Articles: covid-19.
-
Multicenter Study
Headpulse measurement can reliably identify large-vessel occlusion stroke in prehospital suspected stroke patients: Results from the EPISODE-PS-COVID study.
Large-vessel occlusion (LVO) stroke represents one-third of acute ischemic stroke (AIS) in the United States but causes two-thirds of poststroke dependence and >90% of poststroke mortality. Prehospital LVO stroke detection permits efficient emergency medical systems (EMS) transport to an endovascular thrombectomy (EVT)-capable center. Our primary objective was to determine the feasibility of using a cranial accelerometry (CA) headset device for prehospital LVO stroke detection. Our secondary objective was development of an algorithm capable of distinguishing LVO stroke from other conditions. ⋯ Obtaining adequate recordings with a CA headset is highly feasible in the prehospital environment. Use of the device algorithm incorporating both CA and LAMS data for LVO detection resulted in significantly higher sensitivity without reduced specificity when compared to the use of LAMS alone.
-
Multicenter Study Observational Study
Differentiation of Prior SARS-CoV-2 Infection and Postacute Sequelae by Standard Clinical Laboratory Measurements in the RECOVER Cohort.
There are currently no validated clinical biomarkers of postacute sequelae of SARS-CoV-2 infection (PASC). ⋯ National Institutes of Health.
-
Reproducible and standardised neurological assessment scales are important in quantifying research outcomes. These scales are often performed by non-neurologists and/or non-clinicians and must be robust, quantifiable, reproducible and comparable to a neurologist's assessment. ⋯ We investigated the strengths and weaknesses of the NIS when used by non-neurology clinicians and non-clinicians, and compared performance to a structured neurological examination performed by a neurology clinician. Through our findings, we provide practical advice on how non-clinicians can be readily trained in conducting reproducible and standardised neurological assessments in a multi-centre study, as well as illustrating potential pitfalls of these tools.
-
Multicenter Study Observational Study
Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.
Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes. ⋯ Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure.
-
Multicenter Study
Morbidity and Mortality of Hospital-Onset SARS-CoV-2 Infections Due to Omicron Versus Prior Variants : A Propensity-Matched Analysis.
Many hospitals have scaled back measures to prevent nosocomial SARS-CoV-2 infection given large decreases in the morbidity and mortality of SARS-CoV-2 infections for most people. Little is known, however, about the morbidity and mortality of nosocomial SARS-CoV-2 infections for hospitalized patients in the Omicron era. ⋯ Harvard Medical School Department of Population Medicine.