Clinical determinants of long-term quality of life after stroke

Abstract

Objectives: to determine factors that independently predict health-related quality of life (HRQOL) 1 and 3 years after stroke. Methods: subjects numbering 397, from a population-based register of first-ever strokes were assessed for HRQOL using the Short Form 36 (SF36) 1 year after stroke. Physical (PHSS) and mental health (MHSS) summary scores were derived from the eight domains of HRQOL in the SF36. Multivariate stepwise regression analyses were conducted to determine independent predictors of these scores; β coefficients with 95% CI were obtained. β coefficient is the difference between average value of the variable (e.g. male) and average value under consideration (e.g. female). Demographic and stroke risk factors, neurological impairments and cognitive impairment (MMSE <24) were included in the models. Similar analyses were undertaken on 150 subjects 3 years post-stroke. Results: a year after stroke, independent predictors of the worst PHSS were of females (β coefficient −3.3 : 95% CI −5.7 to −0.8), manual workers (−3.2: −5.9 to −0.4), diabetes (−4.2: −7.7 to −0.8), right hemispheric lesions (−4.9: −8.7 to −1.2), urinary incontinence (−7.8: −11.6 to −4.1) and cognitive impairment (−2.7: −5.5 to −0.1); the worst MHSS were associated with being Asian (−11.8: −20.6 to −3.0), ischaemic heart disease (−2.7: −5.4 to −0.03), cognitive impairment (−3.04: −5.8 to −0.3). Subjects aged 65-75 years (5.4 : 2.5 to −8.4) had better MHSS than those <65 years. Three years post-stroke, independent predictors of worse PHSS were hypertension (−8.7: −13.5 to −3.9), urinary incontinence (−8.1: −15 to −1.1) and cognitive impairment (−8.3: −13.2 to −3.5). Conclusions: determinants of HRQOL vary both over time after stroke and whether physical or psychosocial aspects of HRQOL are being considered. This study provides valuable information on factors predicting long-term HRQOL, which can be taken into consideration in audits of clinical practice or in future interventional studies aiming to improve HRQOL after stroke.

Quality of life in aphasia: differences between fluent and non-fluent aphasic Augmentative and Alternative Communication users

“…Moreover, the literature in this area reports that the severity of the aphasia is one of the principal predictors to decrease the QOL post-stroke (23,(26)(27)(28) , which agrees with the data found in our study.…”

“…However, other studies (9,28) show that part of the subjects indicate their QOL remained the same after the stroke. This difference can be explained by the reduced size of the sample in our study, and also, because it refers to a population of aphasics only.…”

Network measures predict neuropsychological outcome after brain injury

“…Extent of poststroke recovery varies with many patient-related factors (e.g., age at stroke onset, sex, handedness, socioeconomic status) and various stroke-and lesion-related factors (e.g., initial stroke severity, lesion size, stroke subtype, lesion location), but these factors have very limited predictive power for outcome (19)(20)(21)(22)(23)(24). Even good predictors, including initial stroke severity and lesion volume, leave most of the variability in outcome unexplained (25)(26)(27)(28).…”

Clinical and Functional Characteristics of Young Adult Patients Admitted in a Stroke Unit

“…Previously referenced as a rare condition among the young adults, with an incidence of 5 to 10% 4.5 , in Brazil, In the last five years have identified a frequency which is close to 40% of young adults between those who had stroke 6 . In addition to the high mortality rate, it is considered as the most disabling of long-term diseases, impacting not only the patient but also their families 7 , and the social security and public system 8 . The growing number of young adults affected by stroke has increased the cost of disability benefits, greatly burdening the public health budget and being responsible for 40% of early retirements 8 .…”

“…Stroke is a disabling disease 7 and the involvement of this active population will bring losses to social security, reaching a rate of 40% of early retirement 8 with a range of 40 to 93% of patients not returning to previous activity, requiring permanent removal from professional activities 11.27 28 .…”