Oral anticoagulant re-initiation following intracerebral hemorrhage in non-valvular atrial fibrillation: Global survey of the practices of neurologists, neurosurgeons and thrombosis experts

<div><p>Background</p><p>While oral anticoagulants (OACs) are highly effective for ischemic stroke prevention in atrial fibrillation, intracerebral hemorrhage (ICH) remains the most feared complication of OAC. Clinical controversy remains regarding OAC resumption and its timing for ICH survivors with atrial fibrillation because the balance between risks and benefits has not been investigated in randomized trials.</p><p>Aims/Hypothesis</p><p>To survey the practice of stroke neurologists, thrombosis experts and neurosurgeons on OAC re-initiation following OAC-associated ICH.</p><p>Methods</p><p>An online survey was distributed to members of the International Society for Thrombosis and Haemostasis, Canadian Stroke Consortium, NAVIGATE-ESUS trial investigators (Clinicatrials.gov identifier NCT02313909) and American Association of Neurological Surgeons. Demographic factors and 11 clinical scenarios were included.</p><p>Results</p><p>Two hundred twenty-eight participants from 38 countries completed the survey. Majority of participants were affiliated with academic centers, and >20% managed more than 15 OAC-associated ICH patients/year. Proportion of respondents suggesting OAC anticoagulant resumption varied from 30% (for cerebral amyloid angiopathy) to 98% (for traumatic ICH). Within this group, there was wide distribution in response for timing of resumption: 21.4% preferred to re-start OACs after 1–3 weeks of incident ICH, while 25.3% opted to start after 1–3 months. Neurosurgery respondents preferred earlier OAC resumption compared to stroke neurologists or thrombosis experts in 5 scenarios (p<0.05 by Kendall’s tau).</p><p>Conclusions</p><p>Wide variations in current practice exist among management of OAC-associated ICH, with decisions influenced by patient- and provider-related factors. As these variations likely reflect the lack of high quality evidence, randomized trials are direly needed in this population.</p></div

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant