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. SelfChristopher J. Lindsell

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere61
JournalJournal of Clinical and Translational Science
Volume6
Issue number1
DOIs
StatePublished - Apr 25 2022

Keywords

  • COVID
  • Clinical Progression Scale
  • critical illness
  • longitudinal assessment
  • proportional odds

ASJC Scopus subject areas

  • Medicine(all)

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