CT and Clinical Predictors of Fatigue at One Month after Stroke

Abstract

Background: Fatigue is a common and distressing consequence of stroke, and the aetiology of post-stroke fatigue (PSF) is poorly understood. It is unclear whether chronic brain changes [cerebral atrophy and white matter lesions (WML)], stroke lesion location or certain clinical features are related to its development. The aim of this study was to identify, in patients with acute stroke, whether features in different brain regions on routine CT imaging or routinely collected clinical features predicted PSF at 1 month. Methods: In total, 107 patients (62% male) with acute ischaemic or haemorrhagic stroke were assessed for fatigue (Fatigue Assessment Scale), anxiety and depression (Hospital Anxiety and Depression Scale) at 1 month. Admission brain CT was rated using a structured scoring system for (i) severity of atrophy and (ii) severity of WML in different regions of the brain, and (iii) site of acute and previous vascular lesions. Results: Cerebral atrophy of mild or greater severity was present in 84 patients (77.5%) and WML of mild or greater severity was present in 54 patients (50.5%) in at least one of the evaluated brain regions. There was no association between PSF and severity of atrophy or WML, or presence of acute or previous vascular lesions. We used the Oxfordshire Community Stroke Project (OCSP) classification to explore the possible influence of lesion location because a minority of the patients (37.4%) had visible acute lesions. Fatigue scores were higher in patients with clinically diagnosed posterior strokes (p = 0.046), in females (p = 0.05) and in those with higher depression and anxiety scores (ρ = 0.52; p < 0.001 and ρ = 0.49; p < 0.001, respectively). Structural CT variables were not significant predictors of fatigue (log FAS) in a linear regression which controlled for age, sex, pre-stroke fatigue, OCSP classification, depression and anxiety. The significant predictors of fatigue were depression (β = 0.30; p = 0.007) and anxiety (β = 0.28; p = 0.013; adjusted R² = 0.254). Stroke subtype (according to the OCSP classification) was marginally predictive (β = 0.17; p = 0.05) and sex was not statistically significant (β = 0.15; p = 0.08). Conclusions: Features on routine post-stroke CT do not appear to associate with fatigue at 1 month. However, clinically diagnosed posterior strokes as well as female gender, anxiety and depression may be linked with fatigue. Therefore, clinical vigilance rather than CT features should be used to predict fatigue early after stroke. Further research is needed in this area to establish whether biological mechanisms underlie the development of PSF.