Abstract HUP17: Thrombectomy Outcomes In Relation To Socioeconomic Status By Utilizing Income And Area Deprivation Index Data

Abstract

Introduction: Socioeconomic status has been shown to affect the outcome of patients in different patient populations. In this study we looked at how stroke patients are affected by using ADI (Area Deprivation Index,) and median income. Higher scores of ADI represent poorer conditions. We hypothesize that patients with higher ADI scores and lower median income will show worse stroke outcomes. Methods: Our data includes patients who were admitted to University of Pittsburgh Medical Center between March 2020 and March 2022. Our variables include demographics, medical background and COVID-19 status, socioeconomics. Last known well, hospital arrival and recanalization times are used with outcomes measures of TICI grade, hemorrhagic conversion, discharge NIHSS, discharge and mRS. Results: TICI score and NIHSS on discharge showed significant correlation with median income. mRS 3 months of discharge was correlated with age, COVID19, hypertension, diabetes, hyperlipidemia, smoking, median income, TICI, NIHSS on discharge. In regression analysis, median income showed a significant impact onLKW-to-arrival time and TICI score. However, there can be many omitted variables on its effects on LKW-to-arrival time. Median income, LKW-to-arrival time, and age significantly affect NIHSS on discharge. Median Income, age, and hyperlipidemia have significant impact on death/mortality (mRS=6). Median Income and ADI have significant impact on mRS 3 m on regression without the covariates. After adding the covariates and age is significantly affecting the mRS 3 m. Conclusions: Median income has significant impacts on stroke patients’ time of treatment and functional outcomes. These patients have less resources to get treatment for stroke and stroke risk factors due to several reasons and less chance for rehabilitation following strokes. ADI data did not show a clear relation with outcome variables despite it is a good indicator of socioeconomic status. With less than 10% of total population, COVID-19 patients were not included in final analysis.