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Stroke survivors frequently experience multiple co-occurring symptoms that cluster together, significantly affecting their quality of life and rehabilitation outcomes. However, previous research has predominantly focused on individual symptoms in isolation, limiting the potential to inform more comprehensive, symptom cluster-based approaches to post-stroke care.
This scoping review aimed to synthesize existing evidence on the assessment tools used to evaluate them, the analytical techniques employed to identify them, and the composition of symptom clusters in people with stroke.
A comprehensive literature search was conducted across seven databases (PubMed, EMBASE, APA PsycInfo, CINAHL, Web of Science, China National Knowledge Infrastructure, and Wanfang) for studies published between 2001 and April 2025. Methodological quality was assessed using the JBI Critical Appraisal Checklists. Data were extracted on study characteristics, measurement instruments, analytical techniques, and symptom cluster composition.
Fourteen studies comprising 6556 stroke patients were included. A total of 11 assessment tools and six analytical techniques were identified, with exploratory factor analysis being the most commonly used. Seven common symptom clusters were synthesized: pain and fatigue, somatic movement dysfunction, cognitive impairment, affective disturbance, mood and sleep dysregulation, psychological distress, and gastrointestinal symptoms. The most frequently reported symptom cluster was pain and fatigue. Considerable heterogeneity was found across studies in terms of measurement instruments, analytical techniques, and symptom cluster composition.
This review highlights the methodological inconsistencies and diversity in symptom cluster research in stroke populations. The findings underscore the need for standardized, culturally adaptable assessment tools and longitudinal designs to capture the dynamic nature of symptom clusters. This comprehensive review summarizes common symptom clusters in stroke patients and provides clinicians and researchers with valuable insights to help them develop more effective symptom management strategies and ultimately improve patient outcomes.
PROSPERO: CRD420251069463
The aim of this study was to explore the trajectory of home-based cardiac rehabilitation exercise adherence in patients with coronary heart disease over 12 months and to identify heterogeneous trajectories and their predictors.
A prospective cohort study with 428 coronary heart disease patients was conducted in this study.
The Latent Class Growth Model was adopted to describe exercise adherence trajectories, and heterogeneous adherence trajectory was determined based on the Cox proportional hazards regression model. Predictors were identified using a multivariable logistic regression model. The study was conducted from January 2023 to April 2024.
This study explored five adherence trajectories, including persistent adherence, gradual decline, U-shaped adherence, delayed initiation and consistent non-adherence. Two of these trajectories (gradual decline and consistent non-adherence) were merged and labelled as a heterogeneous adherence trajectory based on association with cardiovascular readmissions. Regression analysis revealed seven independent predictors for the heterogeneous trajectory, covering education level, ejection fraction, C-reactive protein level, frailty, depression, exercise motivation and work conditions.
The identification of distinct adherence trajectories and their predictors highlights the dynamic nature of cardiac rehabilitation engagement. Heterogeneous trajectories (gradual decline and non-adherence) were strongly linked to increased readmission risks, emphasising the need for targeted interventions in high-risk subgroups.
These findings provide a framework for nurses to stratify patients' adherence risks early and personalise rehabilitation strategies. Addressing modifiable predictors (e.g., depression management, frailty mitigation and motivation enhancement) could improve long-term adherence, reduce healthcare burdens from readmissions and optimise resource allocation in cardiac rehabilitation programmes.
The reporting procedure of this study followed the STROBE guidelines.
No patient or public contribution.
To develop and validate a risk prediction model for oral frailty in elderly patients with ischaemic stroke.
A cross-sectional study.
A temporal cohort of 633 elderly isachemic stroke patients from May 2024 to February 2025 was chronologically divided into a training set (n = 443) and validation set (n = 190). Participants were classified into oral frailty and non-oral frailty groups based on the Oral Frailty Index-8. In the training set, feature selection combined least absolute shrinkage and selection operator regression and random forest recursive feature elimination, followed by Nomogram Construction via Binary Logistic Regression. The model underwent internal validation using bootstrap resampling, and its generalizability was assessed with the validation set. The model was comprehensively evaluated using Receiver Operating Characteristic (ROC) curves, the Hosmer-Lemeshow Test, Calibration Plots, and Decision Curve Analysis (DCA).
In both the training and validation sets, the prevalence of oral frailty among elderly ischaemic stroke patients was 63.2% and 62.1%, respectively. Wearing dentures, tooth brushing frequency, dry mouth symptoms, chewing difficulty, swallowing function, oral health literacy, and oral health status were identified as significant predictors of oral frailty. ROC analysis demonstrated strong discriminative ability of the nomogram. The Hosmer-Lemeshow Test confirmed model consistency, and the calibration curve indicated excellent and stable calibration performance. DCA revealed that the model provided significant net clinical benefit in clinical practice. This free, interactive dynamic nomogram is accessible at: https://xiaowen.shinyapps.io/dynnomapp/.
This study presents a reliable, accessible model to assess oral frailty risk in elderly ischaemic stroke patients, facilitating clinical identification of high-risk individuals and providing a scientific foundation for oral health interventions.
The nomogram helps healthcare professionals identify high-risk patients, understand risk factors, and improve oral health management.
TRIPOD-AI checklist.
No patient or public contribution.
Taking a dimensional view, this study aims to understand, among professional caregivers after patient deaths, the symptom distribution and development of the short-term bereavement reaction (SBR) network and the node-level links between the meaning of patient death (MPD) and the SBR network.
A cross-sectional secondary analysis was conducted with existing data from 220 Chinese urban hospital nurses and physicians who experienced the most recent patient death within a month. MPD was measured by the 10 formative items of the meaning of patient death model, and SBR was measured by the Short-term Bereavement Reactions Subscale of the Professional Bereavement Scale. Both Gaussian graphical network analysis and Bayesian network analysis were applied to the SBR network, and Gaussian graphical network analysis was used to estimate the MPD-SBR network.
Frustrated and guilty are central nodes in the regularized partial correlation SBR network. Meanwhile, a traumatic event and failure at work are important bridge nodes between the MPD network and the SBR network. In the Bayesian SBR network, moved by the family's understanding, moved by the family's gratitude and sad mainly drive other nodes.
After a patient death, nurses' and physicians' SBR networks feature professional-dimension symptoms at their core, while they follow ‘personal to professional’ and ‘concrete to abstract’ symptom development patterns. The personal meaning of a traumatic event and the professional meaning of a failure at work play key roles in bridging the MPD and SBR networks, and meanings of both the personal and the professional dimensions can link to professional-dimension reactions.
The manuscript followed the STROBE checklist for reporting cross-sectional studies.
No patient or public contribution.
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