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Poor mental health after stroke is common and complex. We aimed to identify predictors of poor wellbeing and to examine the overlap of poor wellbeing, fatigue, and depression. Consecutive first-ever ischemic stroke-patients filled in questionnaires on wellbeing, fatigue, and depression at baseline and at one and six months. The World Health Organization 5-Item Wellbeing-Index (WHO-5), the Major Depression Inventory, and the Multidimensional Fatigue Inventory were used. Patients were genotyped according to serotonin-transporter gene polymorphisms. Multivariable logistic regression was used to identify potential predictors of poor wellbeing (WHO-5 score <50). Overlap between wellbeing, fatigue, and depression was examined using an Euler diagram. We included 919 patients. The prevalence of poor wellbeing was 279 (30.4%) six months after stroke. Living alone at stroke onset was the strongest predictor of poor wellbeing with a mutually adjusted odds ratio of 1.53 (95% confidence interval (CI): 1.03 to 2.28) at one month and 1.77 (CI: 1.13 to 2.76) at six months. Severe stroke at admission also predicted poor wellbeing at six months. Abnormal fatigue occurred in half and incorporated almost all patients with poor wellbeing. Less than 5% fulfilled the criteria for depression at any point and almost all of these patients had poor wellbeing and abnormal fatigue. Antidepressants were used by 292 (31.8%) during follow-up. Cognitive impairment was not measured and could interact with wellbeing post-stroke. Living alone strongly predicted poor wellbeing after stroke. Satisfactory mental health-recovery seems to require psychosocial interventions when indicated in combination with antidepressant treatment. Copyright © 2019. Published by Elsevier B.V.

Citation

Andreas Gammelgaard Damsbo, Kristian Lundsgaard Kraglund, Henriette Nørmølle Buttenschøn, Søren Paaske Johnsen, Grethe Andersen, Janne Kaergaard Mortensen. Predictors for wellbeing and characteristics of mental health after stroke. Journal of affective disorders. 2020 Mar 01;264:358-364

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PMID: 32056772

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