The effect of different combinations of vascular, dependency and cognitive endpoints on the sample size required to detect a treatment effect in trials of treatments to improve outcome after lacunar and non-lacunar ischaemic stroke

Background Endpoints that are commonly used in trials of moderate/severe stroke may be less frequent in patients with minor, non-disabling stroke thus inflating sample sizes. We tested whether trial efficiency might be improved with composite endpoints. Methods We prospectively recruited patients with lacunar and minor non-lacunar ischaemic stroke (NIHSS ≤ 7) and assessed recurrent vascular events (stroke, transient ischaemic attack (TIA), ischemic heart disease (IHD)), modified Rankin Score (mRS) and cognitive testing with the Addenbrooke’s Cognitive Examination (ACE-R) one year post-stroke. For a potential secondary prevention randomised controlled trial (RCT), we estimated sample sizes using individual or combined outcomes, at power 80% (and 90%), alpha 5%, required to detect a relative 10% risk reduction. Results Amongst 264 patients (118 lacunar, 146 non-lacunar), at one year, 30/264 (11%) patients had a recurrent vascular event, 5 (2%) had died, 3 (1%) had clinically-diagnosed dementia, 53/264 (20%) had mRS ≥ 3 and 29/158 (19%) had ACE-R ≤ 82 (57 could not attend for cognitive testing). For a potential trial, at 80% power, using mRS ≥ 3 alone would require n > 5000 participants, recurrent vascular events alone n = 9908 participants, and a composite of any recurrent vascular event, ACE-R ≤ 82, dementia or mRS ≥ 2 (present in 56% of patients) n = 2224 patients. However, including cognition increased missing data. Results were similar for lacunar and non-lacunar minor ischaemic stroke. Conclusions Composite outcomes including vascular events, dependency, and cognition reduce sample size and increase efficiency, feasibility, and relevance to patients of RCTs in minor ischaemic stroke. Efficiency might be improved further with more practical cognitive test strategies

Variation in risk factors for recent small subcortical infarcts with infarct size, shape and location

BACKGROUND AND PURPOSE: Lacunar infarction is due to a perforating arteriolar abnormality. Possible causes include embolism, atheromatosis or intrinsic disease. We examined whether the size, shape or location of the lacunar infarct varied with embolic sources, systemic atheroma or vascular risk factors. METHODS: We examined data from three prospective studies of patients with clinical and diffusion-weighted imaging (DWI) positive symptomatic lacunar infarction who underwent full clinical assessment and investigation for stroke risk factors. Lacunar infarct size (maximum diameter; shape, oval/tubular; location, basal ganglia/centrum semiovale/brainstem) were coded blind to clinical details. RESULTS: Amongst 195 patients, 48 infarcts were tubular, 50 were 15-20mm diameter, 97 were in the basal ganglia and 74 in the centrum semiovale. There was no association between infarct size or shape and any risk factors. Centrum semiovale infarcts were less likely to have a potential relevant embolic source (4% v 11%, OR 0.16 95% confidence interval (CI) 0.03-0.83) and caused a lower National Institute of Health Stroke Scale (NIHSS) (2 v 3, OR 0.78 95% CI 0.62-0.98) than basal ganglia infarcts. There were no other differences by infarct location. CONCLUSIONS: Lacunar infarcts in the basal ganglia caused marginally more severe strokes and were three times as likely to have a potential embolic source than those in the centrum semiovale but the overall rate of carotid or known cardiac embolic sources (11%) was low. We found no evidence that other risk factors differed with location, size or shape suggesting that most lacunar infarcts share a common intrinsic arteriolar pathology

Impact of Small Vessel Disease Progression on Long-term Cognitive and Functional Changes after Stroke

Funding Information: This study was supported in part by the Wellcome Trust (WT088134/Z/09/A; funded most of the data collection and S.D.J.M). For the purpose of open access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript version arising from this submission. The 3-year follow-up was also funded by Chest, Heart Stroke Scotland:Res14/A157 (C.A.M.); support for the research was also received from NHS Research Scotland (V.C., F.D.); Row Fogo Charitable Trust Centre for Research Into Aging and the Brain (BROD.FID3668413; M.d.C.V.H., E.S.); the European Union Horizon 2020 project 666,881, SVDs@Target (F.M.C); Scottish Funding Council, Scottish Imaging Network, A Platform for Scientific Excellence Initiative; Chief Scientist Office Scotland (U.C.; CAF/18/08); Stroke Association Princess Margaret Research Development Fellowship (U.C.); and Stroke Association–Garfield Weston Foundation Senior Clinical Lectureship (FND;TSALECT 2015/04). Funding provided by the Fondation Leducq (16-CVD-05) and UK Dementia Research Institute (J.M.W.), funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK, is gratefully acknowledged.Peer reviewedPublisher PD

Small vessel disease and dietary salt intake: cross sectional study and systematic review

Background: Higher dietary salt intake increases the risk of stroke and may increase white matter hyperintensity (WMH) volume. We hypothesized that a long-term higher salt intake may be associated with other features of small vessel disease (SVD). Methods: We recruited consecutive patients with mild stroke presenting to the Lothian regional stroke service. We performed brain magnetic resonance imaging, obtained a basic dietary salt history, and measured the urinary sodium/creatinine ratio. We also carried out a systematic review to put the study in the context of other studies in the field. Results: We recruited 250 patients, 112 with lacunar stroke and 138 with cortical stroke, with a median age of 67.5 years. After adjustment for risk factors, including age and hypertension, patients who had not reduced their salt intake in the long term were more likely to have lacunar stroke (odds ratio [OR], 1.90; 95% confidence interval [CI], 1.10-3.29), lacune(s) (OR, 2.06; 95% CI, 1.09-3.99), microbleed(s) (OR, 3.4; 95% CI, 1.54, 8.21), severe WMHs (OR, 2.45; 95% CI 1.34-4.57), and worse SVD scores (OR, 2.17; 95% CI, 1.22-3.9). There was limited association between SVD and current salt intake or urinary sodium/creatinine ratio. Our systematic review found no previously published studies of dietary salt and SVD. Conclusion: The association between dietary salt and background SVD is a promising indication of a potential neglected contributory factor for SVD. These results should be replicated in larger, long-term studies using the recognized gold-standard measures of dietary sodium

Tracer kinetic assessment of blood–brain barrier leakage and blood volume in cerebral small vessel disease: Associations with disease burden and vascular risk factors

Funding Information: The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: Wellcome Trust [grant number WT088134/Z/09/A ; SDJM, FC]; Row Fogo Charitable Trust (MCVH, FC, AKH, PAA); Scottish Funding Council Scottish Imaging Network A Platform for Scientific Excellence collaboration (JMW); NHS Lothian R + D Department (MJT); the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK MRC, Alzheimer’s Research UK and the Alzheimer’s Society (MS, FC, ES, JMW); the Fondation Leducq Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease [reference number 16 CVD 05] (MS); and European Union Horizon 2020 [project number 666881, SVDs@Target] (MS, FC). We acknowledge the participants, their relatives, and carers for their participation in this study, and the staff of NHS Lothian Stroke Services and Brain Research Imaging Centre Edinburgh for their assistance in recruiting and assessing the patients.Peer reviewedPublisher PD

White matter hyperintensity reduction and outcomes after minor stroke

Objective: To assess factors associated with white matter hyperintensity (WMH) change in a large cohort after observing obvious WMH shrinkage 1 year after minor stroke in several participants in a longitudinal study. Methods: We recruited participants with minor ischemic stroke and performed clinical assessments and brain MRI. At 1 year, we assessed recurrent cerebrovascular events and dependency and repeated the MRI. We assessed change in WMH volume from baseline to 1 year (normalized to percent intracranial volume [ICV]) and associations with baseline variables, clinical outcomes, and imaging parameters using multivariable analysis of covariance, model of changes, and multinomial logistic regression. Results: Among 190 participants (mean age 65.3 years, range 34.3–96.9 years, 112 [59%] male), WMH decreased in 71 participants by 1 year. At baseline, participants whose WMH decreased had similar WMH volumes but higher blood pressure (p = 0.0064) compared with participants whose WMH increased. At 1 year, participants with WMH decrease (expressed as percent ICV) had larger reductions in blood pressure (β = 0.0053, 95% confidence interval [CI] 0.00099–0.0097 fewer WMH per 1–mm Hg decrease, p = 0.017) and in mean diffusivity in normal-appearing white matter (β = 0.075, 95% CI 0.0025–0.15 fewer WMH per 1-unit mean diffusivity decrease, p = 0.043) than participants with WMH increase; those with WMH increase experienced more recurrent cerebrovascular events (32%, vs 16% with WMH decrease, β = 0.27, 95% CI 0.047–0.50 more WMH per event, p = 0.018). Conclusions: Some WMH may regress after minor stroke, with potentially better clinical and brain tissue outcomes. The role of risk factor control requires verification. Interstitial fluid alterations may account for some WMH reversibility, offering potential intervention targets

Sex Differences in Poststroke Cognitive Impairment : A Multicenter Study in 2343 Patients With Acute Ischemic Stroke

Funding Information: Dr Exalto is supported by Alzheimer Nederland WE.03-2019-15 and Netherlands CardioVascular Research Initiative: the Dutch Heart Foundation (CVON 2018-28 & 2012-06). The Meta-VCI Map consortium is supported by Vici Grant 918.16.616 from The Netherlands Organisation for Health Research and Development (ZonMw) to Dr Biessels. Harmonization analyses were supported by a Rudolf Magnus Young Talent Fellowship from the University Medical Center Utrecht Brain Center to Dr Biesbroek. The CASPER cohort was supported by Maastricht University, Health Foundation Limburg, and Stichting Adriana van Rinsum-Ponsen. The CROMIS-2 cohort was funded by the UK Stroke Association and the British Heart Foundation (grant number TSA BHF 2009/01). The CU-STRIDE cohort was supported by the Health and Health Services Research Fund of the Food and Health Bureau of the Government of Hong Kong (grant number 0708041), the Lui Che Woo Institute of Innovative Medicine, and Therese Pei Fong Chow Research Center for Prevention of Dementia. The GRECogVASC cohort was funded by Amiens University Hospital and by a grant from the French Ministry of Health (grant number DGOS R1/2013/144). The MSS-2 cohort is funded by the Wellcome Trust (grant number WT088134/Z/09/A to Dr Wardlaw) and the Row Fogo Charitable Trust. The PROCRAS cohort was funded via ZonMW as part of the TopZorg project in 2015 (grant number 842003011). The CODECS cohort (ongoing) is supported by a grant from Stichting Coolsingel (grant number 514). The Bundang VCI and Hallym VCI cohort groups do not wish to report any relevant funding sources. At the time of contribution, Dr Hamilton was funded by the College of Medicine and Veterinary Medicine at the University of Edinburgh and was supported by the Wellcome Trust through the Translational Neuroscience PhD program at the University of Edinburgh. Publisher Copyright: © 2023 Lippincott Williams and Wilkins. All rights reserved.Peer reviewedPublisher PD

Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models

Background: Cerebral small vessel disease (SVD) contributes to 45% of dementia cases worldwide, yet we lack a reliable model for predicting dementia in SVD. Past attempts largely relied on traditional statistical approaches. Here, we investigated whether machine learning (ML) methods improved prediction of incident dementia in SVD from baseline SVD-related features over traditional statistical methods. Methods: We included three cohorts with varying SVD severity (RUN DMC, n = 503; SCANS, n = 121; HARMONISATION, n = 265). Baseline demographics, vascular risk factors, cognitive scores, and magnetic resonance imaging (MRI) features of SVD were used for prediction. We conducted both survival analysis and classification analysis predicting 3-year dementia risk. For each analysis, several ML methods were evaluated against standard Cox or logistic regression. Finally, we compared the feature importance ranked by different models. Results: We included 789 participants without missing data in the survival analysis, amongst whom 108 (13.7%) developed dementia during a median follow-up of 5.4 years. Excluding those censored before three years, we included 750 participants in the classification analysis, amongst whom 48 (6.4%) developed dementia by year 3. Comparing statistical and ML models, only regularised Cox/logistic regression outperformed their statistical counterparts overall, but not significantly so in survival analysis. Baseline cognition was highly predictive, and global cognition was the most important feature. Conclusions: When using baseline SVD-related features to predict dementia in SVD, the ML survival or classification models we evaluated brought little improvement over traditional statistical approaches. The benefits of ML should be evaluated with caution, especially given limited sample size and features

Sex differences in post-stroke cognitive impairment: a multicenter study in 2343 patients with acute ischemic stroke

No abstract available

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

© 2022 The Author(s)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/.N