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An interpretable mortality prediction model for COVID-19 patients

Nature Machine Intelligence, 2020-05, Vol.2 (5), p.283-288 [Peer Reviewed Journal]

2020. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.springernature.com/gp/researchers/campaigns/coronavirus ;The Author(s), under exclusive licence to Springer Nature Limited 2020. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 2522-5839 ;EISSN: 2522-5839 ;DOI: 10.1038/s42256-020-0180-7

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Title:
An interpretable mortality prediction model for COVID-19 patients
Author: Yan, Li ; Zhang, Hai-Tao ; Goncalves, Jorge ; Xiao, Yang ; Wang, Maolin ; Guo, Yuqi ; Sun, Chuan ; Tang, Xiuchuan ; Jing, Liang ; Zhang, Mingyang ; Huang, Xiang ; Xiao, Ying ; Cao, Haosen ; Chen, Yanyan ; Ren, Tongxin ; Wang, Fang ; Xiao, Yaru ; Huang, Sufang ; Tan, Xi ; Huang, Niannian ; Jiao, Bo ; Cheng, Cheng ; Zhang, Yong ; Luo, Ailin ; Mombaerts, Laurent ; Jin, Junyang ; Cao, Zhiguo ; Li, Shusheng ; Xu, Hui ; Yuan, Ye
Subjects: Biomarkers ; Blood ; COVID-19 ; Decision making ; Fatalities ; Health care ; Health services ; Lymphocytes ; Machine learning ; Mortality ; Prediction models
Is Part Of: Nature Machine Intelligence, 2020-05, Vol.2 (5), p.283-288
Description: 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.
Publisher: Dordrecht: Springer Nature B.V
Language: English
Identifier: ISSN: 2522-5839
EISSN: 2522-5839
DOI: 10.1038/s42256-020-0180-7
Source: Coronavirus Research Database

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An interpretable mortality prediction model for COVID-19 patients

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