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运用机器学习研究感染前服用他汀类药物对 COVID-19 严重程度降低的影响。

Studying the Effect of Taking Statins before Infection in the Severity Reduction of COVID-19 with Machine Learning.

机构信息

Department of Infectious Diseases, Mazandaran University of Medical Sciences, P.O. Box: 48175-866, Sari, Iran.

Antimicrobial Resistance Research Center, Communicable Diseases Institute, Mazandaran University of Medical Sciences, Sari, Iran.

出版信息

Biomed Res Int. 2021 Jun 19;2021:9995073. doi: 10.1155/2021/9995073. eCollection 2021.

DOI:10.1155/2021/9995073
PMID:34250095
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8218917/
Abstract

Statins can help COVID-19 patients' treatment because of their involvement in angiotensin-converting enzyme-2. The main objective of this study is to evaluate the impact of statins on COVID-19 severity for people who have been taking statins before COVID-19 infection. The examined research patients include people that had taken three types of statins consisting of Atorvastatin, Simvastatin, and Rosuvastatin. The case study includes 561 patients admitted to the Razi Hospital in Ghaemshahr, Iran, during February and March 2020. The illness severity was encoded based on the respiratory rate, oxygen saturation, systolic pressure, and diastolic pressure in five categories: mild, medium, severe, critical, and death. Since 69.23% of participants were in mild severity condition, the results showed the positive effect of Simvastatin on COVID-19 severity for people that take Simvastatin before being infected by the COVID-19 virus. Also, systolic pressure for this case study is 137.31, which is higher than that of the total patients. Another result of this study is that Simvastatin takers have an average of 95.77 mmHg OSat; however, the OSat is 92.42, which is medium severity for evaluating the entire case study. In the rest of this paper, we used machine learning approaches to diagnose COVID-19 patients' severity based on clinical features. Results indicated that the decision tree method could predict patients' illness severity with 87.9% accuracy. Other methods, including the -nearest neighbors (KNN) algorithm, support vector machine (SVM), Naïve Bayes classifier, and discriminant analysis, showed accuracy levels of 80%, 68.8%, 61.1%, and 85.1%, respectively.

摘要

他汀类药物可以通过其对血管紧张素转换酶 2 的作用来帮助治疗 COVID-19 患者。本研究的主要目的是评估他汀类药物对 COVID-19 感染前已服用他汀类药物的患者的严重程度的影响。所检查的研究患者包括服用三种他汀类药物的患者,即阿托伐他汀、辛伐他汀和瑞舒伐他汀。病例研究包括 2020 年 2 月至 3 月期间在伊朗 Ghaemshahr 的 Razi 医院住院的 561 名患者。疾病严重程度根据呼吸频率、氧饱和度、收缩压和舒张压分为五类:轻度、中度、重度、危急和死亡。由于 69.23%的参与者处于轻度严重状态,结果表明辛伐他汀对 COVID-19 严重程度的积极影响,对于 COVID-19 病毒感染前服用辛伐他汀的人来说。此外,该病例研究的收缩压为 137.31,高于总患者的收缩压。该研究的另一个结果是,辛伐他汀使用者的平均 OSat 为 95.77mmHg;然而,OSat 为 92.42,对于整个病例研究来说处于中度严重程度。在本文的其余部分,我们使用机器学习方法根据临床特征诊断 COVID-19 患者的严重程度。结果表明,决策树方法可以以 87.9%的准确率预测患者的疾病严重程度。其他方法,包括最近邻(KNN)算法、支持向量机(SVM)、朴素贝叶斯分类器和判别分析,准确率分别为 80%、68.8%、61.1%和 85.1%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a6/8218917/87e9d085b222/BMRI2021-9995073.005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a6/8218917/87e9d085b222/BMRI2021-9995073.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a6/8218917/7a35d820c76a/BMRI2021-9995073.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a6/8218917/1fd0ef5bec37/BMRI2021-9995073.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a6/8218917/6a8b1d5799de/BMRI2021-9995073.003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66a6/8218917/87e9d085b222/BMRI2021-9995073.005.jpg

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