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

Toby B. Cumming, Ai Beng Yeo, Jodie Marquez, Leonid Churilov, Jean Marie Annoni, Umaru Badaru, Nastaran Ghotbi, Joe Harbison, Gert Kwakkel, Anners Lerdal, Roger Mills, Halvor Naess, Harald Nyland, Arlene Schmid, Wai Kwong Tang, Benjamin Tseng, Ingrid van de Port, Gillian Mead, Coralie English

    Research output: Contribution to JournalArticleAcademicpeer-review

    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.

    Original languageEnglish
    Pages (from-to)107-112
    Number of pages6
    JournalJournal of Psychosomatic Research
    Volume113
    DOIs
    Publication statusPublished - 1 Oct 2018

    Fingerprint

    Fatigue
    Meta-Analysis
    Stroke
    Depression
    Linear Models
    Regression Analysis

    Keywords

    • Depression
    • Fatigue
    • Fatigue Severity Scale
    • Individual data
    • Meta-analysis
    • Stroke

    Cite this

    Cumming, T. B., Yeo, A. B., Marquez, J., Churilov, L., Annoni, J. M., Badaru, U., ... English, C. (2018). Investigating post-stroke fatigue: An individual participant data meta-analysis. Journal of Psychosomatic Research, 113, 107-112. https://doi.org/10.1016/j.jpsychores.2018.08.006
    Cumming, Toby B. ; Yeo, Ai Beng ; Marquez, Jodie ; Churilov, Leonid ; Annoni, Jean Marie ; Badaru, Umaru ; Ghotbi, Nastaran ; Harbison, Joe ; Kwakkel, Gert ; Lerdal, Anners ; Mills, Roger ; Naess, Halvor ; Nyland, Harald ; Schmid, Arlene ; Tang, Wai Kwong ; Tseng, Benjamin ; van de Port, Ingrid ; Mead, Gillian ; English, Coralie. / Investigating post-stroke fatigue : An individual participant data meta-analysis. In: Journal of Psychosomatic Research. 2018 ; Vol. 113. pp. 107-112.
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    title = "Investigating post-stroke fatigue: An individual participant data meta-analysis",
    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.",
    keywords = "Depression, Fatigue, Fatigue Severity Scale, Individual data, Meta-analysis, Stroke",
    author = "Cumming, {Toby B.} and Yeo, {Ai Beng} and Jodie Marquez and Leonid Churilov and Annoni, {Jean Marie} and Umaru Badaru and Nastaran Ghotbi and Joe Harbison and Gert Kwakkel and Anners Lerdal and Roger Mills and Halvor Naess and Harald Nyland and Arlene Schmid and Tang, {Wai Kwong} and Benjamin Tseng and {van de Port}, Ingrid and Gillian Mead and Coralie English",
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    Cumming, TB, Yeo, AB, Marquez, J, Churilov, L, Annoni, JM, Badaru, U, Ghotbi, N, Harbison, J, Kwakkel, G, Lerdal, A, Mills, R, Naess, H, Nyland, H, Schmid, A, Tang, WK, Tseng, B, van de Port, I, Mead, G & English, C 2018, 'Investigating post-stroke fatigue: An individual participant data meta-analysis' Journal of Psychosomatic Research, vol. 113, pp. 107-112. https://doi.org/10.1016/j.jpsychores.2018.08.006

    Investigating post-stroke fatigue : An individual participant data meta-analysis. / Cumming, Toby B.; Yeo, Ai Beng; Marquez, Jodie; Churilov, Leonid; Annoni, Jean Marie; Badaru, Umaru; Ghotbi, Nastaran; Harbison, Joe; Kwakkel, Gert; Lerdal, Anners; Mills, Roger; Naess, Halvor; Nyland, Harald; Schmid, Arlene; Tang, Wai Kwong; Tseng, Benjamin; van de Port, Ingrid; Mead, Gillian; English, Coralie.

    In: Journal of Psychosomatic Research, Vol. 113, 01.10.2018, p. 107-112.

    Research output: Contribution to JournalArticleAcademicpeer-review

    TY - JOUR

    T1 - Investigating post-stroke fatigue

    T2 - An individual participant data meta-analysis

    AU - Cumming, Toby B.

    AU - Yeo, Ai Beng

    AU - Marquez, Jodie

    AU - Churilov, Leonid

    AU - Annoni, Jean Marie

    AU - Badaru, Umaru

    AU - Ghotbi, Nastaran

    AU - Harbison, Joe

    AU - Kwakkel, Gert

    AU - Lerdal, Anners

    AU - Mills, Roger

    AU - Naess, Halvor

    AU - Nyland, Harald

    AU - Schmid, Arlene

    AU - Tang, Wai Kwong

    AU - Tseng, Benjamin

    AU - van de Port, Ingrid

    AU - Mead, Gillian

    AU - English, Coralie

    PY - 2018/10/1

    Y1 - 2018/10/1

    N2 - 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.

    AB - 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.

    KW - Depression

    KW - Fatigue

    KW - Fatigue Severity Scale

    KW - Individual data

    KW - Meta-analysis

    KW - Stroke

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