International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

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3 Scopus citations


Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Original languageEnglish
Article number74
Journalnpj Digital Medicine
Issue number1
StatePublished - Dec 2022

Bibliographical note

Funding Information:
G.W. reports funding from NCATS UL1TR002541, NCATS UL1TR000005, and NLM R01LM013345. S.M. and J.K. report funding from NCATS 5UL1TR001857-05 and NHGRI 5R01HG009174-04. Z.X. reports funding from NINDS R01NS098023. G.O. reports funding from NIH grants NIEHS P30ES017885 and NCI U24CA210967. S.V. reports funding from NLM R01LM012095 and NCATS UL1TR001857. A.S. reports funding from NHLBI K23HL148394 and L40HL148910, and NCATS UL1TR001420. B.A. reports funding from NHLBI U24 HL148865. D.B. and R.F. report funding from NCATS UL1TR001881. T.G. and T.G. report funding from 01ZZ1801E German Federal Ministry of Education and Research. D.H. reports funding from NCATS UL1TR002240. M.K. reports funding from NHGRI 5T32HG002295-18. D.K. reports funding from MIRACUM Consortium grant 01ZZ1801A. Y.L. reports funding from NLM R01LM01333. J.M. reports funding from NCATS UL1TR001878. D.M. reports funding from NCATS UL1-TR001878 Institutional Clinical and Translational Science Award (University of Pennsylvania). L.P. reports funding from NCATS CTSA Award #UL1TR002366.

Publisher Copyright:
© 2022, The Author(s).

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Informatics
  • Computer Science Applications
  • Health Information Management


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