建立住院成年患者社区获得性细菌性肺炎和病毒性肺损伤的鉴别诊断模型

O. A. Kupriushina, D. Strelkova, A. S. Yasneva, S. Rachina, S. N. Avdeev, A. Vlasenko, L. Fedina, O. V. Ivanova, I. V. Kaledina, N. Ananicheva
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摘要

相关性。在 COVID-19 大流行期间和之后,病毒已成为成人肺部感染的更常见原因;因此,区分病毒性肺损伤和社区获得性细菌性肺炎变得越来越重要。我们的目标是建立区分社区获得性细菌性肺炎和病毒性肺损伤(包括 COVID-19)的模型。材料和方法。这项回顾性病例对照研究包括 300 名病毒性肺损伤成人患者和 100 名社区获得性细菌性肺炎成人患者。对临床、实验室和仪器数据进行了分析,选出了样本间存在差异的重要因素,并利用逻辑回归建立了一个模型,以区分社区获得性细菌性肺炎和病毒性肺损伤(包括 COVID-19)。结果建立的模型包括以下参数:总蛋白水平、中性粒细胞/淋巴细胞指数、心率、CT 或胸部 X 光片显示的单侧浸润、住院 24 小时内使用血管加压药、意识水平改变、寒战和疲劳。该模型具有以下特点:AUC=0.94(0.92-0.96),AUC_PR=0.84(0.76-0.92),预测准确率 - 90%,灵敏度 - 76%,特异性 - 95%,阳性预测值 - 83%。结论使用该模型有助于对普通病房和重症监护室的成人社区获得性细菌性肺炎和病毒性肺损伤(包括 COVID-19)进行鉴别诊断。
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Development of a model for the differential diagnosis of community-acquired bacterial pneumonia and viral lung injury in hospitalized adult patients
Relevance. During and after the COVID-19 pandemic, viruses have become a more common cause of pulmonary infections in adults; therefore, the distinction between viral lung injury and community-acquired bacterial pneumonia is of increasing importance. Aim. Development of a model for differentiating community-acquired bacterial pneumonia and viral lung injury, including COVID-19. Materials and methods. This retrospective case–control study included 300 adult patients with viral lung injury and 100 adult patients with community-acquired bacterial pneumonia. Clinical, laboratory, and instrumental data were analyzed, significant factors were selected by which the samples differed, and a model was developed using logistic regression to distinguish between community-acquired bacterial pneumonia and viral lung damage, including COVID-19. Results. The developed model included the following parameters: total protein level, neutrophil/lymphocyte index, heart rate, unilateral infiltration on CT or chest x-ray, vasopressor prescription in the first 24 h of hospitalization, altered level of consciousness, chills, and fatigue. The model had the following characteristics: AUC = 0.94 (0.92–0.96), AUC_PR = 0.84 (0.76 to 0.92), prediction accuracy — 90%, sensitivity — 76%, specificity — 95%, positive predictive value — 83 %. Conclusion. The use of this model can facilitate the differential diagnosis of community-acquired bacterial pneumonia and viral lung injury, including COVID-19, in adults in general wards and intensive care units.
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