The role of client characteristics, system characteristics, and interventions in ischemic stroke outcomes

The purpose of this study was to utilize an administrative database to examine differences in system characteristics, client characteristics, and interventions as they influence identified stroke outcomes. Additionally, the study investigated differences that might exist in clinical evaluation and treatment between men and women presenting with ischemic stroke that might indicate a gender bias. The Quality Health Outcomes Model was used as the conceptual framework for this study. MANCOVA, chi-square, multiple regression, and logistic regression were used to test the research questions on a sample of 867 patients, aged 23–100 years, from a large administrative Stroke Database.

MANOVA results indicated significant differences between males and females for total costs (p = .033) and mean admission age ( p = .000). The chi-square analysis demonstrated a significant difference for the following: neurology consultation and gender (p = .005); Carotid Doppler Study (p = .003) and endarterectomy (p = .007) interventions and gender; mortality ( p = .009) and discharge disposition (p = .000) and gender. Males received both procedures at a higher percentage than females. Females had a higher mortality.

The total cost multiple regression model indicated that nursing cost (p = .007), endarterectomy (p = .000), APR severity “4” (p = .000), first care unit medical surgical (p = .000), attending physician specialty ( p = .016), and neurologists consulting (p = .025) were all significant predictors. The model successfully predicted 86.5% of the variance for total cost. The LOS multiple regression model indicated that nursing cost (p = .000), first care unit medical surgical (p = .000), coumadin (p = .000), APR severity “4” (p = .000), APR risk of mortality “4” (p = .000), APR severity “3” (p = .008), first care unit ICU (p = .001), Caucasian ( p = .001), APR risk of mortality “1” (p = .007), and endarterectomy (p = .020) were all significant predictors of LOS. This model successfully predicted 76.8% of the variance for LOS.

The logistic regression model demonstrated that the significant predictors of mortality included: Carotid Doppler Study, LOS, APR mortality, nursing costs, aspirin, coumadin, and ED patient. Goodness-of-fit tests reveal that the model is a good fit for the data. The results indicate that there appear to be gender issues related to age of stroke onset and access to some interventions and treatments that are common in stroke management. Complex interactions between patients and within genders exist which need further investigation.