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基于炎性细胞因子或淋巴细胞亚群的两种新型列线图,用于鉴别诊断重症或危重症与非重症新型冠状病毒肺炎。

Two novel nomograms based on inflammatory cytokines or lymphocyte subsets to differentially diagnose severe or critical and Non-Severe COVID-19.

作者信息

Li Zhijun, Jiang Nan, Li Xinwei, Yang Bo, Jin Mengdi, Sun Yaoyao, He Yang, Liu Yang, Wang Yueying, Si Daoyuan, Ma Piyong, Zhang Jinnan, Liu Tianji, Yu Qiong

机构信息

Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China.

Department of Emergency, China-Japan Union Hospital of Jilin University, Changchun 130021, China.

出版信息

Aging (Albany NY). 2021 Jul 19;13(14):17961-17977. doi: 10.18632/aging.203307.

Abstract

We intend to evaluate the differences of the clinical characteristics, cytokine profiles and immunological features in patients with different severity of COVID-19, and to develop novel nomograms based on inflammatory cytokines or lymphocyte subsets for the differential diagnostics for severe or critical and non-severe COVID-19 patients. We retrospectively studied 254 COVID-19 patients, 90 of whom were severe or critical patients and 164 were non-severe patients. Severe or critical patients had significantly higher levels of inflammatory cytokines than non-severe patients as well as lower levels of lymphocyte subsets. Significantly positive correlations between cytokine profiles were observed, while they were all significantly negatively correlated with lymphocyte subsets. Two effective nomograms were developed according to two multivariable logistic regression cox models based on inflammatory cytokine profiles and lymphocyte subsets separately. The areas under the receiver operating characteristics of two nomograms were 0.834 (95% CI: 0.779-0.888) and 0.841 (95% CI: 0.756-0.925). The bootstrapped-concordance indexes of two nomograms were 0.834 and 0.841 in training set, and 0.860 and 0.852 in validation set. Calibration curves and decision curve analyses demonstrated that the nomograms were well calibrated and had significantly more clinical net benefits. Our novel nomograms can accurately predict disease severity of COVID-19, which may facilitate the identification of severe or critical patients and assist physicians in making optimized treatment suggestions.

摘要

我们旨在评估不同严重程度的新型冠状病毒肺炎(COVID-19)患者的临床特征、细胞因子谱和免疫学特征差异,并基于炎症细胞因子或淋巴细胞亚群开发新的列线图,用于鉴别诊断重型或危重型与非重型COVID-19患者。我们回顾性研究了254例COVID-19患者,其中90例为重型或危重型患者,164例为非重型患者。重型或危重型患者的炎症细胞因子水平显著高于非重型患者,淋巴细胞亚群水平则较低。观察到细胞因子谱之间存在显著的正相关,而它们与淋巴细胞亚群均呈显著负相关。根据分别基于炎症细胞因子谱和淋巴细胞亚群的两个多变量逻辑回归cox模型,开发了两个有效的列线图。两个列线图的受试者工作特征曲线下面积分别为0.834(95%CI:0.779-0.888)和0.841(95%CI:0.756-0.925)。两个列线图在训练集的自展一致性指数分别为0.834和0.841,在验证集分别为0.860和0.852。校准曲线和决策曲线分析表明,列线图校准良好,具有显著更多的临床净效益。我们的新型列线图可以准确预测COVID-19的疾病严重程度,这可能有助于识别重型或危重型患者,并协助医生做出优化的治疗建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/586f/8351679/4fb91689f587/aging-13-203307-g001.jpg

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