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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/201| A machine learning-based model for survival prediction in patients with severe COVID-19 infection | |
| Ying Xiao Haosen Cao Yanyan Chen Tongxin Ren Fang Wang Yaru Xiao Sufang Huang Xi Tan Hai-Tao Zhang Niannian Huang Bo Jiao Yong Zhang Ailin Luo Laurent Mombaerts Junyang Jin Zhiguo Cao Shusheng Li Hui Xu Jorge Goncalves Xiang Huang Yang Xiao Mingyang Zhang Li Yan Maolin Wang Chuan Sun Yuqi Guo Xiuchuan Tang Liang Jin Ye Yuan | |
| Novel Coronavirus | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/2020.02.27.20028027 | |
| The sudden increase of COVID-19 cases is putting a high pressure on healthcare services worldwide. At the current 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 404 infected patients in the region of Wuhan, China to identify crucial predictive biomarkers of disease severity. For this purpose, machine learning tools selected three biomarkers that predict the survival of individual patients 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 paper suggests a simple and operable formula to quickly predict patients at the highest risk, allowing them to be prioritised and potentially reducing the mortality rate. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement None ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data is available once the paper gets accepted. The Python code is available upon request from YY. | |
| Cold Spring Harbor Laboratory Press | |
| 2020 | |
| Preimpreso | |
| https://www.medrxiv.org/content/10.1101/2020.02.27.20028027v3 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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|---|---|---|---|
| A machine learning.pdf | 1.19 MB | Adobe PDF | Visualizar/Abrir |