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Predicting COVID-19 prognosis in hospitalized patients based on early status.
Natanov, David; Avihai, Byron; McDonnell, Erin; Lee, Eileen; Cook, Brennan; Altomare, Nicole; Ko, Tomohiro; Chaia, Angelo; Munoz, Carolayn; Ouellette, Samantha; Nyalakonda, Suraj; Cederbaum, Vanessa; Parikh, Payal D; Blaser, Martin J.
Afiliação
  • Natanov D; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Avihai B; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • McDonnell E; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Lee E; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Cook B; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Altomare N; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Ko T; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Chaia A; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Munoz C; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Ouellette S; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Nyalakonda S; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Cederbaum V; Rutgers Robert Wood Johnson Medical School , Piscataway, New Jersey, USA.
  • Parikh PD; Department of Medicine, Robert Wood Johnson Medical School , New Brunswick, New Jersey, USA.
  • Blaser MJ; Center for Advanced Biotechnology and Medicine, Rutgers University , New Brunswick, New Jersey, USA.
mBio ; 14(5): e0150823, 2023 Oct 31.
Article em En | MEDLINE | ID: mdl-37681966
ABSTRACT
IMPORTANCE COVID-19 remains the fourth leading cause of death in the United States. Predicting COVID-19 patient prognosis is essential to help efficiently allocate resources, including ventilators and intensive care unit beds, particularly when hospital systems are strained. Our PLABAC and PRABLE models are unique because they accurately assess a COVID-19 patient's risk of death from only age and five commonly ordered laboratory tests. This simple design is important because it allows these models to be used by clinicians to rapidly assess a patient's risk of decompensation and serve as a real-time aid when discussing difficult, life-altering decisions for patients. Our models have also shown generalizability to external populations across the United States. In short, these models are practical, efficient tools to assess and communicate COVID-19 prognosis.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article