Investigating post-stroke fatigue: An individual participant data meta-analysis

Investigating but NO SOLUTION. You'll have to figure this out yourself. 

Investigating post-stroke fatigue: An individual participant data meta-analysis

Author links open overlay panelToby B.Cumming^abAi BengYeo^cJodieMarquez^cLeonidChurilov^aJean-MarieAnnoni^dUmaruBadaru^eNastaranGhotbi^fJoeHarbison^gGertKwakkel^hAnnersLerdalⁱRogerMills^jHalvorNaess^kHaraldNyland^lArleneSchmid^mWai KwongTangⁿBenjaminTseng^oIngridvan de Port^pGillianMead^qCoralieEnglish^bc

https://doi.org/10.1016/j.jpsychores.2018.08.006Get rights and content

Highlights

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The first use of individual participant data meta-analysis in post-stroke fatigue.

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Confirms links between fatigue after stroke and female sex, depression, disability.

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Novel finding of greater fatigue with increased time since stroke.

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A non-linear association between post-stroke fatigue and age.

Abstract

Objective

The prevalence of post-stroke fatigue differs widely across studies, and reasons for such divergence are unclear. We aimed to collate individual data on post-stroke fatigue from multiple studies to facilitate high-powered meta-analysis, thus increasing our understanding of this complex phenomenon.

Methods

We conducted an Individual Participant Data (IPD) meta-analysis on post-stroke fatigue and its associated factors. The starting point was our 2016 systematic review and meta-analysis of post-stroke fatigue prevalence, which included 24 studies that used the Fatigue Severity Scale (FSS). Study authors were asked to provide anonymised raw data on the following pre-identified variables: (i) FSS score, (ii) age, (iii) sex, (iv) time post-stroke, (v) depressive symptoms, (vi) stroke severity, (vii) disability, and (viii) stroke type. Linear regression analyses with FSS total score as the dependent variable, clustered by study, were conducted.

Results

We obtained data from 14 of the 24 studies, and 12 datasets were suitable for IPD meta-analysis (total n = 2102). Higher levels of fatigue were independently associated with female sex (coeff. = 2.13, 95% CI 0.44–3.82, p = 0.023), depressive symptoms (coeff. = 7.90, 95% CI 1.76–14.04, p = 0.021), longer time since stroke (coeff. = 10.38, 95% CI 4.35–16.41, p = 0.007) and greater disability (coeff. = 4.16, 95% CI 1.52–6.81, p = 0.010). While there was no linear association between fatigue and age, a cubic relationship was identified (p < 0.001), with fatigue peaks in mid-life and the oldest old.

Conclusion

Use of IPD meta-analysis gave us the power to identify novel factors associated with fatigue, such as longer time since stroke, as well as a non-linear relationship with age.