Abstract TP228: Sex Specific Predictors Of Short Term Stroke Readmission After Hypertensive Urgency: Insights From National Readmission Database

Abstract

Background: Short term outcomes of patients hospitalized with hypertension urgency defined as sudden elevation of Blood pressure above 180 systolic or 110 diastolic is poorly studied in literature. Uncontrolled Hypertension is a major risk factor of stroke. Sex differences exist in stroke presentation and risk factors. Objective: To evaluate the sex specific predictors of short term (90-day) readmission with stroke in patients hospitalized with hypertension urgency. Methods: The study is a retrospective analysis of National Readmission Database (NRD) of years 2016-2018. Adult patients admitted with a primary diagnosis of hypertension urgency were included. Patients were excluded if they had stroke or transient ischemic attack (TIA) in index admission. October to December admissions were excluded to allow for 90 day follow up. Univariate logistic regression was performed on each variable. Variables with a p value >0.2 were included in multivariate logistic regression model (figure1). Results: A total of 104813 patients (58% female) included in our cohort of whom 1057 (1%) were readmitted with ischemic stroke or TIA within 90 days. Mean age of stroke readmissions was higher in female (68±16 vs 61±14 years, p<0.001). Diabetes Mellitus was an independent predictor of stroke readmission in both sexes. Independent predictors specific to females were peripheral vascular disorders (aOR:1.60 95% CI[1.13-2.28], P=0.008) and Hypothyroidism (aOR: 1.38, 95% CI[1.02-1.85], P=0.035). Independent predictor specific to males was age (aOR: 1.016, 95% CI [1.005-1.027], P=0.005). Paradoxically, prior CABG was associated with decreased risk of stroke readmission in females (aOR: 0.39. 95% CI[0.20-078], P=0.007) but not in males (P=0.229). Conclusion: We identified several sex specific predictors of 90-day readmission with stroke in patients admitted with hypertensive crisis. Knowledge of these predictors would help identify patients at risk and improve quality of care.