Racial and socioeconomic disparities in incidence of hospital-acquired complications following cerebrovascular procedures.

BACKGROUND: Patients with cerebrovascular disease undergo complex surgical procedures, often requiring prolonged inpatient hospitalization. Previous studies have demonstrated associations between racial/demographic factors and clinical outcomes in patients undergoing cerebrovascular procedures (CVPs). The Centers for Medicare and Medicaid Services have published a series of 11 hospital-acquired conditions (HACs) deemed "reasonably preventable" for which related costs of treatment are not reimbursed. We hypothesize that race and payer status disparities impact HAC frequency in patients undergoing CVPs and that HAC incidence is associated with length of stay and hospital costs. OBJECTIVE: To assess health disparities in HACs among the cerebrovascular neurosurgical patient population. METHODS: Data were collected from the Nationwide Inpatient Sample (NIS) database from 2002 to 2010. CVPs and HACs were identified by International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic and procedure codes. HAC incidence was evaluated according to demographics including race, payer status, and median zip code income via multivariable analysis. Secondary outcomes of interest included length of stay and resulting inpatient charges. RESULTS: From 2002 to 2010, there were 1 290 883 CVP discharges with an HAC rate of 0.5%. Significant disparities in HAC frequency existed according to ethnicity and insurance provider. Minorities and Medicaid patients had increased frequency of HACs (P < .05), as well as prolonged length of stay and higher inpatient costs (P < .05). CONCLUSION: HAC incidence is associated with racial and socioeconomic factors in patients who undergo CVPs. Awareness of these disparities may lead to improved processes and protocol implementation, which might help to decrease the frequency of these potentially avoidable events.

Image coregistration: quantitative processing framework for the assessment of brain lesions.

The quantitative, multiparametric assessment of brain lesions requires coregistering different parameters derived from MRI sequences. This will be followed by analysis of the voxel values of the ROI within the sequences and calculated parametric maps, and deriving multiparametric models to classify imaging data. There is a need for an intuitive, automated quantitative processing framework that is generalized and adaptable to different clinical and research questions. As such flexible frameworks have not been previously described, we proceeded to construct a quantitative post-processing framework with commonly available software components. Matlab was chosen as the programming/integration environment, and SPM was chosen as the coregistration component. Matlab routines were created to extract and concatenate the coregistration transforms, take the coregistered MRI sequences as inputs to the process, allow specification of the ROI, and store the voxel values to the database for statistical analysis. The functionality of the framework was validated using brain tumor MRI cases. The implementation of this quantitative post-processing framework enables intuitive creation of multiple parameters for each voxel, facilitating near real-time in-depth voxel-wise analysis. Our initial empirical evaluation of the framework is an increased usage of analysis requiring post-processing and increased number of simultaneous research activities by clinicians and researchers with non-technical backgrounds. We show that common software components can be utilized to implement an intuitive real-time quantitative post-processing framework, resulting in improved scalability and increased adoption of post-processing needed to answer important diagnostic questions.