Predictors of mortality in cerebral small vessel disease patients: The RUN DMC Study

Background: Information about life expectancy is very important in medical decision making. Recent attention has been drawn to gait speed, cognitive function and diverse cerebral imaging parameters as reliable and easily obtainable markers of mortality. However, prediction models combining these different markers are currently lacking and these markers have never been investigated in specific groups of elderly, including older adults with cerebral small vessel disease (SVD).
Objective: to investigate the relationship between clinical and imaging parameters and all-cause mortality in elderly with SVD.

Methods: 503 independently living elderly participants with SVD on MRI were included (2006). Vital status and cause of death were collected through November, 2014. Cox regression analysis was used to identify baseline clinical and imaging predictors of all-cause mortality, adjusted for age, sex and vascular risk factors. Prediction models of all-cause mortality were constructed to study which parameters were most predictive.

Results: 80 participants died. Mean follow-up duration was 7.8 years and cumulative 7.5-year risk of mortality was 13.2%. In the prediction of all-cause mortality, gait speed (HR1.13 per 0.1 m/s slower gait, 95% CI:1.04–1.22), gray matter volume (HR0.75 per standard deviation (SD) increase, 95% CI:0.56–0.99) and structural integrity of the white matter, assessed by diffusion tensor imaging using mean diffusivity (HR1.53 per SD increase, 95% CI:1.19–1.96) had the best predictive ability, next to age, sex and vascular risk factors.

Conclusions: Gait speed, gray matter volume and structural integrity of the white matter are the main predictors of mortality at 7.5-years follow-up in elderly with SVD and could possibly be targets for preventive interventions.