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Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values

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Cavallaro, Massimo, Moiz, Haseeb, Keeling, Matt J. and McCarthy, Noel D. (2021) Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values. Working Paper. Cold Spring Harbor: MedRXiv.

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Official URL: https://doi.org/10.1101/2020.12.03.20242941

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Abstract

Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantively to evaluate the association of demographic and health records with two related, yet different, outcomes of severe COVID-19 (viz., death and ICUA); methodologically to compare interpretations based on logistic regression and on gradient-boosted decision tree (GBDT) predictions interpreted by means of the Shapley impacts of covariates. Very different association of some factors, e.g., obesity and chronic respiratory diseases, with death and ICUA may guide review of practice. Shapley explanation of GBDTs identified varying effects of some factors among patients, thus emphasising the importance of individual patient assessment. The results of this study are also relevant for the evaluation of complex automated clinical decision systems, which should optimise prediction scores whilst remaining interpretable to clinicians and mitigating potential biases.

Item Type: Working or Discussion Paper (Working Paper)
Subjects: R Medicine > RA Public aspects of medicine
Divisions: Faculty of Science > Life Sciences (2010- )
Faculty of Science > Mathematics
Faculty of Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH): COVID-19 (Disease) , COVID-19 (Disease) -- Patients -- Treatment, Intensive care units -- Admission and discharge -- Mathematical models
Publisher: MedRXiv
Place of Publication: Cold Spring Harbor
Official Date: 29 April 2021
Dates:
DateEvent
29 April 2021Submitted
Institution: University of Warwick
Status: Not Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Description:

Now published in PLOS Computational Biology doi: 10.1371/journal.pcbi.1009121

RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIED[MRC] Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
UNSPECIFIED[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
UNSPECIFIED[ESRC] Economic and Social Research Councilhttp://dx.doi.org/10.13039/501100000269
UNSPECIFIEDDepartment of Health and Social CareUNSPECIFIED
UNSPECIFIEDChief Scientist Office, Scottish Government Health and Social Care Directoratehttp://dx.doi.org/10.13039/100014589
UNSPECIFIEDHealth and Social Care Research and Development Divisionhttp://dx.doi.org/10.13039/501100010756
UNSPECIFIEDPublic Health Agencyhttp://dx.doi.org/10.13039/501100001626
UNSPECIFIEDBritish Heart Foundationhttp://dx.doi.org/10.13039/501100000274
UNSPECIFIEDWellcome Trusthttp://dx.doi.org/10.13039/100010269
MR/V038613/1UK Research and Innovationhttp://dx.doi.org/10.13039/100014013
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