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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. PLoS Computational Biology, 17 (6). e1009121. doi:10.1371/journal.pcbi.1009121
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WRAP-Contrasting-factors-associated-COVID-19-ICU-death-patients-values-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3270Kb) | Preview |
Official URL: https://doi.org/10.1371/journal.pcbi.1009121
Abstract
The design is a retrospective cohort study of 13954 in-patients of ages ranging from 1 to 105 year (IQR: 56, 70, 81) with a confirmed diagnosis of COVID-19 by 28th June 2020. This study used multivariable logistic regression to generate odd ratios (ORs) multiply adjusted for 37 covariates (comorbidities, demographic, and others) selected on the basis of clinical interest and prior findings. Results were supplemented by gradient-boosted decision tree (GBDT) classification to generate Shapley values in order to evaluate the impact of the covariates on model output for all patients. Factors are differentially associated with death and ICUA and among patients. Deaths due to COVID-19 were associated with immunosuppression due to disease (OR 1.39, 95% CI 1.10–1.76), type-2 diabetes (OR 1.31, 95% CI 1.17–1.46), chronic respiratory disease (OR 1.19, 95% CI 1.05–1.35), age (OR 1.56/10-year increment, 95% CI 1.51–1.61), and male sex (OR 1.54, 95% CI 1.42–1.68). Associations of ICUA with some factors differed in direction (e.g., age, chronic respiratory disease). Self-reported ethnicities were strongly but variably associated with both outcomes. GBDTs had similar performance (ROC-AUC, ICUA 0.83, death 0.68 for GBDT; 0.80 and 0.68 for logistic regression). We derived importance scores based on Shapley values which were consistent with the ORs, despite the underlying machine-learning model being intrinsically different to the logistic regression. Chronic heart disease, hypertension, other comorbidities, and some ethnicities had Shapley impacts on death ranging from positive to negative among different patients, although consistently associated with ICUA for all. Immunosuppressive disease, type-2 diabetes, and chronic liver and respiratory diseases had positive impacts on death with either positive or negative on ICUA. We highlight the complexity of informing clinical practice and public-health interventions. We recommend that clinical support systems should not only predict patients at risk, but also yield interpretable outputs for validation by domain experts.
| Item Type: | Journal Article | ||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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| Journal or Publication Title: | PLoS Computational Biology | ||||||
| Publisher: | Public Library of Science | ||||||
| ISSN: | 1553-7358 | ||||||
| Official Date: | 23 June 2021 | ||||||
| Dates: |
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| Volume: | 17 | ||||||
| Number: | 6 | ||||||
| Article Number: | e1009121 | ||||||
| DOI: | 10.1371/journal.pcbi.1009121 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
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