Short-term Trajectories of Poststroke Cognitive Function: A STROKOG Collaboration Study.

BACKGROUND AND OBJECTIVES: Past studies on poststroke cognitive function have focused on the average performance or change over time, but few have investigated patterns of cognitive trajectories after stroke. This project used latent class growth analysis (LCGA) to identify clusters of patients with similar patterns of cognition scores over the first-year poststroke and the extent to which long-term cognitive outcome is predicted by the clusters ("trajectory groups"). METHODS: Data were sought from the Stroke and Cognition consortium. LCGA was used to identify clusters of trajectories based on standardized global cognition scores at baseline (T1) and at the 1-year follow-up (T2). One-step individual participant data meta-analysis was used to examine risk factors for trajectory groups and association of trajectory groups with cognition at the long-term follow-up (T3). RESULTS: Nine hospital-based stroke cohorts with 1,149 patients (63% male; mean age 66.4 years [SD 11.0]) were included. The median time assessed at T1 was 3.6 months poststroke, 1.0 year at T2, and 3.2 years at T3. LCGA identified 3 trajectory groups, which were characterized by different mean levels of cognition scores at T1 (low-performance, -3.27 SD [0.94], 17%; medium-performance, -1.23 SD [0.68], 48%; and high-performance, 0.71 SD [0.77], 35%). There was significant improvement in cognition for the high-performance group (0.22 SD per year, 95% CI 0.07-0.36), but changes for the low-performance and medium-performance groups were not significant (-0.10 SD per year, 95% CI -0.33 to 0.13; 0.11 SD per year, 95% CI -0.08 to 0.24, respectively). Factors associated with the low- (vs high-) performance group include age (relative risk ratio [RRR] 1.18, 95% CI 1.14-1.23), years of education (RRR 0.61, 95% CI 0.56-0.67), diabetes (RRR 3.78, 95% CI 2.08-6.88), large artery vs small vessel strokes (RRR 2.77, 95% CI 1.32-5.83), and moderate/severe strokes (RRR 3.17, 95% CI 1.42-7.08). Trajectory groups were predictive of global cognition at T3, but its predictive power was comparable with scores at T1. DISCUSSION: The trajectory of cognitive function over the first-year poststroke is heterogenous. Baseline cognitive function ∼3.6 months poststroke is a good predictor of long-term cognitive outcome. Older age, lower levels of education, diabetes, large artery strokes, and greater stroke severity are risk factors for lower cognitive performance over the first year.

Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. AIMS: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. METHODS: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3-12, 12-24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. RESULTS: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3-12 months, 243/853 (28%) at 12-24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34-1.68) and multivariable (OR 1.27, 95%CI 1.10-1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. CONCLUSIONS: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.

Long-Term Cognitive Decline After Stroke: An Individual Participant Data Meta-Analysis.

BACKGROUND: Poststroke cognitive impairment is common, but the trajectory and magnitude of cognitive decline after stroke is unclear. We examined the course and determinants of cognitive change after stroke using individual participant data from the Stroke and Cognition Consortium. METHODS: Nine longitudinal hospital-based cohorts from 7 countries were included. Neuropsychological test scores and normative data were used to calculate standardized scores for global cognition and 5 cognitive domains. One-step individual participant data meta-analysis was used to examine the rate of change in cognitive function and risk factors for cognitive decline after stroke. Stroke-free controls were included to examine rate differences. Based on the literature and our own data that showed short-term improvement in cognitive function after stroke, key analyses were restricted to the period beginning 1-year poststroke to focus on its long-term effects. RESULTS: A total of 1488 patients (mean age, 66.3 years; SD, 11.1; 98% ischemic stroke) were followed for a median of 2.68 years (25th-75th percentile: 1.21-4.14 years). After an initial period of improvement through up to 1-year poststroke, decline was seen in global cognition and all domains except executive function after adjusting for age, sex, education, vascular risk factors, and stroke characteristics (-0.053 SD/year [95% CI, -0.073 to -0.033]; P<0.001 for global cognition). Recurrent stroke and older age were associated with faster decline. Decline was significantly faster in patients with stroke compared with controls (difference=-0.078 SD/year [95% CI, -0.11 to -0.045]; P<0.001 for global cognition in a subgroup analysis). CONCLUSIONS: Patients with stroke experience cognitive decline that is faster than that of stroke-free controls from 1 to 3 years after onset. An increased rate of decline is associated with older age and recurrent stroke.

Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts.

BACKGROUND: Post-stroke cognitive impairment (PSCI) occurs in approximately half of people in the first year after stroke. Infarct location is a potential determinant of PSCI, but a comprehensive map of strategic infarct locations predictive of PSCI is unavailable. We aimed to identify infarct locations most strongly predictive of PSCI after acute ischaemic stroke and use this information to develop a prediction model. METHODS: In this large-scale multicohort lesion-symptom mapping study, we pooled and harmonised individual patient data from 12 cohorts through the Meta-analyses on Strategic Lesion Locations for Vascular Cognitive Impairment using Lesion-Symptom Mapping (Meta VCI Map) consortium. The identified cohorts (as of Jan 1, 2019) comprised patients with acute symptomatic infarcts on CT or MRI (with available infarct segmentations) and a cognitive assessment up to 15 months after acute ischaemic stroke onset. PSCI was defined as performance lower than the fifth percentile of local normative data, on at least one cognitive domain on a multidomain neuropsychological assessment or on the Montreal Cognitive Assessment. Voxel-based lesion-symptom mapping (VLSM) was used to calculate voxel-wise odds ratios (ORs) for PSCI that were mapped onto a three-dimensional brain template to visualise PSCI risk per location. For the prediction model of PSCI risk, a location impact score on a 5-point scale was derived from the VLSM results on the basis of the mean voxel-wise coefficient (ln[OR]) within each patient's infarct. We did combined internal-external validation by leave-one-cohort-out cross-validation for all 12 cohorts using logistic regression. Predictive performance of a univariable model with only the location impact score was compared with a multivariable model with addition of other clinical PSCI predictors (age, sex, education, time interval between stroke onset and cognitive assessment, history of stroke, and total infarct volume). Testing of visual ratings was done by three clinicians, and accuracy, inter-rater reliability, and intra-rater reliability were assessed with Cohen's weighted kappa. FINDINGS: In our sample of 2950 patients (mean age 66·8 years [SD 11·6]; 1157 [39·2%] women), 1286 (43·6%) had PSCI. We achieved high lesion coverage of the brain in our analyses (86·9%). Infarcts in the left frontotemporal lobes, left thalamus, and right parietal lobe were strongly associated with PSCI (after false discovery rate correction, q<0·01; voxel-wise ORs >20). On cross-validation, the location impact score showed good correspondence, based on visual assessment of goodness of fit, between predicted and observed risk of PSCI across cohorts after adjusting for cohort-specific PSCI occurrence. Cross-validations showed that the location impact score by itself had similar performance to the combined model with other PSCI predictors, while allowing for easy visual assessment. Therefore the univariable model with only the location impact score was selected as the final model. Correspondence between visual ratings and actual location impact score (Cohen's weighted kappa: range 0·88-0·92), inter-rater agreement (0·85-0·87), and intra-rater agreement (for a single rater, 0·95) were all high. INTERPRETATION: To the best of our knowledge, this study provides the first comprehensive map of strategic infarct locations associated with risk of PSCI. A location impact score was derived from this map that robustly predicted PSCI across cohorts. Furthermore, we developed a quick and reliable visual rating scale that might in the future be applied by clinicians to identify individual patients at risk of PSCI. FUNDING: The Netherlands Organisation for Health Research and Development.

Prediction of Long-term Cognitive Functions after Minor Stroke, Using Functional Connectivity.

OBJECTIVE: To determine whether functional MRI connectivity can predict the long-term cognitive functions 36 months after minor stroke. METHODS: Seventy-two participants with first-ever stroke were included at baseline and followed up for 36 months. A ridge regression machine learning algorithm was developed and used to predict cognitive scores 36 months post-stroke on the basis of the functional networks measured using MRI at 6 months (referred to here as the post-stroke cognitive impairment (PSCI) network). The prediction accuracy was evaluated in four domains (memory, attention/executive, language and visuospatial functions) and compared with clinical data and other functional networks. The models' statistical significance was probed with permutation tests. The potential involvement of cortical atrophy was assessed 6 months post-stroke. A second, independent dataset (n=40) was used to validate the results and assess their generalizability. RESULTS: Based on the PSCI network, a machine learning model was able to predict memory, attention, visuospatial functions and language functions 36 months post-stroke (r2: 0.67, 0.73, 0.55 and 0.48, respectively). The PSCI-based model was at least as accurate as models based on other functional networks or clinical data. Specific patterns were demonstrated for the four cognitive domains, with involvement of the left superior frontal cortex for memory, attention and visuospatial functions. The cortical thickness 6 months post-stroke was not correlated with cognitive function 36 months post-stroke. The independent validation dataset gave similar results. CONCLUSIONS: A machine learning model based on the PSCI network can predict the long-term cognitive outcome after stroke.

Association of Prediabetes and Type 2 Diabetes With Cognitive Function After Stroke: A STROKOG Collaboration Study.

Background and Purpose- Type 2 diabetes mellitus (T2D) is associated with cognitive impairment and an increased risk of dementia, but the association between prediabetes and cognitive impairment is less clear, particularly in a setting of major cerebrovascular events. This article examines the impact of impaired fasting glucose and T2D on cognitive performance in a stroke population. Methods- Seven international observational studies from the STROKOG (Stroke and Cognition) consortium (n=1601; mean age, 66.0 years; 70% Asian, 26% white, and 2.6% African American) were included. Fasting glucose level (FGL) during hospitalization was used to define 3 groups, T2D (FGL ≥7.0 mmol/L), impaired fasting glucose (FGL 6.1-6.9 mmol/L), and normal (FGL <6.1 mmol/L), and a history of diabetes mellitus and the use of a diabetes mellitus medication were also used to support a diagnosis of T2D. Domain and global cognition Z scores were derived from standardized neuropsychological test scores. The cross-sectional association between glucose status and cognitive performance at 3 to 6 months poststroke was examined using linear mixed models, adjusting for age, sex, education, stroke type, ethnicity, and vascular risk factors. Results- Patients with T2D had significantly poorer performance in global cognition (SD, -0.59 [95% CI, -0.82 to -0.36]; P<0.001) and in all domains compared with patients with normal FGL. There was no significant difference between impaired fasting glucose patients and those with normal FGL in global cognition (SD, -0.10 [95% CI, -0.45 to 0.24]; P=0.55) or in any cognitive domain. Conclusions- Diabetes mellitus, but not prediabetes, is associated with poorer cognitive performance in patients 3 to 6 months after stroke.

Analysis of the association of MPO and MMP-9 with stroke severity and outcome: Cohort study.

OBJECTIVE: In acute cerebral ischemia, circulating neutrophil count and neutrophil-to-lymphocyte ratio (NLR) are positively associated with stroke severity and worse outcomes. Mediators of this effect are unknown. We aimed to investigate (1) the relationship between plasma matrix metalloproteinase-9 (MMP-9) and myeloperoxidase (MPO) concentrations with stroke severity and outcome and (2) MMP-9 and MPO release after ex vivo stimulation of neutrophils by recombinant tissue plasminogen activator (rtPA). METHODS: We analyzed data collected in 255 patients with supratentorial cerebral infarcts recruited within 48 hours of symptoms onset irrespective of rtPA treatment. The endpoints were excellent outcome (modified Rankin Scale score 0-1), symptomatic intracerebral hemorrhage (European Cooperative Acute Stroke Study-II definition), and death at 3 months. The role of rtPA treatment on peripheral neutrophil degranulation was investigated in 18 patients within 4.5 hours and after 72 hours. RESULTS: Neutrophil counts, NLR, and MPO plasma concentrations, but not MMP-9, were positively correlated with stroke severity. Higher neutrophil counts and NLR were independently associated with worse outcomes and higher mortality rates at month 3. Higher MPO plasma concentrations, but not MMP-9, were associated with worse outcome. Neutrophil-derived MMP-9, after ex vivo rtPA stimulation, but not MPO, were higher after 72 hours in patients treated by IV rtPA but not associated with hemorrhagic transformation. CONCLUSIONS: Neutrophil counts, NLR, and MPO plasma concentrations are associated with worse outcome in patients with acute cerebral ischemia, in contrast to MMP-9. Further investigations are needed to deepen our knowledge on MPO's role in the deleterious effect of neutrophils because it could represent a potential therapeutic target.

Texture Features of Magnetic Resonance Images: an Early Marker of Post-stroke Cognitive Impairment.

Stroke is frequently associated with delayed, long-term cognitive impairment (CI) and dementia. Recent research has focused on identifying early predictive markers of CI occurrence. We carried out a texture analysis of magnetic resonance (MR) images to identify predictive markers of CI occurrence based on a combination of preclinical and clinical data. Seventy-two-hour post-stroke T1W MR images of 160 consecutive patients were examined, including 75 patients with confirmed CI at the 6-month post-stroke neuropsychological examination. Texture features were measured in the hippocampus and entorhinal cortex and compared between patients with CI and those without. A correlation study determined their association with MoCA and MMSE clinical scores. Significant features were then combined with the classical prognostic factors, age and gender, to build a machine learning algorithm as a predictive model for CI occurrence. A middle cerebral artery transient occlusion model was used. Texture features were compared in the hippocampus of sham and lesioned rats and were correlated with histologically assessed neural loss. In clinical studies, two texture features, kurtosis and inverse difference moment, differed significantly between patients with and without CI and were significantly correlated with MoCA and MMSE scores. The prediction model had an accuracy of 88 ± 3%. The preclinical model revealed a significant correlation between texture features and neural density in the hippocampus contralateral to the ischemic area. These preliminary results suggest that texture features of MR images are representative of neural alteration and could be a part of a screening strategy for the early prediction of post-stroke CI.

Preadmission use of benzodiazepines and stroke outcomes: the Biostroke prospective cohort study.

OBJECTIVES: We tested the hypothesis that stroke outcomes in patients with preadmission use of benzodiazepine are worse. METHOD: In a prospective cohort study, we recruited patients with acute ischaemic stroke. Mortality, functional outcomes and cognition were evaluated at 8 and 90 days after stroke. RESULTS: 370 patients were included. 62 (18.5%) of the 336 remaining patients were treated with benzodiazepines when stroke occurred, and they did not receive any other psychotropic drug. The mortality rate was higher in benzodiazepines users than non-users at day 8 (2.2% vs 8.1%, p=0.034) and day 90 (8.1% vs 25.9%, p=0.0001). After controlling for baseline differences using propensity-score matching, only the difference in mortality rate at day 90 was of borderline of significance, with a matched OR of 3.93 (95% CI, 0.91 to 16.98). In propensity-score-adjusted cohort, this difference remained significant with a similar treatment effect size (adjusted OR, 3.50; 95% CI, 1.57 to 7.76). A higher rate of poor functional outcome at day 8 and day 90 defined bymodified Rankin scale (mRS) ≥2 or by theBarthel index (BI) <95 was found in benzodiazepines users. In propensity-score-adjusted cohort, only the difference in mRS≥2 at day 90 remained significant (adjusted OR, 1.89; 95% CI, 1.02 to 3.48). In survivors at day 8 and at day 90, there was no significant difference in cognitive evaluation. CONCLUSION: Our study has shown that preadmission use of benzodiazepines could be associated with increased post-stroke mortality at 90 days. These findings do not support a putative neuroprotective effect of γ-aminobutyric acidA receptors agonists and should alert clinicians of their potential risks. TRIAL REGISTRATION NUMBER: NCT00763217.

Neutrophils in tPA-induced hemorrhagic transformations: Main culprit, accomplice or innocent bystander?

The risk of intracerebral hemorrhage still greatly limits the use of tPA in stroke patients. Research is ongoing in order to identify the pathophysiological mechanisms at play, detect predictive biomarkers and discover new pharmacological targets to develop preventive or curative treatments. Going through experimental and clinical studies, this review focuses on the role of neutrophils as key predictive biomarkers for thrombolysis-induced hemorrhages and as pharmacological targets to limit their occurrence. To date, there are no established pharmacological modulators of neutrophils for ischemic stroke and its hemorrhagic complications. Several strategies are under evaluation, including lipid-lowering drugs, free radical scavengers, or minocycline, as well as non-pharmacological interventions such as physical exercise.

Early MoCA predicts long-term cognitive and functional outcome and mortality after stroke.

OBJECTIVE: To examine whether the Montreal Cognitive Assessment (MoCA) administered within 7 days after stroke predicts long-term cognitive impairment, functional impairment, and mortality. METHODS: MoCA was administered to 274 patients from 2 prospective hospital-based cohort studies in Germany (n = 125) and France (n = 149). Cognitive and functional outcomes were assessed at 6, 12, and 36 months after stroke by comprehensive neuropsychological testing, the Clinical Dementia Rating (CDR) scale, the modified Rankin Scale (mRS), and Instrumental Activities of Daily Living (IADL) and analyzed with generalized estimating equations. All-cause mortality was investigated by Cox proportional hazard models. Analyses were adjusted for demographic variables, education, vascular risk factors, premorbid cognitive status, and NIH Stroke Scale scores. The additive predictive value of MoCA was examined with receiver operating characteristic curves. RESULTS: In pooled analyses, a baseline MoCA score <26 was associated with cognitive impairment, defined by neuropsychological testing (odds ratio [OR] 5.30, 95% confidence interval [CI] 2.75-10.22) and by CDR score ≥0.5 (OR 2.53, 95% CI 1.53-4.18); functional impairment, defined by mRS score >2 (OR 5.03, 95% CI 2.20-11.51) and by IADL score <8 (OR 2.48, 95% CI 1.40-4.38); and mortality (hazard ratio 7.24, 95% CI 1.99-26.35) across the 3-year follow-up. Patients with MoCA score <26 performed worse across all prespecified cognitive domains (executive function/attention, memory, language, visuospatial ability). MoCA increased the area under the curve for predicting cognitive impairment (neuropsychological testing 0.81 vs 0.72, p = 0.01) and functional impairment (mRS score >2, 0.88 vs 0.84, p = 0.047). CONCLUSION: Early cognitive testing by MoCA predicts long-term cognitive outcome, functional outcome, and mortality after stroke. Our results support routine use of the MoCA in stroke patients.

Identification of a specific functional network altered in poststroke cognitive impairment.

OBJECTIVE: To study the association between poststroke cognitive impairment and defining a specific resting functional marker. METHODS: The resting-state functional connectivity 6 months after an ischemic stroke in 56 patients was investigated. Twenty-nine of the patients who had an impairment of one or several cognitive domains were compared to 27 without any cognitive deficit. We studied the whole-brain connectivity using 2 complementary approaches: graph theory to study the functional network organization and network-based statistics to explore connectivity between brain regions. We assessed the potential cortical atrophy using voxel-based morphometry analysis. RESULTS: The overall topological organization of the functional network was not altered in cognitively impaired stroke patients, who had the same mean node degree, average clustering coefficient, and global efficiency as cognitively healthy stroke patients. Network-based statistics analysis showed that poststroke cognitive impairment was associated with dysfunction of a whole-brain network composed of 167 regions and 178 connections, and functional disconnections between superior, middle, and inferior frontal gyri and the superior and inferior temporal gyri. These regions had connections that were specifically and positively correlated with cognitive domain scores. No intergroup differences in overall gray matter thickness and ischemic infarct topography were observed. To assess the effect of prestroke white matter hyperintensities on connectivity, we included the initial Fazekas scale in the regression model for a second network-based analysis. The resulting network was associated with the same key alterations but had fewer connections. CONCLUSIONS: The observed functional network alterations suggest that the appearance of a cognitive impairment following stroke may be associated with a particular functional alteration, shared specifically between cognitive domains.

Controlled Education of patients after Stroke (CEOPS)- nurse-led multimodal and long-term interventional program involving a patient's caregiver to optimize secondary prevention of stroke: study protocol for a randomized controlled trial.

BACKGROUND: Setting up a follow-up secondary prevention program after stroke is difficult due to motor and cognitive impairment, but necessary to prevent recurrence and improve patients' quality of life. To involve a referent nurse and a caregiver from the patient's social circle in nurse-led multimodal and long-term management of risk factors after stroke could be an advantage due to their easier access to the patient and family. The aim of this study is to compare the benefit of optimized follow up by nursing personnel from the vascular neurology department including therapeutic follow up, and an interventional program directed to the patient and a caregiving member of their social circle, as compared with typical follow up in order to develop a specific follow-up program of secondary prevention of stroke. METHODS/DESIGN: The design is a randomized, controlled, clinical trial conducted in the French Stroke Unit of the Strokavenir network. In total, 410 patients will be recruited and randomized in optimized follow up or usual follow up for 2 years. In both group, patients will be seen by a neurologist at 6, 12 and 24 months. The optimized follow up will include follow up by a nurse from the vascular neurology department, including therapeutic follow up, and a training program on secondary prevention directed to the patient and a caregiving member of their social circle. After discharge, a monthly telephone interview, in the first year and every 3 months in the second year, will be performed by the nurse. At 6, 12 and 24 month, the nurse will give the patient and caregiver another training session. Usual follow up is only done by the patient's general practitioner, after classical information on secondary prevention of risk factors during hospitalization. The primary outcome measure is blood pressure measured after the first year of follow up. Blood pressure will be measured by nursing personnel who do not know the group into which the patient has been randomized. Secondary endpoints are associated mortality, morbidity, recurrence, drug side-effects and medico-economic analysis. DISCUSSION: The result of this trial is expected to provide the benefit of a nurse-led optimized multimodal and long-term interventional program for management of risk factors after stroke, personalizing the role of the nurse and including the patient's caregiver. TRIAL REGISTRATION: ClinicalTrials.gov, NCT 02132364. Registered on 7 May 2014. EUDRACT, A 00473-40.

STROKOG (stroke and cognition consortium): An international consortium to examine the epidemiology, diagnosis, and treatment of neurocognitive disorders in relation to cerebrovascular disease.

INTRODUCTION: The Stroke and Cognition consortium (STROKOG) aims to facilitate a better understanding of the determinants of vascular contributions to cognitive disorders and help improve the diagnosis and treatment of vascular cognitive disorders (VCD). METHODS: Longitudinal studies with ≥75 participants who had suffered or were at risk of stroke or TIA and which evaluated cognitive function were invited to join STROKOG. The consortium will facilitate projects investigating rates and patterns of cognitive decline, risk factors for VCD, and biomarkers of vascular dementia. RESULTS: Currently, STROKOG includes 25 (21 published) studies, with 12,092 participants from five continents. The duration of follow-up ranges from 3 months to 21 years. DISCUSSION: Although data harmonization will be a key challenge, STROKOG is in a unique position to reuse and combine international cohort data and fully explore patient level characteristics and outcomes. STROKOG could potentially transform our understanding of VCD and have a worldwide impact on promoting better vascular cognitive outcomes.

Aß1-40 and Aß1-42 Plasmatic Levels In Stroke: Influence of Pre-Existing Cognitive Status and Stroke Characteristics.

Many stroke patients have pre-existing cognitive impairment. Plasma amyloid ß peptides (Aß) - possible biomarkers of Alzheimer's pathology - induce vascular dysfunction. Our objective was to evaluate factors influencing plasma Aß1-40 and Aß1-42 peptides in a cohort of stroke patients. In the Biostroke study (ClinicalTrials.gov Identifier: NCT00763217), we collected vascular risk factors, neuroimaging features and biological tests including Aß1-40 and Aß1-42. We used the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) to systematically assess the pre-existing cognitive status. Of 403 patients (371 ischemia), 25 met criteria for pre-existing dementia, 142 for pre-existing cognitive decline-no-dementia, and 236 had no PCoI. Aß1-42 was independently associated with PCoI (odds ratio 0.973; 95% confidence interval: 0.950-0.996; p=0.024). Factors associated with plasma Aß1- 40 were age, smoking and diabetes mellitus. After exclusion of hemorrhagic strokes, the results remained unchanged, but blood samples taken less than 12 hours after onset were associated with lower plasma Aß1-40. Our results support a dissociated response of the 2 plasma Aß peptides in stroke patients, plasma Aß1-40 being involved in vascular aspects whereas Aß1-42 might be involved in neurodegenerative processes.

Proportion of single-chain recombinant tissue plasminogen activator and outcome after stroke.

OBJECTIVE: To determine whether the ratio single chain (sc)/(sc + 2 chain [tc]) recombinant tissue plasminogen activator (rtPA) influences outcomes in patients with cerebral ischemia. METHODS: We prospectively included consecutive patients treated with IV rtPA for cerebral ischemia in 13 stroke centers and determined the sc/(sc + tc) ratio in the treatment administered to each patient. We evaluated the outcome with the modified Rankin Scale (mRS) at 3 months (prespecified analysis) and occurrence of epileptic seizures (post hoc analysis). We registered Outcome of Patients Treated by IV Rt-PA for Cerebral Ischaemia According to the Ratio Sc-tPA/Tc-tPA (OPHELIE) under ClinicalTrials.gov identifier no. NCT01614080. RESULTS: We recruited 1,004 patients (515 men, median age 75 years, median onset-to-needle time 170 minutes, median NIH Stroke Scale score 10). We found no statistical association between sc/(sc + tc) ratios and handicap (mRS > 1), dependency (mRS > 2), or death at 3 months. Patients with symptomatic intracerebral hemorrhages had lower ratios (median 69% vs 72%, adjusted p = 0.003). The sc/(sc + tc) rtPA ratio did not differ between patients with and without seizures, but patients with early seizures were more likely to have received a sc/(sc + tc) rtPA ratio >80.5% (odds ratio 3.61; 95% confidence interval 1.26-10.34). CONCLUSIONS: The sc/(sc + tc) rtPA ratio does not influence outcomes in patients with cerebral ischemia. The capacity of rtPA to modulate NMDA receptor signaling might be associated with early seizures, but we observed this effect only in patients with a ratio of sc/(sc + tc) rtPA >80.5% in a post hoc analysis.

Which factors influence the resort to surrogate consent in stroke trials, and what are the patient outcomes in this context?

BACKGROUND: The provision of informed consent is a prerequisite for inclusion of a patient in a clinical research project. In some countries, the legislation on clinical research authorizes a third person to provide informed consent if the patient is unable to do so directly (i.e. surrogate consent). This is the case during acute stroke, when the symptoms may prevent the patient from providing informed consent and thus require a third party to be approached. Identification of factors associated with the medical team's decision to resort to surrogate consent may (i) help the care team during the inclusion process and (ii) enable the patient's family circle to be better informed (and thus feel less guilty) about providing surrogate consent. METHODS: Patients included in the BIOSTROKE cohort (initially dedicated to the analysis of factors influencing stroke severity) were divided into two groups: those having provided informed consent directly and those for whom a third party (such as a family member) had provided surrogate consent. We compared the groups in terms of the initial clinical characteristics (age, gender, type of stroke, severity on the National Institutes of Health Stroke Scale (NIHSS), pre-stroke cognitive status according to the Informant Questionnaire on Cognitive Decline in the Elderly, and the stroke's aetiology) and the functional and cognitive impairments (according to the NIHSS, the modified Rankin score (mRS) and the Mini Mental State Examination) on post-stroke days 8 and 90. RESULTS: Three hundred and ninety five patients were included (mean ± SD age: 67 ± 15 years; 53% males). Surrogate consent had been obtained in 228 cases, and 167 patients had provided consent themselves. The patients included with surrogate consent were likely to be older and more aphasic, with a pre-existing cognitive disorder and more severe stroke (relative to the patients having provided consent). In terms of recovery, the patients included with surrogate consent had a worse functional prognosis (day 90 mRS ≥3: 57.6%, compared with 16.8% in patients having provided consent themselves; p < 0.0001) and a worse cognitive prognosis (day 90 MMS < 24: 15.4% and 4.8%, respectively; p < 0.002). The mortality rate was significantly higher in the surrogate consent group. CONCLUSIONS: We found that in addition to age, aphasia and stroke severity, pre-stroke cognitive status is a factor that should prompt the care team to consider requesting surrogate consent for participation in a clinical study. Given that the unfavourable outcome in patients with surrogate consent is often due to their initial clinical state (rather than inclusion in a trial per se), the issue of the family's feelings of guilt (and how to avoid these feelings) should be further addressed.