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Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes

2022; Elsevier BV; Volume: 87; Linguagem: Inglês

10.1016/j.ebiom.2022.104413

2352-3964

Justin Reese, Hannah Blau, Elena Casiraghi, Timothy Bergquist, Johanna Loomba, Tiffany J Callahan, Bryan Laraway, Corneliu Antonescu, Ben Coleman, Michael Gargano, Kenneth J. Wilkins, Luca Cappelletti, Tommaso Fontana, Nariman Ammar, Blessy Antony, T. M. Murali, J. Harry Caufield, Guy Karlebach, Julie A. McMurry, Andrew E. Williams, Richard A. Moffitt, Jineta Banerjee, Anthony Solomonides, Hannah Davis, Kristin Kostka, Giorgio Valentini, David Sahner, Christopher G. Chute, Charisse Madlock‐Brown, Melissa Haendel, Peter N. Robinson, Heidi Spratt, Shyam Visweswaran, Joseph Eugene Flack, Yun Jae Yoo, Davera Gabriel, G. Caleb Alexander, Hemalkumar B. Mehta, Feifan Liu, Robert Miller, Rachel Wong, Elaine Hill, Lorna E. Thorpe, Jasmin Divers,

Inflammasome and immune disorders

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.