Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-26T13:08:25.640Z Has data issue: false hasContentIssue false
  • English
  • Français

Prevalence, predictors, and prognoses of prestroke neuropsychiatric symptoms at 3 months poststroke

Published online by Cambridge University Press:  30 December 2020

Akin Ojagbemi ,
Toyin Bello ,
Mayowa Owolabi  and
Olusegun Baiyewu
Akin Ojagbemi
Affiliation:
Department of Psychiatry, World Health Organization (WHO) Collaborating Centre for Research and Training in Mental health, Neuroscience, and Substance Abuse, College of Medicine, University of Ibadan, Ibadan, Nigeria Department of Psychiatry, College of Medicine, University of Ibadan, Ibadan, Nigeria
Toyin Bello
Affiliation:
Department of Psychiatry, World Health Organization (WHO) Collaborating Centre for Research and Training in Mental health, Neuroscience, and Substance Abuse, College of Medicine, University of Ibadan, Ibadan, Nigeria
Mayowa Owolabi
Affiliation:
Division of Neurology, Department of Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
Olusegun Baiyewu*
Affiliation:
Department of Psychiatry, College of Medicine, University of Ibadan, Ibadan, Nigeria
*
Correspondence should be addressed to: Olusegun Baiyewu, Department of Psychiatry, College of Medicine, University of Ibadan, P.M.B 5017 (G.P.O), Ibadan, Nigeria. Phone: +2348036737171; Fax: 346546. Email: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives:

Prior neuropsychiatric disturbances are risk factors for stroke. There is a knowledge gap on the predictors of prestroke psychopathology, as well as their association with stroke outcomes in survivors living in low- and middle-income countries (LMICs). We estimated prevalence, predictors, and association of prestroke neuropsychiatric symptoms with poststroke depression (PSD), disability, and mortality.

Design:

Prospective observation.

Setting:

Nigeria.

Participants:

Adult ischemic and hemorrhagic stroke survivors.

Measurements:

Prestroke psychopathology were ascertained using the Neuropsychiatric Inventory Questionnaire (NPI-Q). Outcomes were assessed using validated tools, including the Centre for Epidemiologic Studies – Depression Scale (CES-D 10) and modified Rankin scale (mRS). Independent associations were investigated using regression models with Bonferroni corrections, and presented as standardized mean differences (SMD) and odds ratios (OR) within 95% confidence intervals (CI).

Results:

Among 150 participants, prestroke neuropsychiatric symptoms were found in 78 (52%). In multivariate logistic regression analyses, prestroke sleep disturbance was associated with systemic hypertension (OR = 5.39, 95% CI = 1.70–17.08). Prestroke neuropsychiatric symptoms independently predicted worse motor disability scores (SMD = 0.92, 95% CI = 0.21–1.62) and greater odds of poststroke mortality (OR = 2.7, 95% CI = 1.1–7.0) at 3 months. However, prestroke depression was not significantly associated with PSD.

Conclusion:

Prestroke sleep disturbances was associated with systemic hypertension, a key index of high cardiovascular risk profile and stroke. The findings should energize before-the-stroke identification and prioritization of limited treatment resources in LMICs to persons with sleep symptoms who have multiple, additional, risks of stroke.

Keywords

stroke burdenstroke risk factorsmental health complicationsprestroke symptomssub-Saharan Africa
Type
Original Research Article
Information
International Psychogeriatrics , Volume 33 , Special Issue 8: Issue Theme: Longitudinal Studies of Aging , August 2021 , pp. 827 - 834
Copyright
© International Psychogeriatric Association 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andresen, E. M., Malmgren, J. A., Carter, W. B. and Patrick, D. L. (1994). Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). American Journal of Preventive Medicine, 10, 77–84. doi: 10.1016/S0749-3797(18)30622-6 CrossRefGoogle Scholar
Aron, A. W., Staff, I., Fortunato, G. and Mccullough, L. D. (2015). Prestroke living situation and depression contribute to initial stroke severity and stroke recovery. Journal of Stroke & Cerebrovascular Diseases, 24, 492–499. doi: 10.1016/j.jstrokecerebrovasdis.2014.09.024 CrossRefGoogle ScholarPubMed
Baiyewu, O. et al. (2012). Behavioral symptoms in community-dwelling elderly Nigerians with dementia, mild cognitive impairment, and normal cognition. International Journal of Geriatric Psychiatry, 27, 931–939. doi: 10.1017/S1041610215001088 CrossRefGoogle ScholarPubMed
Barlinn, K., Kepplinger, J., Puetz, V., Illigens, B. M., Bodechtel, U. and Siepmann, T. (2015). Exploring the risk-factor association between depression and incident stroke: a systematic review and meta-analysis. Neuropsychiatric Disease and Treatment, 11, 1–14. doi: 10.2147/NDT.S63904 Google ScholarPubMed
Bell, C. L. et al. 2013. Prestroke factors associated with poststroke mortality and recovery in older women in the Women’s Health Initiative. Journal of the American Geriatrics Society, 61, 1324–1330. doi: 10.1111/jgs.12361 CrossRefGoogle ScholarPubMed
Castellanos-Pinedo, F. et al. (2011). Influence of premorbid psychopathology and lesion location on affective and behavioral disorders after ischemic stroke. Journal of Neuropsychiatry and Clinical Neurosciences, 23, 340–347. doi: 10.1176/jnp.23.3.jnp340 CrossRefGoogle ScholarPubMed
Cummings, J. L. (1997). The Neuropsychiatric Inventory: assessing psychopathology in dementia patients. Neurology, 48, S10–S16. doi: 10.1212/wnl.48.5_suppl_6.10s CrossRefGoogle ScholarPubMed
Ferguson, B., Tandon, A. and Gakidou, E. (2003). Estimating permanent income using indicator variables. In: Murray, C. and Evans, D. (Eds.), Health Systems Performance Assessment: Debates, Methods and Empericism. Geneva: World Health Organisation. https://apps.who.int/iris/handle/10665/42735 Google Scholar
Folstein, M. F., Folstein, S. E. and Mchugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198. doi: 10.1016/0022-3956(75)90026-6 CrossRefGoogle ScholarPubMed
Hendrie, H. C. et al. (2001). Incidence of dementia and Alzheimer disease in 2 communities: Yoruba residing in Ibadan, Nigeria, and African Americans residing in Indianapolis, Indiana. JAMA, 285, 739–747. doi: 10.1001/jama.285.6.739 CrossRefGoogle ScholarPubMed
Jorm, A. F. (1994). A short form of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): development and cross-validation. Psychological Medicine, 24, 145–153. doi: 10.1017/s003329170002691x CrossRefGoogle Scholar
Kaufer, D. I. et al. (2000). Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. Journal of Neuropsychiatry and Clinical Neurosciences, 12, 233–239. doi: 10.1176/jnp.12.2.233 CrossRefGoogle ScholarPubMed
Kessler, R. C. et al. (2004). Clinical calibration of DSM-IV diagnoses in the World Mental Health (WMH) version of the World Health Organization (WHO) Composite International Diagnostic Interview (WMHCIDI). International Journal of Methods in Psychiatric Research, 13, 122–139. doi: 10.1002/mpr.169 CrossRefGoogle Scholar
Klimiec, E. et al. (2017). Pre-stroke apathy symptoms are associated with an increased risk of delirium in stroke patients. Scientific Reports, 7, 7658. doi: 10.1038/s41598-017-08087-7 CrossRefGoogle ScholarPubMed
Kwon, S., Hartzema, A. G., Duncan, P. W. and Min-Lai, S. (2004). Disability measures in stroke: relationship among the Barthel Index, the Functional Independence Measure, and the Modified Rankin Scale. Stroke, 35, 918–923. doi: 10.1161/01.STR.0000119385.56094.32 CrossRefGoogle ScholarPubMed
Loubinoux, I. et al. (2012). Post-stroke depression: mechanisms, translation and therapy. Journal of Cellular and Molecular Medicine, 16, 1961–1969. doi: 10.1111/j.1582-4934.2012.01555.x CrossRefGoogle ScholarPubMed
McCann, S. K. et al. (2014). Efficacy of antidepressants in animal models of ischemic stroke: a systematic review and meta-analysis. Stroke, 45, 3055–3063. doi: 10.1161/STROKEAHA.114.006304 CrossRefGoogle ScholarPubMed
Nuyen, J., Spreeuwenberg, P. M., Groenewegen, P. P., Van Den Bos, G. A. and Schellevis, F. G. (2008). Impact of preexisting depression on length of stay and discharge destination among patients hospitalized for acute stroke: linked register-based study. Stroke, 39, 132–138. doi: 10.1161/STROKEAHA.107.490565 CrossRefGoogle ScholarPubMed
O’Donnell, M. J. et al. (2016). Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. The Lancet, 388, 761–775. doi: 10.1016/S0140-6736(16)30506-2 CrossRefGoogle ScholarPubMed
Ojagbemi, A. and Owolabi, M. (2017). Do occupational therapy interventions improve quality of life in persons with dementia? A meta-analysis with implications for future directions. Psychogeriatrics, 17, 133–141. doi: 10.1111/psyg.12201 CrossRefGoogle ScholarPubMed
Owolabi, M. O. and Platz, T. (2008). Proposing the Stroke Levity Scale: a valid, reliable, simple, and time-saving measure of stroke severity. European Journal of Neurology, 15, 627–633. doi: 10.1111/j.1468-1331.2008.02140.x CrossRefGoogle ScholarPubMed
Owolabi, M. O. et al. (2015). The burden of stroke in Africa: a glance at the present and a glimpse into the future. Cardiovascular Journal of Africa, 26, S27–S38. doi: 10.5830/CVJA-2015-038 CrossRefGoogle Scholar
Owolabi, M. O. et al. (2018). Dominant modifiable risk factors for stroke in Ghana and Nigeria (SIREN): a case-control study. The Lancet Global Health, 6, e436–e446. doi: 10.1016/S2214-109X(18)30002-0 CrossRefGoogle ScholarPubMed
Radloff, L. S. (1977). The CES-D scale a self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401. doi: 10.1177/014662167700100306 CrossRefGoogle Scholar
Robinson, R. G. and Jorge, R. E. (2016). Post-stroke depression: a review. The American Journal of Psychiatry, 173, 221–231. doi: 10.1176/appi.ajp.2015.15030363 CrossRefGoogle ScholarPubMed
Sacco, R. L. et al. (2013). An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 44, 2064–2089. doi: 10.1161/STR.0b013e318296aeca CrossRefGoogle ScholarPubMed
Sarfo, F. S. et al. (2017). Post-stroke depression in Ghana: characteristics and correlates. Journal of the Neurological Sciences, 379, 261–265. doi: 10.1016/j.jns.2017.02.018 CrossRefGoogle ScholarPubMed
S chottke, H. and Giabbiconi, C. M. (2015). Post-stroke depression and post-stroke anxiety: prevalence and predictors. International Psychogeriatrics, 27, 1805–1812. doi: 10.1017/S1041610215000988 CrossRefGoogle Scholar
Sharrief, A. Z. et al. (2017). The impact of pre-stroke depressive symptoms, fatalism, and social support on disability after stroke. Journal of Stroke & Cerebrovascular Diseases, 26, 2686–2691. doi: 10.1016/j.jstrokecerebrovasdis.2017.06.039 CrossRefGoogle ScholarPubMed
Sinnema, H., Terluin, B., Volker, D., Wensing, M. and Van Balkom, A. (2018). Factors contributing to the recognition of anxiety and depression in general practice. BMC Family Practice, 19, 99. doi: 10.1186/s12875-018-0784-8 CrossRefGoogle ScholarPubMed
Stata Corp (2013). Stata Statistical Software. College Station, TX: StataCorp LP.Google Scholar
Storor, D. L. and Byrne, G. J. (2006). Pre-morbid personality and depression following stroke. International Psychogeriatrics, 18, 457–469. doi: 10.1017/S1041610206003188 CrossRefGoogle ScholarPubMed
Stulberg, E. L. et al. (2019). Associations of self-reported history of depression and antidepressant use before stroke onset with poststroke post-acute rehabilitation care-an exploratory study: the BASIC (Brain Attack Surveillance in Corpus Christi) project. Journal of the American Heart Association, 8, e013382. doi: 10.1161/JAHA.119.013382 CrossRefGoogle Scholar
Stuller, K. A., Jarrett, B. and Devries, A. C. (2012). Stress and social isolation increase vulnerability to stroke. Experimental Neurology, 233, 33–39. doi: 10.1016/j.expneurol.2011.01.016 CrossRefGoogle Scholar
Taylor-Rowan, M., Momoh, O., Ayerbe, L., Evans, J. J., Stott, D. J. and Quinn, T. J. (2019). Prevalence of pre-stroke depression and its association with post-stroke depression: a systematic review and meta-analysis. Psychological Medicine, 49, 685–696. doi: 10.1017/S0033291718002003 CrossRefGoogle ScholarPubMed
Van Swieten, J. C., Koudstaal, P. J., Visser, M. C., Schouten, H. J. and Van Gijn, J. (1988). Interobserver agreement for the assessment of handicap in stroke patients. Stroke, 19, 604–607. doi: 10.1161/01.str.19.5.604 CrossRefGoogle ScholarPubMed
Villa, R. F., Ferrari, F. and Moretti, A. (2018). Post-stroke depression: mechanisms and pharmacological treatment. Pharmacology & Therapeutics, 184, 131–144. doi: 10.1016/j.pharmthera.2017.11.005 CrossRefGoogle ScholarPubMed
Wang, E. Y., Meyer, C., Graham, G. D. and Whooley, M. A. (2018). Evaluating screening tests for depression in post-stroke older adults. Journal of Geriatric Psychiatry and Neurology, 31, 129–135. doi: 10.1016/j.jad.2018.04.011 CrossRefGoogle ScholarPubMed
Wing, J. (1996). SCAN and the PSE tradition. Social Psychiatry and Psychiatric Epidemiology, 31, 50–54. doi: 10.1007/BF00801900 CrossRefGoogle ScholarPubMed
Wong, A. et al. (2014). Validity and reliability of the neuropsychiatric inventory questionnaire version in patients with stroke or transient ischemic attack having cognitive impairment. Journal of Geriatric Psychiatry and Neurology, 27, 247–252. doi: 10.1177/0891988714532017 CrossRefGoogle ScholarPubMed
Supplementary material: File

Ojagbemi et al. Supplementary Materials

Ojagbemi et al. Supplementary Materials 1

Download Ojagbemi et al. Supplementary Materials(File)
File 22 KB
Supplementary material: File

Ojagbemi et al. Supplementary Materials

Ojagbemi et al. Supplementary Materials 2

Download Ojagbemi et al. Supplementary Materials(File)
File 23.1 KB