ArticleTrials2017
Sample size considerations for trials using cerebral white matter hyperintensity progression as an intermediate outcome at 1 year after mild stroke: results of a prospective cohort study.
Article in Trials, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Inconsistent Classification of Mild Stroke and Implications on Health Services Delivery.Archives of physical medicine and rehabilitation · 2020Pooled it
- Cerebrospinal Fluid Volume and Other Intracranial Volumes Are Associated with Fazekas Score in Adults: A Single Center Experience.Medicina (Kaunas, Lithuania) · 2025Article
- Prediction of white matter hyperintensities evolution one-year post-stroke from a single-point brain MRI and stroke lesions information.Scientific reports · 2025Article
- Design of trials in lacunar stroke and cerebral small vessel disease: review and experience with the LACunar Intervention Trial 2 (LACI-2).Stroke and vascular neurology · 2024Review
- Blood Pressure and Vascular Cognitive Impairment.Stroke · 2022Review
- ESO Guideline on covert cerebral small vessel disease.European stroke journal · 2021Article
- Rationale and design of a longitudinal study of cerebral small vessel diseases, clinical and imaging outcomes in patients presenting with mild ischaemic stroke: Mild Stroke Study 3.European stroke journal · 2021Article
- Protocol: The Lacunar Intervention Trial 2 (LACI-2). A trial of two repurposed licenced drugs to prevent progression of cerebral small vessel disease.European stroke journal · 2020Article
- Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks.Medical image analysis · 2020Article
- Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration.Alzheimer's & dementia (Amsterdam, Netherlands) · 2019Review
- Small vessels, dementia and chronic diseases - molecular mechanisms and pathophysiology.Clinical science (London, England : 1979) · 2018Article
- Advanced Neuroimaging of Cerebral Small Vessel Disease.Current treatment options in cardiovascular medicine · 2017Review
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Authors and funding
9 authors.
Funding
Abstract
backgroundWhite matter hyperintensities (WMHs) are commonly seen on in brain imaging and are associated with stroke and cognitive decline. Therefore, they may provide a relevant intermediate outcome in clinical trials. WMH can be measured as a volume or visually on the Fazekas scale. We investigated predictors of WMH progression and design of efficient studies using WMH volume and Fazekas score as an intermediate outcome.
methodsWe prospectively recruited 264 patients with mild ischaemic stroke and measured WMH volume, Fazekas score, age and cardiovascular risk factors at baseline and 1 year. We modelled predictors of WMH burden at 1 year and used the results in sample size calculations for hypothetical randomised controlled trials with different analysis plans and lengths of follow-up.
resultsFollow-up WMH volume was predicted by baseline WMH: a 0.73-ml (95% CI 0.65-0.80, p < 0.0001) increase per 1-ml baseline volume increment, and a 2.93-ml increase (95% CI 1.76-4.10, p < 0.0001) per point on the Fazekas scale. Using a mean difference of 1 ml in WMH volume between treatment groups, 80% power and 5% alpha, adjusting for all predictors and 2-year follow-up produced the smallest sample size (n = 642). Other study designs produced samples sizes from 2054 to 21,270. Sample size calculations using Fazekas score as an outcome with the same power and alpha, as well as an OR corresponding to a 1-ml difference, were sensitive to assumptions and ranged from 2504 to 18,886.
conclusionsBaseline WMH volume and Fazekas score predicted follow-up WMH volume. Study size was smallest using volumes and longer-term follow-up, but this must be balanced against resources required to measure volumes versus Fazekas scores, bias due to dropout and scanner drift. Samples sizes based on Fazekas scores may be best estimated with simulation studies.
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