ArticleBMJ open2017
Development and validation of a priori risk model for extensive white matter lesions in people age 65 years or older: the Dijon MRI study.
Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Impact of blood pressure variability and cerebral small vessel disease: A systematic review and meta-analysis.Heliyon · 2024Article
- Development and validation of a predictive model for white matter lesions in young- and middle-aged people.Frontiers in neurology · 2023Article
- Structural and functional MRI correlates of T2 hyperintensities of brain white matter in young neurologically asymptomatic adults.European radiology · 2019Article
- A Neuroimaging Marker Based on Diffusion Tensor Imaging and Cognitive Impairment Due to Cerebral White Matter Lesions.Frontiers in neurology · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe objective was to develop and validate a risk model for the likelihood of extensive white matter lesions (extWML) to inform clinicians on whether to proceed with or forgo diagnostic MRI.
designPopulation-based cohort study and multivariable prediction model.
settingTwo representative samples from France.
participantsPersons aged 60-80 years without dementia or stroke. Derivation sample n=1714; validation sample n=789. PRIMARY AND SECONDARY OUTCOME MEASURES: Volume of extWML (log cm
resultsThe multivariable prediction model included six clinical risk factors (C-statistic=0.61). A cut-off of 7 points on the multivariable prediction model yielded the optimum balance in sensitivity 63.7% and specificity 54.0% and the negative predictive value was high (81.8%), but the positive predictive value was low (31.5%). In further validation, incident stroke risk was associated with continuous scores on the multivariable prediction model (HR 1.02; 95% CI 1.01 to 1.04, P=0.02) and dichotomised scores from the multivariable prediction model (HR 1.28; 95% CI 1.02 to 1.60, P=0.03).
conclusionsA simple clinical risk equation for WML constituted by six variables can inform decisions whether to proceed with or forgo brain MRI. The high-negative predictive value demonstrates potential to reduce unnecessary MRI in the population aged 60-80 years.
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