The optimal management of post-stroke cognitive impairment remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making around prevention, diagnosis, treatment and prognosis. These guidelines were developed according to ESO standard operating procedure and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews and, where possible, meta-analyses of the literature, assessed the quality of the available evidence and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations based on the GRADE approach. There was limited randomised controlled trial evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Interventions to improve lifestyle and treat vascular risk factors may have many health benefits but a beneficial effect on cognition is not proven. We found no evidence around routine cognitive screening following stroke but recognise the importance of targeted cognitive assessment. We described the accuracy of various cognitive screening tests but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognitive syndromes (cognitive impairment, dementia and delirium). The association between post-stroke cognitive impairment and most acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on acute MRI brain may help predict cognitive outcomes. These guidelines have highlighted fundamental areas where robust evidence is lacking. Further, definitive randomised controlled trials are needed, and we suggest priority areas for future research.

Background: There are many prognostic scales that aim to predict functional outcome following acute stroke. Despite considerable research interest, these scales have had limited impact in routine clinical practice. This may be due to perceived problems with internal validity (quality of research), as well as external validity (generalizability of results). We set out to collate information on exemplar stroke prognosis scales, giving particular attention to the scale content, derivation, and validation. Methods: We performed a focused literature search, designed to return high profile scales that use baseline clinical data to predict mortality or disability. We described prognostic utility and collated information on the content, development and validation of the tools. We critically appraised chosen scales based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS). Results: We chose 10 primary scales that met our inclusion criteria, six of which had revised/modified versions. Most primary scales used 5 input variables (range: 4–13), with substantial overlap in the variables included. All scales included age, eight included a measure of stroke severity, while five scales incorporated pre-stroke level of function (often using modified Rankin Scale), comorbidities and classification of stroke type. Through our critical appraisal, we found issues relating to excluding patients with missing data from derivation studies, and basing the selection of model variable on significance in univariable analysis (in both cases noted for six studies). We identified separate external validation studies for all primary scales but one, with a total of 60 validation studies. Conclusions: Most acute stroke prognosis scales use similar variables to predict long-term outcomes and most have reasonable prognostic accuracy. While not all published scales followed best practice in development, most have been subsequently validated. Lack of clinical uptake may relate more to practical application of scales rather than validity. Impact studies are now necessary to investigate clinical usefulness of existing scales.

We aimed to determine prevalence of pre-stroke frailty in acute stroke and describe validity of a Frailty Index–based assessment. Cross-sectional. Single UK urban teaching hospital. Consecutive acute stroke unit admissions, recruited in four waves (May 2016–August 2018). We performed the assessments within first week and attempted to include all admissions. Our primary measure was a Frailty Index, based on cumulative disorders. A proportion of participants were also assessed with the ‘Frail non-disabled’ questionnaire. We evaluated concurrent validity of Frailty Index against variables associated with frailty in non-stroke populations. We described predictive validity of Frailty Index for stroke severity and delirium. We described convergent validity, quantifying agreement between frailty assessments and a measure of pre-stroke disability (modified Rankin Scale) using kappa statistics and correlations. We included 546 patients. A Frailty Index–defined frailty syndrome was observed in 427 of 545 patients (78%), of whom, 151 (28%) had frank frailty and 276 (51%) were pre-frail. Phenotypic frailty was observed in 72 of 258 patients (28%). We demonstrated concurrent validity via significant associations with all variables (all p < 0.01). We demonstrated predictive validity for stroke severity and delirium (p < 0.01). Agreement between the frailty measures was poor (kappa = –0.06) and convergent validity was moderate (Frail non-disabled ‘Cramer’s V’ = 0.25; modified Rankin Scale ‘Cramer’s V’ = 0.47). Frailty is present in around one in four patients with acute stroke; if pre-frailty is included, then a frailty syndrome is seen in three out of four patients. The Frailty Index is a valid measure of frailty in stroke; however, there is little agreement between this scale and other measurements of frailty.

Background and Purpose—: Delirium is a common and serious complication of acute illness. We describe delirium occurrence in an unselected, acute stroke population. Methods—: We collected data from consecutive stroke admissions. We performed comprehensive cognitive assessment within the first week including Diagnostic Statistical Manual-5–based delirium diagnosis. We reported proportion with delirium and the clinical and demographic associations with delirium using multiple logistic regression. Results—: Of 708 patients, median age of 71 years (interquartile range, 59–80), we recorded delirium in 187 of 708 (26.4%; 95% CI, 23.0–30.0). Across 395 patients with complete risk factor data (105 delirium), factors independently associated with delirium were: age (odds ratio, 1.05; 95% CI, 1.03–1.08), drug/alcohol misuse (odds ratio, 2.64; 95% CI, 1.10–6.26), and stroke severity (odds ratio, 1.22; 95% CI, 1.14–1.31). Conclusions—: Delirium is common in acute stroke, affecting 1 in 4. It may be possible to predict those at risk using prestroke and stroke-specific factors. Clinical Trial Registration—: URL: researchregistry.com . Protocol: 1147.

Objective: Post-stroke cognitive impairment is common, but mechanisms and risk factors are poorly understood. Frailty may be an important risk factor for cognitive impairment after stroke. We investigated the association between pre-stroke frailty and acute post-stoke cognition. Methods: We studied consecutively admitted acute stroke patients in a single urban teaching hospital during three recruitment waves between May 2016 and December 2017. Cognition was assessed using the Mini-Montreal Cognitive Assessment (min=0; max=12). A Frailty Index was used to generate frailty scores for each patient (min=0; max=100). Clinical and demographic information were collected, including pre-stroke cognition, delirium, and stroke-severity. We conducted univariate and multiple-linear regression analyses with covariates forced in (covariates included were: age, sex, stroke severity, stroke-type, pre-stroke cognitive impairment, delirium, previous stroke/transient ischemic attack) to investigate the association between pre-stroke frailty and post-stroke cognition. Results: Complete data were available for 154 stroke patients. Mean age was 68 years (SD=11; range=32–97); 93 (60%) were male. Median mini-Montreal Cognitive Assessment score was 8 (IQR=4–12). Mean Frailty Index score was 18 (SD=11). Pre-stroke cognitive impairment was apparent in 13/154 (8%) patients. Pre-stroke frailty was significantly associated with lower post-stroke cognition (Standardized-Beta=−0.40; p<0.001) and this association was independent of covariates (Unstandardized-Beta=−0.05; p=0.005). Additional significant variables in the multiple regression model were age (Unstandardized-Beta=−0.05; p=0.002), delirium (Unstandardized-Beta=−2.81; p<0.001), pre-stroke cognitive impairment (Unstandardized-Beta=−2.28; p=0.001), and stroke-severity (Unstandardized-Beta=−0.20; p<0.001). Conclusions: Pre-stroke frailty may be a moderator of post-stroke cognition, independent of other well-established post-stroke cognitive impairment risk factors. (JINS, 2019, 25, 501–506)

Background: There is increasing interest in the concept of frailty in stroke, including both physical frailty and imaging-evidence of brain frailty. We aimed to establish the prevalence of brain frailty in stroke survivors as well as the concurrent and predictive validity of various frailty measures against long-term cognitive outcomes. Methods: We included consecutively admitted stroke or transient ischaemic attack (TIA) survivors from participating stroke centres. Baseline CT scans were used to generate an overall brain frailty score for each participant. We measured frailty via the Rockwood frailty index, and a Fried frailty screening tool. Presence of major or minor neurocognitive disorder at 18-months following stroke or TIA was established via a multicomponent assessment. Prevalence of brain frailty was established based upon observed percentages within groups defined by frailty status (robust, pre-frail, frail). We assessed the concurrent validity of brain frailty and frailty scales via Spearman’s rank correlation. We conducted multivariable logistic regression analyses, controlling for age, sex, baseline education and stroke severity, to evaluate association between each frailty measure and 18-month cognitive impairment. Results: Three-hundred-forty-one stroke survivors participated. Three-quarters of people who were frail had moderate-severe brain frailty and prevalence increased according to frailty status. Brain frailty was weakly correlated with Rockwood frailty (Rho: 0.336; p < 0.001) and with Fried frailty (Rho: 0.230; p < 0.001). Brain frailty (OR: 1.64, 95% CI = 1.17–2.32), Rockwood frailty (OR: 1.05, 95% CI = 1.02–1.08) and Fried frailty (OR: 1.93, 95% CI = 1.39–2.67) were each independently associated with cognitive impairment at 18 months following stroke. Conclusions: There appears to be value in the assessment of both physical and brain frailty in patients with ischaemic stroke and TIA. Both are associated with adverse cognitive outcomes and physical frailty remains important when assessing cognitive outcomes.