A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19
A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19
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DOI：
10.1007/s00330-020-07022-1
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发表时间：
2020-07-01
影响因子：
5.9
通讯作者：
Yang, Wenjie
中科院分区：
文献类型：
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作者：
Qin, Le;Yang, Yanzhao;Yang, Wenjie
目的建立预测模型和评分系统，提高新冠肺炎对冠状病毒病2019年的诊断效率。方法回顾分析2020年1月19日至2月6日确诊的88例新冠肺炎肺炎患者和80例非新冠肺炎肺炎患者的临床资料。收集临床资料和实验室结果。根据病变的位置、密度和形态，在节段性水平评价CT征象和评分。根据肺炎病灶的大小(1~4分)进行评分，并对充气支气管镜、树芽征、疯狂铺路、胸膜下曲线、支气管扩张症、气腔、胸腔积液、纵隔和/或肺门淋巴结肿大进行评估。结果多因素Logistic回归分析显示，接触史(β=3.095，OR=22.088)、白细胞计数(β=-1.495，OR=0.224)、周围病变节段数(β=1.604，OR=1.604)、疯狂铺路模式(β=2.836，OR=2.836)是新冠肺炎阳性患者的预测模型。在该模型中，训练组和测试组的曲线下面积分别为0.910和0.914(p<；0.001)。根据预测模型计算出新冠肺炎的预测评分：PSC-19=2 x暴露史(0~1分)-1 x白细胞计数(0~2分)+1 x周围病变(0~1分)+2 x疯狂铺路模式(0~1分)，最佳截断点为1(敏感性88.5%；特异性91.7%)。结论我们的预测模型和PSC-19可以用于新冠肺炎阳性病例的鉴定，辅助内科和放射科医生直到收到逆转录-聚合酶链式反应的结果。
Objectives To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19). Methods From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non-COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions' position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/or hilar lymphadenopathy were also evaluated. Results Multivariate logistic regression analysis showed that history of exposure (beta = 3.095, odds ratio (OR) = 22.088), leukocyte count (beta = - 1.495, OR = 0.224), number of segments with peripheral lesions (beta = 1.604, OR = 1.604), and crazy-paving pattern (beta = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 x history of exposure (0-1 point) - 1 x leukocyte count (0-2 points) + 1 x peripheral lesions (0-1 point) + 2 x crazy-paving pattern (0-1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%). Conclusions Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription-polymerase chain reaction (RT-PCR) tests.