Abstract TP258: Social Network Features Involved in Stroke Decision Making During Acute Stroke

Abstract

Introduction: 80% of strokes occur in the company of others and 70% take place at home. However, research on prehospital delay has overlooked the social nature of stroke detection and triage. In the Time is Brain study, we aim to understand how a patient’s social network affects the decision to seek medical care during stroke symptom onset. Methods: We conducted in-depth interview in 78 patients (ages 30 to 90 years) in the longitudinal, multicenter Time is Brain study. During the interview, we mapped and examined relationships and interactions between patients and individuals involved in the decision-making process during the stroke. We enrolled during their initial stroke hospitalization at Mass General Brigham or Yale New Haven Hospital. Eligibility criteria include having an acute stroke syndrome presentation that may include diagnoses of acute ischemic stroke, intracerebral hemorrhage (ICH), or transient ischemic attack (TIA). Trauma-related syndromes, such as subdural hemorrhage, were excluded. Results: 82.5% (n=66) of patients were accompanied by at least one other person during symptom onset. 60% (n=48) contacted at least one other person. 82.5% (n=66) of strokes occurred in a home-setting and 17.5% (n=14) occurred at work or another public space. 36.25% (n=29) of patients identified their spouse as the first person made aware of symptoms. Men were more likely than women to be accompanied by a spouse (X 2 = 6.8211, p-value = 0.03302). Conclusions: Building on prior studies that have highlighted the important role of the social environment in stroke detection and triage, this is a high-resolution study of the social agents and behaviors driving stroke decision-making. These findings highlight the active and often lengthy communication, and unspoken observations that shape layperson decisions during stroke crises. These data are important to identify mechanisms of prehospital delay and build social network-based interventions to improve time-to-treatment.