disturbance, depressive symptoms, and perceived social support. The nomogram demonstrated robust discrimination, with the area under the receiver operating characteristic curve of 0.841 and 0.786 in the training and testing sets, respectively. Calibration was accurate in both sets. The training set achieved a mean absolute error of 0.037 and a Brier score of 0.154, while the testing set showed 0.026 and 0.188, accompanied by non-significant Hosmer-Lemeshow test results. Decision curve and clinical impact analyses further supported favorable clinical utility. Conclusions and implications: A 12-variable nomogram was developed and validated in older individuals with acute heart failure, enabling early identification of hospitalization-associated disability risk and supporting personalized care strategies.

Background: Social participation refers to individuals engaging in important and meaningful activities in life situations, such as social interaction. There is limited published literature about social participation patterns and their relationships with personal and environmental factors. Objectives: We aimed to identify social participation patterns among Chinese survivors of stroke and evaluate how personal and environmental factors differentiate these patterns. Methods: Using data from the 2018 China Health and Retirement Longitudinal Study, 502 survivors of stroke aged 45 and above were analyzed. Latent class analysis identified distinct participation patterns, and multinomial logistic regression explored associated factors. Results: Three patterns of social participation were identified: High Organization- and Information-Centered pattern (class 1, 6.18%); High Interaction- and Entertainment-Centered pattern (class 2, 4.78%); Low Entertainment- and Organization-Centered pattern (class 3, 89.04%). Participants assigned to class 2 had minimal comorbidities and the lowest depression scores, while those in class 1 exhibited higher cognitive function scores, as well as the highest scores for livable residence. Age, the number of chronic diseases, recall ability, monthly household expenditure, and having a livable residence were significantly associated with patterns of social participation. Conclusion: This study identified 3 distinct patterns of social participation among Chinese survivors of stroke, with personal factors (ie, number of chronic diseases and cognitive function) and environmental factors (ie, livable residence) significantly differentiating these patterns. These findings suggest that interventions should be tailored to survivors’ health status and living conditions to promote meaningful social participation and potentially improve their physical, psychological, and cognitive health outcomes.

Aims and Objectives: To summarise the currently developed risk prediction models for medication adherence in patients with chronic diseases and evaluate their performance and applicability.Background: Ensuring medication adherence is crucial in effectively managing chronic diseases. Although numerous studies have endeavoured to construct risk prediction models for predicting medication adherence in patients with chronic illnesses, the reliability and practicality of these models remain uncertain.Design: Systematic review.Methods: We conducted searches on PubMed, Web of Science, Cochrane, CINAHL, Embase and Medline from inception until 16 July 2023. Two authors independently screened risk prediction models for medication adherence that met the predefined inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to evaluate both the risk of bias and clinical applicability of the included studies. This systematic review adhered to the 2020 PRISMA checklist.Results: The study included a total of 11 risk prediction models from 11 studies. Medication regimen and age were the most common predictors. The use of PROBAST revealed that some essential methodological details were not thoroughly reported in these models. Due to limitations in methodology, all models were rated as having a high‐risk for bias.Conclusions: According to PROBAST, the current models for predicting medication adherence in patients with chronic diseases exhibit a high risk of bias. Future research should prioritise enhancing the methodological quality of model development and conducting external validations on existing models.Relevance to Clinical Practice: Based on the review findings, recommendations have been provided to refine the construction methodology of prediction models with an aim of identifying high‐risk individuals and key factors associated with low medication adherence in chronic diseases.Patient or Public Contribution: This systematic review was conducted without patient or public participation.