BACKGROUND: The estimates of patients who present with transient ischemic attacks (TIA) in the emergency departments (EDs) of United States and their disposition and factors that determine hospital admission are not well understood. OBJECTIVE: We used a nationally representative database to determine the rate and predictors of admission in TIA patients presenting to EDs. METHODS: We analyzed data from the National Emergency Department Sample (2006-2008) for all patients presenting with a primary diagnosis of TIA in the United States. Samples were weighted to provide national estimates of TIA hospitalizations and identify factors that increase the odds of hospital admission including age, sex, type of insurance, median household income, and hospital type (urban teaching, urban nonteaching, and nonurban). Multivariate logistic regression analysis was used to identify independent predictors of hospital admission. RESULTS: There were 812908 ED visits for primary diagnosis of TIA; mean age (±SD), 70.3 ± 14.9 years; and 57.9% were women from 2006 to 2008. Of these ED visits, 516837 (63.5%) were admitted to the hospital, whereas 296071 (36.5%) were discharged from the ED to home. In the multivariate logistic regression analysis adjusting age, sex, and medical comorbidities, independent factors associated with hospital admissions were median household income $64000 or higher (odds ratio [OR], 1.33; 95% confidence interval [CI], 1.22-1.44; P = .003), Medicare insurance type (OR, 1.19; 95% CI, 1.14-1.26; P < .0001), and metropolitan teaching hospital ED (OR, 2.17; 95% CI, 1.90-2.48; P < .0001). CONCLUSION: From 2006 to 2008, approximately 64% of all patients presenting with TIAs to the EDs within United States were admitted to the hospital. Factors unrelated to patients' condition such as median household income, insurance status, and ED affiliated hospital type play an important role in the decision to admit TIA patients to the hospitals.
BACKGROUND: The estimates of patients who present with transient ischemic attacks (TIA) in the emergency departments (EDs) of United States and their disposition and factors that determine hospital admission are not well understood. OBJECTIVE: We used a nationally representative database to determine the rate and predictors of admission in TIApatients presenting to EDs. METHODS: We analyzed data from the National Emergency Department Sample (2006-2008) for all patients presenting with a primary diagnosis of TIA in the United States. Samples were weighted to provide national estimates of TIA hospitalizations and identify factors that increase the odds of hospital admission including age, sex, type of insurance, median household income, and hospital type (urban teaching, urban nonteaching, and nonurban). Multivariate logistic regression analysis was used to identify independent predictors of hospital admission. RESULTS: There were 812908 ED visits for primary diagnosis of TIA; mean age (±SD), 70.3 ± 14.9 years; and 57.9% were women from 2006 to 2008. Of these ED visits, 516837 (63.5%) were admitted to the hospital, whereas 296071 (36.5%) were discharged from the ED to home. In the multivariate logistic regression analysis adjusting age, sex, and medical comorbidities, independent factors associated with hospital admissions were median household income $64000 or higher (odds ratio [OR], 1.33; 95% confidence interval [CI], 1.22-1.44; P = .003), Medicare insurance type (OR, 1.19; 95% CI, 1.14-1.26; P < .0001), and metropolitan teaching hospital ED (OR, 2.17; 95% CI, 1.90-2.48; P < .0001). CONCLUSION: From 2006 to 2008, approximately 64% of all patients presenting with TIAs to the EDs within United States were admitted to the hospital. Factors unrelated to patients' condition such as median household income, insurance status, and ED affiliated hospital type play an important role in the decision to admit TIApatients to the hospitals.
Authors: Dawn M Bravata; Laura J Myers; Greg Arling; Edward J Miech; Teresa Damush; Jason J Sico; Michael S Phipps; Alan J Zillich; Zhangsheng Yu; Mathew Reeves; Linda S Williams; Jason Johanning; Seemant Chaturvedi; Fitsum Baye; Susan Ofner; Curt Austin; Jared Ferguson; Glenn D Graham; Rachel Rhude; Chad S Kessler; Donald S Higgins; Eric Cheng Journal: JAMA Neurol Date: 2018-04-01 Impact factor: 18.302
Authors: Bernard P Chang; Sara Rostanski; Joshua Willey; Eliza C Miller; Steven Shapiro; Rachel Mehendale; Benjamin Kummer; Babak B Navi; Mitchell S V Elkind Journal: Ann Emerg Med Date: 2019-07-17 Impact factor: 5.721
Authors: Ava L Liberman; Hui Zhang; Sara K Rostanski; Natalie T Cheng; Charles C Esenwa; Neil Haranhalli; Puneet Singh; Daniel L Labovitz; Richard B Lipton; Shyam Prabhakaran Journal: J Am Heart Assoc Date: 2021-05-31 Impact factor: 6.106
Authors: Aleksandra Yakhkind; Ryan A McTaggart; Mahesh V Jayaraman; Matthew S Siket; Brian Silver; Shadi Yaghi Journal: Front Neurol Date: 2016-06-10 Impact factor: 4.003
Authors: Lucas Ramirez; May A Kim-Tenser; Nerses Sanossian; Steven Cen; Ge Wen; Shuhan He; William J Mack; Amytis Towfighi Journal: J Am Heart Assoc Date: 2016-09-24 Impact factor: 5.501