Original Investigation
April 2016

Factors Associated With 8-Year Mortality in Older Patients With Cerebral Small Vessel DiseaseThe Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC) Study

Author Affiliations
  • 1Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Center for Neuroscience, Department of Neurology, Nijmegen, the Netherlands
  • 2Radboud University, Donders Institute for Brain, Cognition and Behaviour, Center for Cognitive Neuroimaging, Nijmegen, the Netherlands
  • 3HagaZiekenhuis Den Haag, Department of Neurology, Den Haag, the Netherlands
  • 4Amphia ziekenhuis Breda, Department of Neurology, Breda, the Netherlands
  • 5Erwin L. Hahn Institute for Magnetic Resonance Imaging, UNESCO-Weltkulturerbe Zollverein, Essen, Germany
  • 6MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, the Netherlands
  • 7Department of Clinical Neurosciences, University of Cambridge, Cambridge, England

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Neurol. 2016;73(4):402-409. doi:10.1001/jamaneurol.2015.4560

Importance  Gait and cognition have been related to mortality in population-based studies. This association is possibly mediated by cerebral small vessel disease (SVD), which has been associated with mortality as well. It is unknown which factors can predict mortality in individuals with SVD. Identification of high-risk patients may provide insight into factors that reflect their vital health status.

Objectives  To assess mortality in patients with cerebral SVD and identify potential clinical and/or imaging factors associated with mortality.

Design, Setting, and Participants  A prospective, single-center cohort study was conducted. The present investigation is embedded in the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC) study. Between January 17, 2006, and February 27, 2007, all participants underwent a cognitive and motor assessment and cerebral magnetic resonance imaging (MRI) including a diffusion tensor imaging sequence to assess microstructural integrity of the white matter. Participants were followed up until their death or November 24, 2014. Participants included 503 older adults with SVD noted on brain imaging. Data analysis was performed from November 26, 2014, to February 2, 2015.

Main Outcomes and Measures  Eight-year all-cause mortality.

Results  Of 503 participants (mean [SD] age, 65.7 [8.8] years; range, 50-85 years; 284 [56.5%] were male), 80 individuals (15.9%) died during a mean (SD) follow-up of 7.8 (1.5) years. In the final analysis, 494 (98.2%) were included, of whom 78 (15.8%) died. Gait speed, cognitive index, conventional MRI markers of SVD (white matter hyperintensity volume, brain volume, and lacunes), and diffusion measures of the white matter were associated with an 8-year risk of mortality independent of age, sex, and vascular risk factors. The prediction of mortality was determined using Cox proportional hazards models with backward stepwise selection and including age, sex, vascular risk factors, gait speed, cognitive index, MRI, and diffusion measures. Results are reported as hazard ratios (HRs) (95% CI). Older age (1.05 per 1-year increase [1.01-1.08]), lower gait speed (1.15 per 0.1-m/s slower gait [1.06-1.24]), lower gray matter volume (0.72 per 1-SD increase [0.55-0.95]), and greater global mean diffusivity of the white matter (1.51 per 1-SD increase [1.19-1.92]) were identified as the main factors associated with mortality. Cognitive index and other conventional SVD markers were not retained in the prediction model.

Conclusions and Relevance  Gait, cognition, and imaging markers of SVD are associated with 8-year risk of mortality. In the prediction of mortality, an older age, lower gait speed, lower gray matter volume, and greater global mean diffusivity of white matter at baseline best predicted mortality in our population. Further research is needed to investigate the reproducibility of this prediction model and to elucidate the association between the factors identified and mortality.