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

doi:10.1017/cts.2022.393

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 journal › Article › peer-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 language	English (US)
Article number	e61
Journal	Journal of Clinical and Translational Science
Volume	6
Issue number	1
DOIs	https://doi.org/10.1017/cts.2022.393
State	Published - Apr 25 2022

Keywords

COVID
Clinical Progression Scale
critical illness
longitudinal assessment
proportional odds

ASJC Scopus subject areas

General Medicine

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10.1017/cts.2022.393

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Ward, M. J., Douin, D. J., Gong, W., Ginde, A. A., Hough, C. L., Exline, M. C., Tenforde, M. W., Stubblefield, W. B., Steingrub, J. S., Prekker, M. E., Khan, A., Files, D. C., Gibbs, K. W., Rice, T. W., Casey, J. D., Henning, D. J., Wilson, J. G., Brown, S. M., Patel, M. M., ... Lindsell, C. J. (2022). Modeling clinical trajectory status of critically ill COVID-19 patients over time: A method for analyzing discrete longitudinal and ordinal outcomes. Journal of Clinical and Translational Science, 6(1), Article e61. https://doi.org/10.1017/cts.2022.393

Ward, MJ, Douin, DJ, Gong, W, Ginde, AA, Hough, CL, Exline, MC, Tenforde, MW, Stubblefield, WB, Steingrub, JS, Prekker, ME, Khan, A, Files, DC, Gibbs, KW, Rice, TW, Casey, JD, Henning, DJ, Wilson, JG, Brown, SM, Patel, MM, Self, WH & Lindsell, CJ 2022, 'Modeling clinical trajectory status of critically ill COVID-19 patients over time: A method for analyzing discrete longitudinal and ordinal outcomes', Journal of Clinical and Translational Science, vol. 6, no. 1, e61. https://doi.org/10.1017/cts.2022.393

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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.",

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doi = "10.1017/cts.2022.393",

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AU - Exline, Matthew C.

AU - Tenforde, Mark W.

AU - Stubblefield, William B.

AU - Steingrub, Jay S.

AU - Prekker, Matthew E.

AU - Khan, Akram

AU - Files, D. Clark

AU - Gibbs, Kevin W.

AU - Rice, Todd W.

AU - Casey, Jonathan D.

AU - Henning, Daniel J.

AU - Wilson, Jennifer G.

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AU - Self, Wesley H.

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

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Keywords

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