Browsing by Subject "acute stroke"

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    Age, Sex, and Racial Differences in Neuroimaging Use in Acute Stroke: A Population-Based Study
    (2017-10) Vagal, A.; Sanelli, P.; Sucharew, H.; Alwell, K. A.; Khoury, J. C.; Khatri, P.; Woo, D.; Flaherty, M.; Kissela, B. M.; Adeoye, O.; Ferioli, S.; De Los Rios La Rosa, F.; Martini, S.; Mackey, Jason; Kleindorfer, D.; Neurology, School of Medicine
    BACKGROUND AND PURPOSE: Limited information is available regarding differences in neuroimaging use for acute stroke work-up. Our objective was to assess whether race, sex, or age differences exist in neuroimaging use and whether these differences depend on the care center type in a population-based study. MATERIALS AND METHODS: Patients with stroke (ischemic and hemorrhagic) and transient ischemic attack were identified in a metropolitan, biracial population using the Greater Cincinnati/Northern Kentucky Stroke Study in 2005 and 2010. Multivariable regression was used to determine the odds of advanced imaging use (CT angiography/MR imaging/MR angiography) for race, sex, and age. RESULTS: In 2005 and 2010, there were 3471 and 3431 stroke/TIA events, respectively. If one adjusted for covariates, the odds of advanced imaging were higher for younger (55 years or younger) compared with older patients, blacks compared with whites, and patients presenting to an academic center and those seen by a stroke team or neurologist. The observed association between race and advanced imaging depended on age; in the older age group, blacks had higher odds of advanced imaging compared with whites (odds ratio, 1.34; 95% CI, 1.12–1.61; P < .01), and in the younger group, the association between race and advanced imaging was not statistically significant. Age by race interaction persisted in the academic center subgroup (P < .01), but not in the nonacademic center subgroup (P = .58). No significant association was found between sex and advanced imaging. CONCLUSIONS: Within a large, biracial stroke/TIA population, there is variation in the use of advanced neuroimaging by age and race, depending on the care center type.
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    Estimating and Reporting on the Quality of Inpatient Stroke Care by Veterans Health Administration Medical Centers
    (2012-01) Arling, Greg; Reeves, Mathew; Ross, Joseph S.; Williams, Linda S.; Keyhani, Salomeh; Chumbler, Neale R.; Phipps, Michael S.; Roumie, Christianne L; Myers, Laura J.; Salanitro, Amanda H; Ordin, Diana L.; Myers, Jennifer; Bravata, Dawn M.
    Background—Reporting of quality indicators (QIs) in Veterans Health Administration Medical Centers is complicated by estimation error caused by small numbers of eligible patients per facility. We applied multilevel modeling and empirical Bayes (EB) estimation in addressing this issue in performance reporting of stroke care quality in the Medical Centers. Methods and Results—We studied a retrospective cohort of 3812 veterans admitted to 106 Medical Centers with ischemic stroke during fiscal year 2007. The median number of study patients per facility was 34 (range, 12–105). Inpatient stroke care quality was measured with 13 evidence-based QIs. Eligible patients could either pass or fail each indicator. Multilevel modeling of a patient's pass/fail on individual QIs was used to produce facility-level EB-estimated QI pass rates and confidence intervals. The EB estimation reduced interfacility variation in QI rates. Small facilities and those with exceptionally high or low rates were most affected. We recommended 8 of the 13 QIs for performance reporting: dysphagia screening, National Institutes of Health Stroke Scale documentation, early ambulation, fall risk assessment, pressure ulcer risk assessment, Functional Independence Measure documentation, lipid management, and deep vein thrombosis prophylaxis. These QIs displayed sufficient variation across facilities, had room for improvement, and identified sites with performance that was significantly above or below the population average. The remaining 5 QIs were not recommended because of too few eligible patients or high pass rates with little variation. Conclusions—Considerations of statistical uncertainty should inform the choice of QIs and their application to performance reporting.