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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/5036| A machine learning-based model for survival prediction in patients with severe COVID-19 infection | |
| Yan Li. Zhang Hai-Tao. Goncalves Jorge. Xiao Yang. Wang Maolin. Guo Yuqi. Sun Chuan. Tang Xiuchuan. Jin 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. Zhang Yong. Luo Ailin. Mombaerts Laurent. Jin Junyang. Cao Zhiguo. Li Shusheng. Xu Hui. Yuan Ye. | |
| 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. | |
| www.medrxiv.org | |
| 2020 | |
| Artículo | |
| https://www.medrxiv.org/content/medrxiv/early/2020/03/17/2020.02.27.20028027.full.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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| 1108096.pdf | 1.19 MB | Adobe PDF | Visualizar/Abrir |