评估COVID-19患者不良结局风险的放射学和实验室生物标志物的预后价值

А. D. Strutynskaya, M. Karnaushkina, L. I. Dvoretskiy, I. Tyurin
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引用次数: 0

摘要

目的:研究新冠肺炎实验室和放射学生物标志物之间的关系,建立新冠肺炎患者病情恶化和死亡结局的预后模型。材料和方法。该研究包括162名新冠肺炎患者,根据住院期间是否恶化进行分层。我们评估了胸部计算机断层扫描(CT)数据,根据经验进行评估,并使用半定量量表、血细胞计数和生化血液测试参数进行评估。利用梯度增强和具有S型激活函数的人工神经网络建立了预测模型。后果两种CT征象(疯狂铺路模式、病变内支气管扩张、症状的外周分布、没有主要分布模式、病变分级和程度)和大多数实验室标志物都与恶化及其标准有关。CT严重程度指数与白细胞、中性粒细胞、尿素、天冬氨酸转氨酶、乳酸脱氢酶、肌酸激酶、葡萄糖、C反应蛋白水平呈正相关,与白蛋白、钙浓度和淋巴细胞数量呈负相关。根据分类模型的选择和训练结果,根据住院期间病情恶化、转入重症监护室的需要、机械通气和不良结果对新冠肺炎患者进行分层的最佳方法是梯度增强。结论在我们的研究中获得的基于放射学和实验室参数组合的预后模型,使预测新冠肺炎病程的性质具有高可靠性成为可能。
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Prognostic Value of Radiological and Laboratory Biomarkers for Assessing Risk of Adverse Outcome in Patients with COVID-19
Objective: to study associations between laboratory and radiological biomarkers of COVID-19, to develop prognostic model of deterioration and lethal outcome in a patient with COVID-19.Material and methods. The study included 162 patients with COVID-19 stratified according to the presence or absence of deterioration during hospitalization. We evaluated chest computed tomography (CT) data, assessed empirically and using a semi-quantitative scale, blood cell counts and parameters of biochemical blood test. The predictive model was built using gradient boosting and artificial neural network with sigmoid activation function.Results. Both CT signs (crazy-paving pattern, bronchial dilatation inside a lesion, peripheral distribution of symptoms, absence of a predominant distribution pattern, lesion grade and extent), and most of laboratory markers were associated with deterioration and its criteria. The CT severity index correlated positively with the levels of leukocytes, neutrophils, urea, aspartate aminotransferase, lactate dehydrogenase, creatine phosphokinase, glucose, C-reactive protein, and negatively with the concentrations of albumin, calcium and the number of lymphocytes. Based on the results of the selection and training of classifying models, the optimal method for stratifying patients with COVID-19 on the basis of deterioration during hospitalization, the need for transfer to the intensive care unit, mechanical ventilation, and adverse outcome was gradient boosting.Conclusion. The prognostic model obtained in our study, based on a combination of radiological and laboratory parameters, makes it possible to predict the nature of COVID-19 course with high reliability.
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