Neuroimaging Markers of Brain Reserve and Associations with Delirium in Patients with Intracerebral Hemorrhage.

BACKGROUND

Delirium occurs frequently in patients with stroke, but the role of preexisting neural substrates in delirium pathogenesis remains unclear. We sought to explore associations between acute and chronic neural substrates of delirium in patients with intracerebral hemorrhage (ICH).

METHODS

Using data from a single-center ICH registry, we identified consecutive patients with acute nontraumatic ICH and available magnetic resonance imaging scans. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria were used to classify each patient as delirious or nondelirious during their hospitalization. Magnetic resonance imaging scans were processed and analyzed using semiautomated software, with volumetric measurement of acute ICH volume as well as white matter hyperintensity volume (WMHV) and gray and white matter volumes from the contralateral hemisphere. We tested associations between WMHV and incident delirium using multivariable regression models, and then determined the predictive accuracy of these neuroimaging models via area under the curve (AUC) analysis.

RESULTS

Of 139 patients in our cohort (mean [standard deviation] age 67.3 [17.3] years, 53% male), 58 (42%) patients experienced delirium. In our primary analyses, WMHV was significantly associated with delirium after adjusting for ICH features (odds ratio 1.56 per 10 cm3, 95% confidence interval 1.13-2.13), and this association was strengthened after further adjustment for segmented brain volume in patients with high-resolution scans (odds ratio 1.89 per 10 cm3, 95% confidence interval 1.24-2.86). Neuroimaging-based models predicted delirium with high accuracy (AUC 0.81), especially in patients with Glasgow Coma Scale score > 13 (AUC 0.85) and smaller ICH (AUC 0.91).

CONCLUSIONS

Chronic white matter disease is independently associated with delirium in patients with acute ICH, and neuroimaging biomarkers may have utility in predicting delirium occurrence.