Journal of Clinical and Translational Science (Jan 2022)

Modeling clinical trajectory status of critically ill COVID-19 patients over time: A method for analyzing discrete longitudinal and ordinal outcomes

  • Michael J. Ward,
  • David J. Douin,
  • Wu Gong,
  • Adit A. Ginde,
  • Catherine L. Hough,
  • Matthew C. Exline,
  • Mark W. Tenforde,
  • William B. Stubblefield,
  • Jay S. Steingrub,
  • Matthew E. Prekker,
  • Akram Khan,
  • D. Clark Files,
  • Kevin W. Gibbs,
  • Todd W. Rice,
  • Jonathan D. Casey,
  • Daniel J. Henning,
  • Jennifer G. Wilson,
  • Samuel M. Brown,
  • Manish M. Patel,
  • Wesley H. Self,
  • Christopher J. Lindsell,
  • for the Influenza and Other Viruses in the Acutely Ill (IVY) Network

DOI
https://doi.org/10.1017/cts.2022.393
Journal volume & issue
Vol. 6

Abstract

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Early in the COVID-19 pandemic, the World Health Organization stressed the importance of daily clinical assessments of infected patients, yet current approaches frequently consider cross-sectional timepoints, cumulative summary measures, or time-to-event analyses. Statistical methods are available that make use of the rich information content of longitudinal assessments. We demonstrate the use of a multistate transition model to assess the dynamic nature of COVID-19-associated critical illness using daily evaluations of COVID-19 patients from 9 academic hospitals. We describe the accessibility and utility of methods that consider the clinical trajectory of critically ill COVID-19 patients.

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