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Association Between Stroke Characteristics and Post-Stroke Fatigue: A Meta-Analysis
Correspondence Address: Source of Support: None, Conflict of Interest: None DOI: 10.4103/0028-3886.344612
Keywords: Meta-analysis, post-stroke fatigue, stroke characteristics
Stroke is one of the major factors that leads to disability,[1] and is commonly categorized as either hemorrhagic stroke or ischemic stroke. With the increase of stroke incidence and survival rate,[2],[3] great efforts are necessary to properly manage post-stroke-related long-term complications. Fatigue after stroke is a common and highly debilitating symptom. A recent systematic review reported a prevalence of post-stroke fatigue (PSF) ranging from 25% to 85%.[4] Previous studies have revealed[5] that the recovery of work and activities of daily living was significantly affected by PSF, and PSF could independently predict a shorter lifespan[6] and poorer functional outcomes after stroke.[7] The cause of PSF is highly complex, and the mechanism is largely unclear. Several reviews have examined the relationship between PSF and many factors, including demographic characteristics, such as gender and age; psychological factors, such as depression and anxiety; stroke type and location; sleep disorders; and white matter lesions.[8],[9],[10] Among these factors, a previous meta-analysis revealed that PSF was significantly correlated with depressive symptoms and tended to be associated with anxiety.[11] However, the association between stroke characteristics (stroke type and location) and PSF remains unclear. Some studies have found that PSF was associated with infarcts locations including basal ganglia, thalamus, internal capsule, brainstem lesion, and corona radiata,[12],[13],[14],[15] whereas others have found no such association.[16],[17] Compared to fatigue after cerebral hemorrhage, fatigue after ischemic stroke was more severe;[18] however, another study reported no difference in PSF between hemorrhagic and ischemic stroke.[15] Therefore, the main intention of the present study is to determine whether PSF is associated with stroke characteristics (stroke type and location). These results will promote the comprehension of the onset and progression of PSF.
Search strategy We conducted a meta-analysis in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.[19] Electronic databases (PubMed, Web of Science, Cochrane Library) were systematically searched from the inception of all databases up to July 9, 2019. The search terms (“cerebral hemorrhage” or “cerebral infarction” or “cerebral ischemia” or “stroke” or “ poststroke” or “post-stroke” and fatigue) were used for identifying related articles. Two investigators independently discarded irrelevant studies and evaluated potentially eligible studies to be included in the meta-analysis. Inclusion criteria The inclusion criteria were as follows: 1. The studies recruited people with stroke (whether first or recurrent, ischemic or hemorrhagic). 2. fatigue was measured by a dedicated fatigue scale at any time post-stroke; 3. statistics that could be used to estimate the associations between PSF and stroke subtype (ischemic or hemorrhagic), and/or stroke lesion location (site and side) defined radiologically or clinically were provided; 4. the publication was written in English. Study types included case-control studies, cross-sectional studies, or cohort studies. Exclusion criteria Studies were eliminated if they were not related to strokes, such as studies examining transient ischemic attack or traumatic brain injury, or if they provided incomplete data that could not be used to analyze the relationship between PSF and stroke characteristics. Case reports, duplicate publications, and reviews were removed. In case where more than one study reported data from the same participant cohort, we included only the most exhaustive information study. Data extraction and quality assessment The following data from the included studies were independently extracted by two researchers: stroke type and location, the total number of participants, study design, and the number of fatigued participants, publication date, first author of each article. The Newcastle–Ottawa Scale (NOS) scale, which is used to assess the quality of included studies, was applied to our analysis. The scale has a total score ranging from 0 to 9, and studies with an overall NOS score ≥7 were considered high-quality ones studies.[20] Statistical analysis The meta-analysis was performed using Review Manager 5.3 software, and odds ratios (ORs) and 95% confidence intervals (95% CIs) were applied to represent the combined effect value of each study. Cochran's Q (P < 0.10) and the I2 statistic (>50% was thought to be high) were used to test statistical heterogeneity across the various studies. If heterogeneity existed among studies (P > 0.10, or I2 >50%), the random effects (RE) model was used. Otherwise, the fixed effects (FE) model was applied.[21] Potential publication bias was assessed when the number of included studies was 10 or more.[22] We don't conduct the analysis of publication bias for only eight studies included.
Study characteristics A total of 1683 relevant studies were initially retrieved. After duplicate papers, reviews, and papers based on the title and abstract were excluded, 214 papers remained for full-text review. Finally, eight studies[13],[15],[23],[24],[25],[26],[27],[28] involving 1816 patients were included [shown in [Figure 1]]. Overall, three studies discussed the association between stroke type and PSF,[15],[23],[27] and five studies investigated the relationship between stroke location and PSF.[13],[24],[25],[26],[28] The essential features of the eligible studies are displayed in [Table 1].
Association between stroke type and post-stroke fatigue This meta-analysis included three studies[15],[23],[27] that discussed the association between stroke type and PSF. The FE model was used because the heterogeneity test (I2 = 0%, P = 0.37) indicated no significant between-study heterogeneity. The analysis showed a significant association between stroke type and PSF (OR = 2.42, 95% CI = [1.27, 4.61], P = 0.007 < 0.01) [shown in [Figure 2]].
Association between stroke location and post-stroke fatigue Three included studies[13],[26],[28] examining the associations between PSF and cortical/subcortical stroke (white matter, basal ganglia, thalamus), subcortical stroke/infratentorial stroke (brain stem, cerebellum), and basal ganglia stroke and other stroke locations (cortex, white matter, thalamus, brainstem, cerebellum) were analyzed. The RE model was adopted because of the significant level of between-study heterogeneity (I2 = 86%, P = 0.007 < 0.01; I2 = 84%, P = 0.002 < 0.01; I2 = 84%, P = 0.002 < 0.01, respectively). The results indicated that PSF was not significantly associated with cortical/subcortical stroke (OR = 1.06, 95% CI = [0.42, 2.69], P = 0.90 > 0.01) [shown in [Figure 3]a], subcortical stroke/infratentorial infarcts (OR = 0.97, 95% CI = [0.35, 2.97], P = 0.95 > 0.01) [shown in [Figure 3]b], or basal ganglia stroke and other stroke locations (OR = 1.13, 95% CI = [0.59, 2.18], P = 0.71 > 0.01) [shown in [Figure 3]c].
The associations between PSF and left/right hemisphere stroke and unilateral/bilateral hemisphere stroke were examined by four[13],[24],[25],[28] and three[13],[24],[25] studies, respectively. The FE model was used because of a lack of significant heterogeneity (I2 = 0%, P = 0.37 > 0.01, I2 = 0%, P = 0.76 > 0.01, respectively). There was no significant association between PSF and left/right hemisphere infarcts (OR = 0.97, 95% CI = [0.73, 1.29], P = 0.85 > 0.01) [shown in [Figure 4]a] or unilateral/bilateral hemisphere stroke (OR = 1.02, 95% CI = [0.76, 1.36], P = 0.91 > 0.01) [shown in [Figure 4]b].
Although Kutlubaev et al.[29] summarized associations between PSF and stroke characteristics, it was a systematic review with no specific data and cannot estimate the strength of associations. To our knowledge, our meta-analysis is the first to explore whether PSF is associated with stroke characteristics (stroke type and stroke location). The analysis demonstrated that PSF was closely relevant to stroke type and had no significant relationship with stroke location. This review may contribute to the understanding of the onset and progression of PSF; the findings indicated that PSF is a complex, heterogeneous syndrome and that stroke characteristics may only play a very small role in the risk of developing PSF. However, the number of studies included in our analysis was small, therefore our results should be interpreted with caution. This study showed that compared to fatigue after hemorrhagic stroke, the risk of fatigue after ischemic stroke was higher. The underlying mechanism of this effect is unknown. A previous study[6] reported that advanced aging was related to a greater risk of PSF. It's also well known, on average, that ischemic stroke patients are older than hemorrhagic stroke patients, which may lead to a higher risk of PSF after ischemic stroke. No significant association between PSF and stroke location was observed in this study. Some studies have detected associations between PSF and stroke location, including the basal ganglia,[30] infratentorial lesions,[28] right side lesions, and thalamic and brainstem lesions.[12] Some authors have hypothesized that basal ganglia strokes and thalamus and brainstem lesions were more inclined to cause PSF, and the main possible reason was disturbances in the limbic-motor integration networks[30] and damage to the ascending reticular activating system, which led to mild disorders in arousal and changes in attention.[31] These findings have some implications for exploring PSF, but it should be noted that the sample sizes of those researches were small, and there may have been selective bias. We separately explored the relationships between PSF and cortical/subcortical stroke, subcortical/infratentorial stroke, basal ganglia/other stroke locations, left/right hemisphere stroke, and unilateral/bilateral hemisphere stroke and found no significant association between PSF and stroke location. In addition, another large-sample study[6] did not show a relationship between PSF and stroke lesions, indicating that fatigue may have a “central” origin; that is, it might be a direct consequence of stroke. Whether the development of PSF is significantly influenced by lesion location is still a controversial issue, and more clinical studies will be needed to clarify the above argument in the future. Several potential limitations of our present study should be stated. First, the number of included studies was low; thus, we failed to analyze publication bias due to the limited amount of data. Besides, the tools for assessing PSF and the time points for screening PSF were different across studies. Finally, other confounding factors that may influence the presence of PSF, such as psychological factors, age, sleep disorders, and cognitive impairment, were not considered in our analysis.
In conclusion, our meta-analysis indicated that PSF was closely related to stroke type and had no significant relationship with stroke location. However, the findings should be interpreted cautiously. Thus, we suggest an updated meta-analysis on this subject when more comprehensive studies that explore the relationship between PSF and stroke characteristics are available. Financial support and sponsorship This work was funded by the Shanghai Municipal Health Commission, grant number 2020YJZX0109 and the Shanghai Pujiang Program, grant number 2019PJD013 and National Natural Science Foundation of China (NSFC), grant number 81471103. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Conflicts of interest There are no conflicts of interest.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1]
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