Jeremy Ader1, Jingjing Wu2, Gregg C Fonarow2, Eric E Smith2, Shreyansh Shah2, Ying Xian2, Deepak L Bhatt2, Lee H Schwamm2, Mathew J Reeves2, Roland A Matsouaka2, Kevin N Sheth2. 1. From the Department of Neurology (J.A.), Columbia University Medical Center, New York, NY; Duke Clinical Research Institute (J.W., S.S., Y.X., R.A.M.), Durham, NC; Division of Cardiology (G.C.F.), Ronald Reagan-UCLA Medical Center, Los Angeles, CA; Department of Clinical Neurosciences and Hotchkiss Brain Institute (E.E.S.), University of Calgary, Canada; Department of Neurology (S.S.), Duke University Hospital; Department of Neurology (Y.X.), Duke University Medical Center, Durham, NC; Brigham and Women's Hospital Heart & Vascular Center (D.L.B.) and Department of Neurology, Massachusetts General Hospital (L.H.S.), Harvard Medical School, Boston; Department of Epidemiology (M.J.R.), Michigan State University, East Lansing; Department of Biostatistics and Bioinformatics (R.A.M.), Duke University, Durham, NC; and Department of Neurology (K.N.S.), Division of Neurocritical Care & Emergency Neurology, Yale University, New Haven, CT. jpa9004@nyp.org. 2. From the Department of Neurology (J.A.), Columbia University Medical Center, New York, NY; Duke Clinical Research Institute (J.W., S.S., Y.X., R.A.M.), Durham, NC; Division of Cardiology (G.C.F.), Ronald Reagan-UCLA Medical Center, Los Angeles, CA; Department of Clinical Neurosciences and Hotchkiss Brain Institute (E.E.S.), University of Calgary, Canada; Department of Neurology (S.S.), Duke University Hospital; Department of Neurology (Y.X.), Duke University Medical Center, Durham, NC; Brigham and Women's Hospital Heart & Vascular Center (D.L.B.) and Department of Neurology, Massachusetts General Hospital (L.H.S.), Harvard Medical School, Boston; Department of Epidemiology (M.J.R.), Michigan State University, East Lansing; Department of Biostatistics and Bioinformatics (R.A.M.), Duke University, Durham, NC; and Department of Neurology (K.N.S.), Division of Neurocritical Care & Emergency Neurology, Yale University, New Haven, CT.
Abstract
OBJECTIVE: To determine whether lower socioeconomic status (SES) and longer home to hospital driving time are associated with reductions in tissue plasminogen activator (tPA) administration and timeliness of the treatment. METHODS: We conducted a retrospective observational study using data from the Get With The Guidelines-Stroke Registry (GWTG-Stroke) between January 2015 and March 2017. The study included 118,683 ischemic stroke patients age ≥18 who were transported by emergency medical services to one of 1,489 US hospitals. We defined each patient's SES based on zip code median household income. We calculated the driving time between each patient's home zip code and the hospital where he or she was treated using the Google Maps Directions Application Programing Interface. The primary outcomes were tPA administration and onset-to-arrival time (OTA). Outcomes were analyzed using hierarchical multivariable logistic regression models. RESULTS: SES was not associated with OTA (p = 0.31) or tPA administration (p = 0.47), but was associated with the secondary outcomes of onset-to-treatment time (OTT) (p = 0.0160) and in-hospital mortality (p = 0.0037), with higher SES associated with shorter OTT and lower in-hospital mortality. Driving time was associated with tPA administration (p < 0.001) and OTA (p < 0.0001), with lower odds of tPA (0.83, 0.79-0.88) and longer OTA (1.30, 1.24-1.35) in patients with the longest vs shortest driving time quartiles. Lower SES quintiles were associated with slightly longer driving time quartiles (p = 0.0029), but there was no interaction between the SES and driving time for either OTA (p = 0.1145) or tPA (p = 0.6103). CONCLUSIONS: Longer driving times were associated with lower odds of tPA administration and longer OTA; however, SES did not modify these associations.
OBJECTIVE: To determine whether lower socioeconomic status (SES) and longer home to hospital driving time are associated with reductions in tissue plasminogen activator (tPA) administration and timeliness of the treatment. METHODS: We conducted a retrospective observational study using data from the Get With The Guidelines-Stroke Registry (GWTG-Stroke) between January 2015 and March 2017. The study included 118,683 ischemic stroke patients age ≥18 who were transported by emergency medical services to one of 1,489 US hospitals. We defined each patient's SES based on zip code median household income. We calculated the driving time between each patient's home zip code and the hospital where he or she was treated using the Google Maps Directions Application Programing Interface. The primary outcomes were tPA administration and onset-to-arrival time (OTA). Outcomes were analyzed using hierarchical multivariable logistic regression models. RESULTS: SES was not associated with OTA (p = 0.31) or tPA administration (p = 0.47), but was associated with the secondary outcomes of onset-to-treatment time (OTT) (p = 0.0160) and in-hospital mortality (p = 0.0037), with higher SES associated with shorter OTT and lower in-hospital mortality. Driving time was associated with tPA administration (p < 0.001) and OTA (p < 0.0001), with lower odds of tPA (0.83, 0.79-0.88) and longer OTA (1.30, 1.24-1.35) in patients with the longest vs shortest driving time quartiles. Lower SES quintiles were associated with slightly longer driving time quartiles (p = 0.0029), but there was no interaction between the SES and driving time for either OTA (p = 0.1145) or tPA (p = 0.6103). CONCLUSIONS: Longer driving times were associated with lower odds of tPA administration and longer OTA; however, SES did not modify these associations.
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