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Background: White matter hyperintensities (WMH) are associated with cognitive dysfunction after ischemic stroke. Yet, uncertainty remains about affected domains, the role of other preexisting brain injury, and infarct types in the relation between WMH burden and poststroke cognition. We aimed to disentangle these factors in a large sample of patients with ischemic stroke from different cohorts., Methods: We pooled and harmonized individual patient data (n=1568) from 9 cohorts, through the Meta VCI Map consortium (www.metavcimap.org). Included cohorts comprised patients with available magnetic resonance imaging and multidomain cognitive assessment <15 months poststroke. In this individual patient data meta-analysis, linear mixed models were used to determine the association between WMH volume and domain-specific cognitive functioning ( Z scores; attention and executive functioning, processing speed, language and verbal memory) for the total sample and stratified by infarct type. Preexisting brain injury was accounted for in the multivariable models and all analyses were corrected for the study site as a random effect., Results: In the total sample (67 years [SD, 11.5], 40% female), we found a dose-dependent inverse relationship between WMH volume and poststroke cognitive functioning across all 4 cognitive domains (coefficients ranging from -0.09 [SE, 0.04, P =0.01] for verbal memory to -0.19 [SE, 0.03, P <0.001] for attention and executive functioning). This relation was independent of acute infarct volume and the presence of lacunes and old infarcts. In stratified analyses, the relation between WMH volume and domain-specific functioning was also largely independent of infarct type., Conclusions: In patients with ischemic stroke, increasing WMH volume is independently associated with worse cognitive functioning across all major domains, regardless of old ischemic lesions and infarct type., Competing Interests: Disclosures Dr H.-J. Bae reports grants from Chong Gun Dang Pharmaceutical Corp and Korean Drug Co, Ltd outside of the submitted work. Dr G.J. Biessels reports grants from The Netherlands Organisation for Health Research and Development (ZonMW), during the conduct of the study. The other authors report no conflicts.

Background: Identification of risk factors and causes of stroke is key to optimize treatment and prevent recurrence. Up to one-third of young patients with stroke have a cryptogenic stroke according to current classification systems (Trial of ORG 10172 in Acute Stroke Treatment [TOAST] and atherosclerosis, small vessel disease, cardiac pathology, other causes, dissection [ASCOD]). The aim was to identify risk factors and leads for (new) causes of cryptogenic ischemic stroke in young adults, using the pediatric classification system from the IPSS study (International Pediatric Stroke Study)., Methods: This is a multicenter prospective cohort study conducted in 17 hospitals in the Netherlands, consisting of 1322 patients aged 18 to 49 years with first-ever, imaging confirmed, ischemic stroke between 2013 and 2021. The main outcome was distribution of risk factors according to IPSS classification in patients with cryptogenic and noncryptogenic stroke according to the TOAST and ASCOD classification., Results: The median age was 44.2 years, and 697 (52.7%) were men. Of these 1322 patients, 333 (25.2%) had a cryptogenic stroke according to the TOAST classification. Additional classification using the ASCOD criteria reduced the number patients with cryptogenic stroke from 333 to 260 (19.7%). When risk factors according to the IPSS were taken into account, the number of patients with no potential cause or risk factor for stroke reduced to 10 (0.8%)., Conclusions: Among young adults aged 18 to 49 years with a cryptogenic ischemic stroke according to the TOAST classification, risk factors for stroke are highly prevalent. Using a pediatric classification system provides new leads for the possible causes in cryptogenic stroke, and could potentially lead to more tailored treatment for young individuals with stroke.

Background and Objectives: Causes of stroke in young adults differ from those in the elderly individuals, and in a larger percentage, no cause can be determined. To gain more insight into the etiology of (cryptogenic) stroke in the young population, we investigated whether trigger factors, such as short-lasting exposure to toxins or infection, may play a role., Methods: Patients aged 18-49 years with a first-ever ischemic stroke or intracerebral hemorrhage (ICH) in 17 participating centers in the Netherlands completed a questionnaire about exposure to 9 potential trigger factors in hazard periods and on a regular yearly basis. A case-crossover design was used to assess relative risks (RRs) with 95% confidence intervals (95% CIs) by the Mantel-Haenszel case-crossover method, for any stroke (ischemic stroke and ICH combined) and for different etiologic subgroups of ischemic stroke., Results: One thousand one hundred forty-six patients completed the questionnaire (1,043 patients with an ischemic stroke and 103 with an ICH, median age 44.0 years, 52.6% men). For any stroke, an increased risk emerged within 1 hour of cola consumption (RR 2.0, 95% CI 1.5-2.8) and vigorous physical exercise (RR 2.6, 95% CI 2.2-3.0), within 2 hours after sexual activity (RR 2.4, 95% CI 1.6-3.5), within 4 hours after illicit drug use (RR 2.8, 95% CI 1.7-4.9), and within 24 hours after fever or flu-like disease (RR 14.1, 95% CI 10.5-31.2; RR 13.9, 95% CI 8.9-21.9). Four trigger factors increased the risk of other determined and cryptogenic ischemic stroke, 3 that of cardioembolic stroke, 2 that of large vessel atherosclerosis and likely atherothrombotic stroke combined and stroke with multiple causes, and none that of stroke due to small vessel disease., Discussion: We identified cola consumption, vigorous physical exercise, sexual activity, illicit drug use, fever, and flu-like disease as potential trigger factors for stroke in the young population and found differences in the type and number of trigger factors associated with different etiologic subgroups of ischemic stroke. These findings might help in better understanding the pathophysiologic mechanisms of (cryptogenic) stroke in the young population., (© 2022 American Academy of Neurology.)

Background: We evaluated data from all patients in the Netherlands who underwent endovascular treatment for acute ischemic stroke in the past 3.5 years, to identify nationwide trends in time to treatment and procedural success, and assess their effect on clinical outcomes., Methods: We included patients with proximal occlusions of the anterior circulation from the second and first cohorts of the MR CLEAN (Multicenter Randomized Clinical trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Registry (March 2014 to June 2016; June 2016 to November 2017, respectively). We compared workflow times and rates of successful reperfusion (defined as an extended Thrombolysis in Cerebral Infarction score of 2B-3) between cohorts and chronological quartiles (all included patients stratified in chronological quartiles of intervention dates to create equally sized groups over the study period). Multivariable ordinal logistic regression was used to assess differences in the primary outcome (ordinal modified Rankin Scale at 90 days)., Results: Baseline characteristics were similar between cohorts (second cohort n=1692, first cohort n=1488) except for higher age, poorer collaterals, and less signs of early ischemia on computed tomography in the second cohort. Time from stroke onset to groin puncture and reperfusion were shorter in the second cohort (median 185 versus 210 minutes; P <0.001 and 236 versus 270 minutes; P <0.001, respectively). Successful reperfusion was achieved more often in the second than in the first cohort (72% versus 66%; P <0.001). Functional outcome significantly improved (adjusted common odds ratio 1.23 [95% CI, 1.07-1.40]). This effect was attenuated by adjustment for time from onset to reperfusion (adjusted common odds ratio, 1.12 [95% CI, 0.98-1.28]) and successful reperfusion (adjusted common odds ratio, 1.13 [95% CI, 0.99-1.30]). Outcomes were consistent in the analysis per chronological quartile., Conclusions: Clinical outcomes after endovascular treatment for acute ischemic stroke in routine clinical practice have improved over the past years, likely resulting from improved workflow times and higher successful reperfusion rates.

Background and Purpose: Prediction of long-term recovery of a poststroke cognitive disorder (PSCD) is currently inaccurate. We assessed whether diffusion-weighted imaging (DWI)-based measures of brain connectivity predict cognitive recovery 1 year after stroke in patients with PSCD in addition to conventional clinical, neuropsychological, and imaging variables., Methods: This prospective monocenter cohort study included 217 consecutive patients with a clinical diagnosis of ischemic stroke, aged ≥50 years, and Montreal Cognitive Assessment score below 26 during hospitalization. Five weeks after stroke, patients underwent DWI magnetic resonance imaging. Neuropsychological assessment was performed 5 weeks and 1 year after stroke and was used to classify PSCD as absent, modest, or marked. Cognitive recovery was operationalized as a shift to a better PSCD category over time. We evaluated 4 DWI-based measures of brain connectivity: global network efficiency and mean connectivity strength, both weighted for mean diffusivity and fractional anisotropy. Conventional predictors were age, sex, level of education, clinical stroke characteristics, neuropsychological variables, and magnetic resonance imaging findings (eg, infarct size). DWI-based measures of brain connectivity were added to a multivariable model to assess additive predictive value., Results: Of 135 patients (mean age, 71 years; 95 men [70%]) with PSCD 5 weeks after ischemic stroke, 41 (30%) showed cognitive recovery. Three of 4 brain connectivity measures met the predefined threshold of P <0.1 in univariable regression analysis. There was no added value of these measures to a multivariable model that included level of education and infarct size as significant predictors of cognitive recovery., Conclusions: Current DWI-based measures of brain connectivity appear to predict recovery of PSCD but at present have no added value over conventional predictors.

Background and Purpose- It is uncertain what determines the potential for cognitive recovery after ischemic stroke. The extent to which strategic areas of the brain network, so-called hubs, are affected by the infarct could be a key factor. We developed a lesion impact score, which estimates the damage to network hubs by integrating information on infarct size with healthy brain network topology. We verified whether the lesion impact score indeed reflects global network disturbances in patients and assessed if it could predict cognitive recovery. Methods- Seventy-five ischemic stroke patients without signs of a prestroke cognitive disorder were included, all with evidence of a cognitive disorder during hospitalization. A brain magnetic resonance imaging and neuropsychological assessment were performed 5 weeks (±1 week) after stroke. Neuropsychological testing was repeated after 1 year to assess cognitive recovery. Brain networks were reconstructed from diffusion-weighted data and consisted of 90 gray matter regions (ie, network nodes). A standard brain network map, indicating the hub-score of each node, was obtained from network data of 44 cognitively healthy adults. For each patient, we calculated the lesion impact score by multiplying the percentage of node volume affected by the infarct with the node's corresponding hub-score. The patients' maximum lesion impact score was used as outcome predictor. Results- A higher lesion impact score in patients, indicating an increasing infarct size in nodes with a higher hub-score, was related to lower global brain network efficiency (β=-0.528 [-0.776 to -0.277]; P <0.001), independent of age, brain volume, infarct volume, and white matter hyperintensity severity. A lower lesion impact score, however, was an independent predictor of cognitive recovery 1 year after stroke (odds ratio=0.434 [0.193-0.978]; P =0.044). Conclusions- We introduced a lesion impact score that combines information on infarct size and network topology to predict long-term recovery after stroke. This score can potentially be used in a clinical setting, also without availability of high-resolution diffusion-weighted magnetic resonance imaging.

Objective: To investigate the influence of cognitive performance on long-term functional outcome after ischemic stroke (IS) in young adults aged 18 through 50 years (young IS)., Methods: This study is part of a prospective cohort study among 277 stroke survivors with a young IS admitted to our department between January 1, 1980, and November 1, 2010. Functional outcome was assessed during follow-up between 2009 and 2012 with the modified Rankin Scale (mRS) and Instrumental Activities of Daily Living scale (IADL). Extensive neuropsychological investigation was performed. Logistic regression was used to calculate odds ratios (ORs) for a poor functional outcome (mRS >2 or IADL <8) for the 7 cognitive domains adjudicated for confounders. Cognitive function (continuous) as well as cognitive impairment (dichotomous) were studied., Results: Only decline in working memory (OR 0.3, 95% confidence interval [CI] 0.1-0.6) was associated with poor functional outcome on the mRS. Except for decline in processing speed (OR 0.5, 95% CI 0.3-0.8) and working memory (OR 0.4, 95% CI 0.2-0.7), no relation was found with poor functional outcome on IADL. Impairment in none of the individual cognitive domains was related to long-term functional outcome, although impairment in global cognitive function was related to a poor functional outcome on the IADL (OR 4.8, 95% CI 1.7-14.0)., Conclusions: On average, 11 years after young IS there was no clear relationship between long-term cognitive deficits and long-term functional outcome or IADL, stressing the need for further prospective studies with further development of sensitive measures of functional prognosis., (© 2015 American Academy of Neurology.)