CUED Publications database

An interpretable mortality prediction model for COVID-19 patients

Yan, L and Zhang, H-T and Goncalves, J and Xiao, Y and Wang, M and Guo, Y and Sun, C and Tang, X and Jing, L and Zhang, M and Huang, X and Xiao, Y and Cao, H and Chen, Y and Ren, T and Wang, F and Xiao, Y and Huang, S and Tan, X and Huang, N and Jiao, B and Cheng, C and Zhang, Y and Luo, A and Mombaerts, L and Jin, J and Cao, Z and Li, S and Xu, H and Yuan, Y An interpretable mortality prediction model for COVID-19 patients. Nature Machine Intelligence. (Unpublished)

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The sudden increase in COVID-19 cases is putting high pressure on healthcare services worldwide. At this stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 485 infected patients in the region of Wuhan, China, to identify crucial predictive biomarkers of disease mortality. For this purpose, machine learning tools selected three biomarkers that predict the mortality of individual patients more than 10 days in advance with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this Article suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.

Item Type: Article
Divisions: Div F > Control
Depositing User: Unnamed user with email
Date Deposited: 15 May 2020 20:04
Last Modified: 09 Sep 2021 02:07
DOI: 10.1038/s42256-020-0180-7