22. European Stroke Conference 418 Behavioral disorders and post-stroke dementia Global Cognitive Functioning is Associated with Quality of Life (QOL) in Carotid Occlusion: Analysis from the Randomized Evaluation of Carotid Occlusion and Neurocognition (RE-CON) 508 © 2013 S. Karger AG, Basel Scientific Programme Trial. J.R. Festa1, M.A. Pavol2, Y.K. Cheung3, X. Jia4, R.L. Gubb5, W.J. Powers6, R.M. Lazar7, R.S. Marshall8 St. Luke’s-Roosevelt Hospitals; Columbia University, New York, USA1, Columbia Universtiy, New York, USA2, Columbia University, New York, USA3, Columbia University, New York, USA4, Washington University, St. Louis, USA5, University of North Carolina, Chapel Hill, USA6, Colum-bia University, New York, USA7, Columbia University, New York, USA8 Background: Cognitive dysfunction in patients with carotid occlusive disease is well established but the relationship between cognitive domains and QOL in carotid disease is unknown. We sought to determine the cognitive functions that had the greatest association with quality of life. Methods: We examined the relationship between cognitive functioning and QOL in 89 patients en-rolled in the RECON trial. Patients had symptomatic carotid artery occlusion; subjects diagnosed with dementia were excluded. We used a battery of standardized neuropsychological measures and the Stroke Specific Quality of Life Scale (SSQOL) to examine the relationship between neurocog-nition and QOL. Cognitive composite scores were derived from age-adjusted z-scores. We used linear regressions for demographic, stroke severity, and cognitive measures, with SSQOL as the out-come variable. Multivariate linear regression was used with cognitive scores and the SSQOL, ad-justing for significant factors. Results: Patients had mild to moderate cognitive dysfunction on the cognitive battery with an av-erage z-score of -1.34 SD below normative means on the composite score. A lower composite z-score was associated with a poor total SSQOL score (p=0.002), even after adjusting for depression and stroke severity (p=0.005). Post-hoc analyses showed that tests assessing global cognitive func-tioning (speed-dependent, multimodal processing) were independently significant. In addition, the composite score was significantly associated with 75% of the SSQOL subscales. Conclusions: Speed-dependent, multimodal processing had the greatest impact on QOL in patients with carotid occlusion across most domains. Quality of life may be particularly sensitive to changes in global cognitive function rather than focal stroke deficits such as aphasia or neglect. 419 Behavioral disorders and post-stroke dementia An early prediction of delirium in the acute phase after stroke A.W. Oldenbeuving1, P.L.M. de Kort2, L.J. Kappelle3, G. Roks4 St Elisabeth hospital, Tilburg, THE NETHERLANDS1, St Elisabeth hospital, Tilburg, THE NETHERLANDS2, UMC Utrecht, Utrecht, THE NETHERLANDS3, St Elisabeth hospital, Tilburg, THE NETHERLANDS4 Background: In this study we aim to develop and validate a simple risk score to predict delirium af-ter stroke. Methods: The risk score was derived from our prospective cohort study (n=527). Pre-existent cogni-tive decline, infection, right-sided hemispheric stroke, anterior circulation large-vessel stroke, stroke severity and age were independent predictors of delirium. Using the beta coefficients from the logis-tic regression model we allocated a score to values of the risk factors. In total we tested 3 models. In the first model NIH stroke scale, stroke subtype, infection, stroke localisation, Informant Question-naire on Cognitive Decline in the Elderly (IQCODE) above 50 and age were included. The second model included age, NIH stroke scale, stroke subtype and infection. A third model was more a sim-plified with only age and NIH stroke scale. The risk score was validated in an independent dataset (n = 332). Results: The AUC of the first model was 0.85 (95%CI 0.81-0.90) with a sensitivity and specificity of 86% and 74 %. In the second model the AUC was 0.84 (95%CI 0.80-0.89) with a sensitivity and specificity of 80% and 75%. The third model had an AUC of 0.80 (95%CI 0.75-0.85) with a sensi-tivity of 79% and a specificity of 73%. In the validation set model 1 resulted in an AUC of 0.83 (95%CI 0.76-0.90) with a sensitivity and specificity of 78% and 77%. The second had an AUC of 0.83 (95%CI 0.77-0.90) with a sensitivity and specificity of 76% and 81%. The third model gave an AUC of 0.82 (95%CI 0.75-0.89) with a sensitivity of 73% and a specificity of 75%. Given these results we conclude that model 2 is easy to use in clinical practice and slightly better than model 3. Model 2 was used to create risk tables. Conclusions: We created and validated a risk model which adequately predicts delirium risk and will facilitate early identification of stroke patients with high delirium risk in clinical practice.
Karger_ESC London_2013
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