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Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis
PURPOSE: To identify latent classes of acute stroke patients with distinct experiences with the symptom clusters of depression, anxiety, fatigue, sleep disturbance, and pain symptoms and assess, if the selected variables determine a symptom-cluster experience in acute stroke patients. PARTICIPANTS A...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977864/ https://www.ncbi.nlm.nih.gov/pubmed/35386423 http://dx.doi.org/10.2147/JPR.S350727 |
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author | Dong, Xiaofang Yang, Sen Guo, Yuanli Lv, Peihua Liu, Yanjin |
author_facet | Dong, Xiaofang Yang, Sen Guo, Yuanli Lv, Peihua Liu, Yanjin |
author_sort | Dong, Xiaofang |
collection | PubMed |
description | PURPOSE: To identify latent classes of acute stroke patients with distinct experiences with the symptom clusters of depression, anxiety, fatigue, sleep disturbance, and pain symptoms and assess, if the selected variables determine a symptom-cluster experience in acute stroke patients. PARTICIPANTS AND METHODS: A sample of 690 participants were collected from July 2020 to December 2020 in a cross-sectional descriptive study. Latent class analysis was conducted to distinguish different clusters of acute stroke participants who experienced five patient-reported symptoms. Furthermore, multinomial logistic regression was selected to verify the influencing indicators of each subgroup, with selected socio-demographic variables, clinical characteristics, self-efficacy, and perceived social support as independent variables and the different latent classes as the dependent variable. RESULTS: Three latent classes, named “all high symptom,” “high psychological disorder,” and “all low symptom,” were identified, accounting for 9.6%, 26.3%, and 64.1% of symptom clusters, respectively. Patients in the “all high symptom” and “high psychological disorder” classes reported significantly lower quality of life (F=40.21, p <0.05). Female gender, younger age, higher National Institutes of Health Stroke Scale scores, and lower self-efficacy and perceived social support were risk factors associated with the “high psychological disorder” class. Younger patients with lower self-efficacy and perceived social support were more likely to be in the “all high symptom” class. CONCLUSION: This study identified latent classes of acute stroke patients that can be used in predicting symptom-cluster experiences following a stroke. Also, the ability to characterize subgroups of patients with distinct symptom experiences helps identify high-risk patients. Focusing on symptom clusters in clinical practice can inspire us to create effective targeted interventions for subgroups of stroke patients suffering from the same symptom cluster. |
format | Online Article Text |
id | pubmed-8977864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-89778642022-04-05 Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis Dong, Xiaofang Yang, Sen Guo, Yuanli Lv, Peihua Liu, Yanjin J Pain Res Original Research PURPOSE: To identify latent classes of acute stroke patients with distinct experiences with the symptom clusters of depression, anxiety, fatigue, sleep disturbance, and pain symptoms and assess, if the selected variables determine a symptom-cluster experience in acute stroke patients. PARTICIPANTS AND METHODS: A sample of 690 participants were collected from July 2020 to December 2020 in a cross-sectional descriptive study. Latent class analysis was conducted to distinguish different clusters of acute stroke participants who experienced five patient-reported symptoms. Furthermore, multinomial logistic regression was selected to verify the influencing indicators of each subgroup, with selected socio-demographic variables, clinical characteristics, self-efficacy, and perceived social support as independent variables and the different latent classes as the dependent variable. RESULTS: Three latent classes, named “all high symptom,” “high psychological disorder,” and “all low symptom,” were identified, accounting for 9.6%, 26.3%, and 64.1% of symptom clusters, respectively. Patients in the “all high symptom” and “high psychological disorder” classes reported significantly lower quality of life (F=40.21, p <0.05). Female gender, younger age, higher National Institutes of Health Stroke Scale scores, and lower self-efficacy and perceived social support were risk factors associated with the “high psychological disorder” class. Younger patients with lower self-efficacy and perceived social support were more likely to be in the “all high symptom” class. CONCLUSION: This study identified latent classes of acute stroke patients that can be used in predicting symptom-cluster experiences following a stroke. Also, the ability to characterize subgroups of patients with distinct symptom experiences helps identify high-risk patients. Focusing on symptom clusters in clinical practice can inspire us to create effective targeted interventions for subgroups of stroke patients suffering from the same symptom cluster. Dove 2022-03-25 /pmc/articles/PMC8977864/ /pubmed/35386423 http://dx.doi.org/10.2147/JPR.S350727 Text en © 2022 Dong et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Dong, Xiaofang Yang, Sen Guo, Yuanli Lv, Peihua Liu, Yanjin Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis |
title | Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis |
title_full | Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis |
title_fullStr | Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis |
title_full_unstemmed | Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis |
title_short | Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis |
title_sort | exploring psychoneurological symptom clusters in acute stroke patients: a latent class analysis |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977864/ https://www.ncbi.nlm.nih.gov/pubmed/35386423 http://dx.doi.org/10.2147/JPR.S350727 |
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