> Detailanzeige

Rangelov, Bojidar; Young, Alexandra; Lilaonitkul, Watjana; Aslani, Shahab; Taylor, Paul; Guðmundsson, Eyjólfur; Yang, Qianye; Hu, Yipeng; Hurst, John R.; Hawkes, David J.; Jacob, Joseph; Bains, Pardeep; Cushnan, Dominic; Halling-Brown, Mark; Jacob, Joseph; Jefferson, Emily; Lemarchand, Francois; Sarellas, Anastasios; Schofield, Daniel; Sutherland, James; Watt, Mathew; Alexander, Daniel; Aziz, Hena; Hurst, John R.; [...]

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

Literatur-
verwaltung

Zur
Merkliste

Lösche von
Merkliste

Per Whatsapp teilen

Schließen

Merkliste



Sie können Bookmarks mittels Listen verwalten, loggen Sie sich dafür bitte in Ihr SLUB Benutzerkonto ein.
  • Medientyp: E-Artikel
  • Titel: Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes
  • Beteiligte: Rangelov, Bojidar; Young, Alexandra; Lilaonitkul, Watjana; Aslani, Shahab; Taylor, Paul; Guðmundsson, Eyjólfur; Yang, Qianye; Hu, Yipeng; Hurst, John R.; Hawkes, David J.; Jacob, Joseph; Bains, Pardeep; Cushnan, Dominic; Halling-Brown, Mark; Jacob, Joseph; Jefferson, Emily; Lemarchand, Francois; Sarellas, Anastasios; Schofield, Daniel; Sutherland, James; Watt, Mathew; Alexander, Daniel; Aziz, Hena; Hurst, John R.; [...]
  • Erschienen: Springer Science and Business Media LLC, 2023
  • Erschienen in: Scientific Reports
  • Sprache: Englisch
  • DOI: 10.1038/s41598-023-32469-9
  • ISSN: 2045-2322
  • Schlagwörter: Multidisciplinary
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:title>Abstract</jats:title><jats:p>The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model—SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (<jats:italic>General Haemodynamic</jats:italic>, <jats:italic>Renal</jats:italic> and <jats:italic>Immunological</jats:italic>) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease.</jats:p>
  • Zugangsstatus: Freier Zugang