Anxiety subtypes in rural ischaemic stroke survivors: A latent profile analysis

Abstract

Aim To determine the potential profile classes of anxiety reported by ischaemic stroke survivors in rural China, and to explore the characteristics of patients having different types of post‐stroke anxiety. Design A cross‐sectional survey. Methods A cross‐sectional survey was conducted by using convenience sampling to collect data from 661 ischaemic stroke survivors in rural Anyang city, Henan Province, China, from July 2021 to September 2021. The parameters included in the study were the socio‐demographic characteristics, self‐rating anxiety scale (SAS), self‐rating depression scale (SDS) and the Barthel index of daily activity ability. Potential profile analysis was done to recognize subgroups of post‐stroke anxiety. The Chi‐square test was performed to explore the characteristics of individuals with different types of post‐stroke anxiety. Results The model fitting indices of stroke survivors supported three classes of anxiety models which were as follows: (a) Class 1, low‐level, stable group (65.3%, N = 431); (b) Class 2, moderate‐level, unstable group (17.9%, N = 118) and (c) Class 3, high‐level, stable group (16.9%, N = 112). The risk factors associated with post‐stroke anxiety were female patients, lower levels of education, living alone, lower monthly household income, other chronic diseases, impaired daily activity ability and depression. Conclusions This study identified three different subgroups of post‐ischaemic stroke anxiety and their characteristics in patients in rural China. Impact This study has significance in providing evidence for the development of targeted intervention measures to reduce negative emotions in different subgroups of post‐stroke anxiety patients. Patient or Public Contribution In this study, the researchers arranged the time of questionnaire collection with the village committee in advance, gathered the patients to the village committee for face‐to‐face questionnaire survey and collected the household data of the patients with mobility difficulties.

Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients

“…While many factors like recurrent stroke, the most important feature that predicted PSAMO in our study, were non-modifiable, some risk factors might be amenable to interventions. Lower educational level and household income were risk factors for PSAMO in our study, as well as in others [61][62][63][64]. This is also coherent with Fast et al's study [25], which also used SHAP to interpret its GBT model and its top important features are years of education and sex.…”

“…The most discriminative features in the dataset as assessed by SHAP (Figure 4) were consistent with the findings of prior clinical studies [61][62][63][64][65][66][67][68][69]. While many factors like recurrent stroke, the most important feature that predicted PSAMO in our study, were non-modifiable, some risk factors might be amenable to interventions.…”

“…This is also coherent with Fast et al’s study [ 25 ], which also used SHAP to interpret its GBT model and its top important features are years of education and sex. Higher education enables patients to have clearer insights into their disease, participate more actively in their own care, and better manage their emotions, all of which may reduce the risk of PSAMO [ 63 , 65 ]. Lower income has been found to be associated with less patient participation in after-stroke care [ 66 ], possibly due to a lack of access to appropriate care.…”

Automated risk prediction of post-stroke adverse mental outcomes using artificial intelligence and machine learning

“…The higher education levels meant that stroke patients are able to understand their disease better and hence take a less passive approach in handling it. Higher educated patients could also potentially deal with their emotions better, reducing the risk of PSAMO (Lin et al, 2019;Zhang et al, 2023). Low income has been found to be associated with less participation following stroke (Egan et al, 2015).…”