Risk factors for progression of coronavirus disease 2019 (COVID-19) to severe disease or death are underexplored in U.S. cohorts.
To determine the factors on hospital admission that are predictive of ...severe disease or death from COVID-19.
Retrospective cohort analysis.
Five hospitals in the Maryland and Washington, DC, area.
832 consecutive COVID-19 admissions from 4 March to 24 April 2020, with follow-up through 27 June 2020.
Patient trajectories and outcomes, categorized by using the World Health Organization COVID-19 disease severity scale. Primary outcomes were death and a composite of severe disease or death.
Median patient age was 64 years (range, 1 to 108 years); 47% were women, 40% were Black, 16% were Latinx, and 21% were nursing home residents. Among all patients, 131 (16%) died and 694 (83%) were discharged (523 63% had mild to moderate disease and 171 20% had severe disease). Of deaths, 66 (50%) were nursing home residents. Of 787 patients admitted with mild to moderate disease, 302 (38%) progressed to severe disease or death: 181 (60%) by day 2 and 238 (79%) by day 4. Patients had markedly different probabilities of disease progression on the basis of age, nursing home residence, comorbid conditions, obesity, respiratory symptoms, respiratory rate, fever, absolute lymphocyte count, hypoalbuminemia, troponin level, and C-reactive protein level and the interactions among these factors. Using only factors present on admission, a model to predict in-hospital disease progression had an area under the curve of 0.85, 0.79, and 0.79 at days 2, 4, and 7, respectively.
The study was done in a single health care system.
A combination of demographic and clinical variables is strongly associated with severe COVID-19 disease or death and their early onset. The COVID-19 Inpatient Risk Calculator (CIRC), using factors present on admission, can inform clinical and resource allocation decisions.
Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.
Magnetic reconnection is a ubiquitous astrophysical process that rapidly converts magnetic energy into some combination of plasma flow energy, thermal energy, and non-thermal energetic particles, ...including energetic electrons. Various reconnection acceleration mechanisms in different low-\(\beta\) (plasma-to-magnetic pressure ratio) and collisionless environments have been proposed theoretically and studied numerically, including first- and second-order Fermi acceleration, betatron acceleration, parallel electric field acceleration along magnetic fields, and direct acceleration by the reconnection electric field. However, none of them have been heretofore confirmed experimentally, as the direct observation of non-thermal particle acceleration in laboratory experiments has been difficult due to short Debye lengths for \textit{in-situ} measurements and short mean free paths for \textit{ex-situ} measurements. Here we report the direct measurement of accelerated non-thermal electrons from low-\(\beta\) magnetically driven reconnection in experiments using a laser-powered capacitor coil platform. We use kiloJoule lasers to drive parallel currents to reconnect MegaGauss-level magnetic fields in a quasi-axisymmetric geometry. The angular dependence of the measured electron energy spectrum and the resulting accelerated energies, supported by particle-in-cell simulations, indicate that the mechanism of direct electric field acceleration by the out-of-plane reconnection electric field is at work. Scaled energies using this mechanism show direct relevance to astrophysical observations. Our results therefore validate one of the proposed acceleration mechanisms by reconnection, and establish a new approach to study reconnection particle acceleration with laboratory experiments in relevant regimes.
Human semantic memory is modeled as a network with a finite automation embedded at each node. The nodes represent concepts in the memory, and every arc bears a label denoting the binary relation ...between the two concepts that it joins. The process of question-answering is formulated as a mathematical problem: Given a finite sequence of labels, find a path in memory between two given nodes whose arcs bear that sequence of labels. It is shown that the network of automata can determine the existence of such a path using only local computation, meaning that each automaton communicates only with its immediate neighbors in the network. Furthermore, any node-concept along the solution path can be retrieved. The question-answering algorithm is then extended to incorporate simple inferences based on the equivalence of certain sequences of relational labels. In this case, it is shown that the network of automata will find the shortest inferable solution path, if one exists. Application of these results to a semantic corpus is illustrated.
The two semantic topics of negation and quantification receive special treatment. Careful study is made of the network structure required to encode information relating to those topics and of the question-answering procedures required to extract this information. The notions of a negated relation and a negated question are introduced, and a negation-sensitive path-searching algorithm is developed that provides for strong denials of queries. For sentences involving universal and existential quantifiers, it is shown how the terminal can translate a first-order language question into a sequence of network queries. In both areas, the network model makes reaction-time predictions that are supported by several experimental findings.
Extensions of the model that would permit the encoding and retrieval of propositional information are mentioned.
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