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1.
Bioelectronic Medicine ; 6(1):14-14, 2020.
Artículo | WHO COVID | ID: covidwho-637250

RESUMEN

The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for “Emergency ML ” Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities With rapidly growing datasets, there also remain important considerations when developing and validating ML models This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume

2.
Thromb Haemost ; 120(7): 1004-1024, 2020 Jul.
Artículo en Inglés | MEDLINE | ID: covidwho-418767

RESUMEN

Coronavirus disease 2019 (COVID-19), currently a worldwide pandemic, is a viral illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The suspected contribution of thrombotic events to morbidity and mortality in COVID-19 patients has prompted a search for novel potential options for preventing COVID-19-associated thrombotic disease. In this article by the Global COVID-19 Thrombosis Collaborative Group, we describe novel dosing approaches for commonly used antithrombotic agents (especially heparin-based regimens) and the potential use of less widely used antithrombotic drugs in the absence of confirmed thrombosis. Although these therapies may have direct antithrombotic effects, other mechanisms of action, including anti-inflammatory or antiviral effects, have been postulated. Based on survey results from this group of authors, we suggest research priorities for specific agents and subgroups of patients with COVID-19. Further, we review other agents, including immunomodulators, that may have antithrombotic properties. It is our hope that the present document will encourage and stimulate future prospective studies and randomized trials to study the safety, efficacy, and optimal use of these agents for prevention or management of thrombosis in COVID-19.


Asunto(s)
Infecciones por Coronavirus/inmunología , Fibrinolíticos/uso terapéutico , Inflamación/tratamiento farmacológico , Neumonía Viral/inmunología , Trombosis/tratamiento farmacológico , Animales , Antiinflamatorios/uso terapéutico , Anticoagulantes/uso terapéutico , Antivirales/uso terapéutico , Betacoronavirus , Infecciones por Coronavirus/complicaciones , Infecciones por Coronavirus/tratamiento farmacológico , Glicosaminoglicanos/uso terapéutico , Hemostasis , Humanos , Inflamación/complicaciones , Inflamación/inmunología , Pandemias , Inhibidores de Agregación Plaquetaria/uso terapéutico , Neumonía Viral/complicaciones , Neumonía Viral/tratamiento farmacológico , Trombosis/complicaciones , Trombosis/inmunología
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