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Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models

  • Matteo Di Maso
  • Monica Ferraroni
  • Pasquale Ferrante
  • Serena Delbue
  • Federico Ambrogi

In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to correctly estimate these associations combining a survival and longitudinal sub-models by means of a shared parameter (i.e., random effects of the longitudinal sub-model are inserted in the survival one). This study aims at showing the use of JM to evaluate the association between a set of inflammatory biomarkers and Covid-19 mortality. During Covid-19 pandemic, physicians at Istituto Clinico di Città Studi in Milan collected biomarkers (endogenous time-varying covariates) to understand what might be used as prognostic factors for mortality. Furthermore, in the first epidemic outbreak, physicians did not have standard clinical protocols for management of Covid-19 disease and measurements of biomarkers were highly incomplete especially at the baseline. Between February and March 2020, a total of 403 COVID-19 patients were admitted. Baseline characteristics included sex and age, whereas biomarkers measurements, during hospital stay, included log-ferritin, log-lymphocytes, log-neutrophil granulocytes, log-C-reactive protein, glucose and LDH. A Bayesian approach using Markov chain Monte Carlo algorithm were used for fitting JM. Independent and non-informative priors for the fixed effects (age and sex) and for shared parameters were used. Hazard ratios (HR) from a (biased) time-dependent Cox and joint models for log-ferritin levels were 2.10 (1.67-2.64) and 1.73 (1.38-2.20), respectively. In multivariable JM, doubling of biomarker levels resulted in a significantly increase of mortality risk for log-neutrophil granulocytes, HR=1.78 (1.16-2.69); for log-C-reactive protein, HR=1.44 (1.13-1.83); and for LDH, HR=1.28 (1.09-1.49). Increasing of 100 mg/dl of glucose resulted in a HR=2.44 (1.28-4.26). Age, however, showed the strongest effect with mortality risk starting to rise from 60 years.

  • Keywords:
  • Endogenous time-varying covariates,
  • Time-dependent Cox model,
  • Joint models,
  • Inflammatory biomarkers,
  • Covid-19 mortality,
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Matteo Di Maso

University of Milan, Italy - ORCID: 0000-0002-6481-990X

Monica Ferraroni

University of Milan, Italy - ORCID: 0000-0002-4542-4996

Pasquale Ferrante

University of Milan, Italy

Serena Delbue

University of Milan, Italy - ORCID: 0000-0002-3199-9369

Federico Ambrogi

University of Milan, Italy - ORCID: 0000-0001-9358-011X

  1. Rizopoulos D. (2012). Joint Models for Longitudinal and Time-to-Event Data. With Application in R. Boca Raton: Chapman & Hall/CRC.
  2. Therneau T., Grambsch P. (2000). Modeling Survival Data: Extending the Cox Model. Springer-Verlag, New York (NY).
  3. van Houwelingen HC., Putter H. (2012). Dynamic Prediction in Clinical Survival Analysis. Boca Raton: Chapman & Hall/CRC.
  4. Rizopoulos D. (2016). The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data using MCMC. J Stat Softw. 72(7), pp. 1-45.
  5. Putter H. (2015). dynpred: Companion Package to "Dynamic Prediction in Clinical Survival Analysis". R package version 0.1.2. <https://CRAN.Rproject.org/package=dynpred>.
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  • Publication Year: 2021
  • Pages: 191-196
  • Content License: CC BY 4.0
  • © 2021 Author(s)

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  • Publication Year: 2021
  • Content License: CC BY 4.0
  • © 2021 Author(s)

Chapter Information

Chapter Title

Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models

Authors

Matteo Di Maso, Monica Ferraroni, Pasquale Ferrante, Serena Delbue, Federico Ambrogi

DOI

10.36253/978-88-5518-461-8.36

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Table of Contents

Book Title

ASA 2021 Statistics and Information Systems for Policy Evaluation

Book Subtitle

BOOK OF SHORT PAPERS of the on-site conference

Editors

Alessandra Petrucci, Bruno Bertaccini, Luigi Fabbris

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/978-88-5518-461-8

eISBN (pdf)

978-88-5518-461-8

eISBN (xml)

978-88-5518-462-5

Series Title

Proceedings e report

Series Issn ISSN

2704-601X

Series E-Issn

2704-5846

27

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