Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional Analysis
BackgroundPoststroke cognitive impairment (PSCI) is a common and debilitating complication that affects stroke survivors, impacting memory, attention, and executive function. Despite its prevalence, the factors contributing to PSCI remain unclear, with limited insights into h...
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JMIR Publications
2024-11-01
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| author | Wu Zhou HaiXia Feng Hua Tao Hui Sun TianTian Zhang QingXia Wang Li Zhang |
| author_facet | Wu Zhou HaiXia Feng Hua Tao Hui Sun TianTian Zhang QingXia Wang Li Zhang |
| author_sort | Wu Zhou |
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BackgroundPoststroke cognitive impairment (PSCI) is a common and debilitating complication that affects stroke survivors, impacting memory, attention, and executive function. Despite its prevalence, the factors contributing to PSCI remain unclear, with limited insights into how demographic and clinical variables influence cognitive outcomes.
ObjectiveThis study investigates the incidence of cognitive impairment in patients with stroke and examines key demographic and clinical factors, such as age, gender, and education level, which contribute to cognitive decline. The aim is to provide a deeper understanding of PSCI to inform early intervention strategies for improving patient outcomes.
MethodsA cross-sectional study was conducted on 305 patients with ischemic stroke admitted to Zhongda Hospital, Southeast University, from January 2019 to September 2022. Cognitive function was assessed using the Mini-Mental State Examination (MMSE) within 72 hours of hospital admission. Demographic information, including age, gender, and education level, were collected. Statistical analyses were performed using chi-square tests, independent t tests, and multivariate regression to assess the relationship between cognitive function and key variables. Pearson correlation analysis explored associations among age, education, and MMSE scores.
ResultsAmong the 305 patients with stroke, 16.7% (n=51) were diagnosed with cognitive impairment based on MMSE scores. The prevalence of cognitive impairment was slightly higher in males (17.6%, n=159) than females (15.8%, n=146), but this difference was not statistically significant. A strong negative correlation was found between MMSE scores and age (r=–0.32; P<.01), indicating that older patients had lower cognitive function. Education level showed a positive correlation with MMSE scores (r=0.41; P<.01), with patients with higher educational attainment demonstrating better cognitive outcomes. Cognitive function showed a marked decline in patients older than 60 years, particularly in domains such as memory, attention, and language skills.
ConclusionsThis study confirms that age and education are significant factors in determining cognitive outcomes after stroke. The results align with existing literature showing that cognitive function declines with age, while higher educational attainment serves as a protective factor. The findings suggest that individuals with greater cognitive reserve, often linked to higher education, are better equipped to cope with the impact of brain injury. However, the study’s reliance on MMSE may have limited its ability to detect domain-specific impairments. Future studies should consider using more sensitive cognitive tools, such as the Montreal Cognitive Assessment (MoCA), to provide a more comprehensive evaluation of PSCI. Cognitive impairment is prevalent among stroke survivors, with age and education level being key factors influencing outcomes. These findings underscore the importance of early detection and targeted interventions to mitigate cognitive decline. Further research with larger samples and more sensitive cognitive assessments is needed to fully understand PSCI and improve rehabilitation strategies for patients with stroke. |
| format | Article |
| id | doaj-art-d60b5c9428514c7ab38ce908e6c0c9e0 |
| institution | Kabale University |
| issn | 2561-326X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | JMIR Publications |
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| series | JMIR Formative Research |
| spelling | doaj-art-d60b5c9428514c7ab38ce908e6c0c9e02024-11-19T15:46:39ZengJMIR PublicationsJMIR Formative Research2561-326X2024-11-018e5957210.2196/59572Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional AnalysisWu Zhouhttps://orcid.org/0009-0006-6872-4680HaiXia Fenghttps://orcid.org/0000-0002-1718-0971Hua Taohttps://orcid.org/0009-0007-2349-4532Hui Sunhttps://orcid.org/0009-0000-1350-5647TianTian Zhanghttps://orcid.org/0009-0003-5738-2548QingXia Wanghttps://orcid.org/0009-0006-9275-0651Li Zhanghttps://orcid.org/0009-0004-1398-5184 BackgroundPoststroke cognitive impairment (PSCI) is a common and debilitating complication that affects stroke survivors, impacting memory, attention, and executive function. Despite its prevalence, the factors contributing to PSCI remain unclear, with limited insights into how demographic and clinical variables influence cognitive outcomes. ObjectiveThis study investigates the incidence of cognitive impairment in patients with stroke and examines key demographic and clinical factors, such as age, gender, and education level, which contribute to cognitive decline. The aim is to provide a deeper understanding of PSCI to inform early intervention strategies for improving patient outcomes. MethodsA cross-sectional study was conducted on 305 patients with ischemic stroke admitted to Zhongda Hospital, Southeast University, from January 2019 to September 2022. Cognitive function was assessed using the Mini-Mental State Examination (MMSE) within 72 hours of hospital admission. Demographic information, including age, gender, and education level, were collected. Statistical analyses were performed using chi-square tests, independent t tests, and multivariate regression to assess the relationship between cognitive function and key variables. Pearson correlation analysis explored associations among age, education, and MMSE scores. ResultsAmong the 305 patients with stroke, 16.7% (n=51) were diagnosed with cognitive impairment based on MMSE scores. The prevalence of cognitive impairment was slightly higher in males (17.6%, n=159) than females (15.8%, n=146), but this difference was not statistically significant. A strong negative correlation was found between MMSE scores and age (r=–0.32; P<.01), indicating that older patients had lower cognitive function. Education level showed a positive correlation with MMSE scores (r=0.41; P<.01), with patients with higher educational attainment demonstrating better cognitive outcomes. Cognitive function showed a marked decline in patients older than 60 years, particularly in domains such as memory, attention, and language skills. ConclusionsThis study confirms that age and education are significant factors in determining cognitive outcomes after stroke. The results align with existing literature showing that cognitive function declines with age, while higher educational attainment serves as a protective factor. The findings suggest that individuals with greater cognitive reserve, often linked to higher education, are better equipped to cope with the impact of brain injury. However, the study’s reliance on MMSE may have limited its ability to detect domain-specific impairments. Future studies should consider using more sensitive cognitive tools, such as the Montreal Cognitive Assessment (MoCA), to provide a more comprehensive evaluation of PSCI. Cognitive impairment is prevalent among stroke survivors, with age and education level being key factors influencing outcomes. These findings underscore the importance of early detection and targeted interventions to mitigate cognitive decline. Further research with larger samples and more sensitive cognitive assessments is needed to fully understand PSCI and improve rehabilitation strategies for patients with stroke.https://formative.jmir.org/2024/1/e59572 |
| spellingShingle | Wu Zhou HaiXia Feng Hua Tao Hui Sun TianTian Zhang QingXia Wang Li Zhang Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional Analysis JMIR Formative Research |
| title | Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional Analysis |
| title_full | Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional Analysis |
| title_fullStr | Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional Analysis |
| title_full_unstemmed | Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional Analysis |
| title_short | Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional Analysis |
| title_sort | factors influencing poststroke cognitive dysfunction cross sectional analysis |
| url | https://formative.jmir.org/2024/1/e59572 |
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