Abstract TP297: Predictors of Acute Ischemic Stroke in Patients Presenting to the Emergency Department With Dizziness, Imbalance, and Vertigo

Objectives: To identify predictors of acute ischemic stroke (AIS) among patients presenting to the Emergency Department (ED) with dizziness, imbalance, or vertigo (DIV) based on demographic and clinical characteristics. Methods: We identified patients admitted to the hospital after presenting to the ED with DIV from the Statewide Planning and Research Cooperative System database of New York from 2010 through 2014. ICD-9-CM codes were used to identify patient's clinical characteristics. Demographic and clinical characteristics were systematically collected. Multivariable logistic regression was used to determine predictors of a discharge diagnosis of AIS. Results: Among 77,993 patients with DIV, 3,857 (4.9%) had a discharge diagnosis of AIS. Admission presentation of imbalance (Odd ratio [OR] 1.64 95% confidence interval [CI] 1.51-1.77 p<0.001), African-American race (OR 1.15, 95% CI 1.05-1.25, p=0.002), history of hypertension (OR 1.60, 95% CI 1.49-1.72, p<0.001), diabetes mellitus (OR 1.39, 95% CI 1.29-1.49, p<0.001), hypercholesterolemia (OR 1.52, 95% CI 1.42-1.62, p<0.001), tobacco use (OR 1.70, 95% CI 1.53-1.88, p<0.001), atrial fibrillation (OR 1.41, 95% CI 1.28-1.56, p<0.001), and prior AIS due to extracranial artery atherosclerosis (OR 4.47, 95% CI 4.00-5.01, p<0.001) were each positively associated with an AIS diagnosis independently. Factors negatively associated with an AIS discharge diagnosis included: admission presentation of vertigo (OR 0.56, 95% CI 0.41-0.75, p<0.001), female sex (OR 0.64, 95% CI 0.60-0.69, p<0.001), age>81 (OR 0.86, 95% CI 0.77-0.96, p=0.01), history of anemia (OR 0.65, 95% CI 0.58-0.73, p<0.001), coronary artery disease (OR 0.84, 95% CI 0.78-0.91, p<0.001), asthma (OR 0.68, 95% CI 0.59-0.76, p<0.001), depressive disorders (OR 0.74, 95% CI 0.64-0.84, p<0.001), and anxiety disorders (OR 0.70, 95% CI 0.59-0.82, p<0.001). Conclusions: Multiple potential positive and negative predictive AIS risk factors were identified. Combining with currently available centrally-caused dizziness prediction tools, these newly identified factors could provide more accurate AIS risk stratifying method for DIV patients.