Ischemic stroke in COVID-19 patients: Mechanisms, treatment, and outcomes in a consecutive Swiss Stroke Registry analysis.

BACKGROUND: Most case series of patients with ischemic stroke (IS) and COVID-19 are limited to selected centers or lack 3-month outcomes. The aim of this study was to describe the frequency, clinical and radiological features, and 3-month outcomes of patients with IS and COVID-19 in a nationwide stroke registry. METHODS: From the Swiss Stroke Registry (SSR), we included all consecutive IS patients ≥18 years admitted to Swiss Stroke Centers or Stroke Units during the first wave of COVID-19 (25 February to 8 June 2020). We compared baseline features, etiology, and 3-month outcome of SARS-CoV-2 polymerase chain reaction-positive (PCR+) IS patients to SARS-CoV-2 PCR- and/or asymptomatic non-tested IS patients. RESULTS: Of the 2341 IS patients registered in the SSR during the study period, 36 (1.5%) had confirmed COVID-19 infection, of which 33 were within 1 month before or after stroke onset. In multivariate analysis, COVID+ patients had more lesions in multiple vascular territories (OR 2.35, 95% CI 1.08-5.14, p = 0.032) and fewer cryptogenic strokes (OR 0.37, 95% CI 0.14-0.99, p = 0.049). COVID-19 was judged the likely principal cause of stroke in 8 patients (24%), a contributing/triggering factor in 12 (36%), and likely not contributing to stroke in 13 patients (40%). There was a strong trend towards worse functional outcome in COVID+ patients after propensity score (PS) adjustment for age, stroke severity, and revascularization treatments (PS-adjusted common OR for shift towards higher modified Rankin Scale (mRS) = 1.85, 95% CI 0.96-3.58, p = 0.07). CONCLUSIONS: In this nationwide analysis of consecutive ischemic strokes, concomitant COVID-19 was relatively rare. COVID+ patients more often had multi-territory stroke and less often cryptogenic stroke, and their 3-month functional outcome tended to be worse.

AI support for ethical decision-making around resuscitation: proceed with care.

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient's Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions-conceptual, methodological and procedural-that need to be considered in further development and implementation efforts.