Department of Pediatric, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Road, Wuhan, 430022, P.R. China.
Huazhong University of Science and Technology Hostipal. Luoyu Road 1037, Wuhan, 430074, P.R China.
Int J Med Sci. 2021 Jan 1;18(1):120-127. doi: 10.7150/ijms.47193. eCollection 2021.
To evaluate the characteristics at admission of patients with moderate COVID-19 in Wuhan and to explore risk factors associated with the severe prognosis of the disease for prognostic prediction. In this retrospective study, moderate and severe disease was defined according to the report of the WHO-China Joint Mission on COVID-19. Clinical characteristics and laboratory findings of 172 patients with laboratory-confirmed moderate COVID-19 were collected when they were admitted to the Cancer Center of Wuhan Union Hospital between February 13, 2020 and February 25, 2020. This cohort was followed to March 14, 2020. The outcomes, being discharged as mild cases or developing into severe cases, were categorized into two groups. The data were compared and analyzed with univariate logistic regression to identify the features that differed significantly between the two groups. Based on machine learning algorithms, a further feature selection procedure was performed to identify the features that can contribute the most to the prediction of disease severity. Of the 172 patients, 112 were discharged as mild cases, and 60 developed into severe cases. Four clinical characteristics and 18 laboratory findings showed significant differences between the two groups in the statistical test (<0.01) and univariate logistic regression analysis (<0.01). In the further feature selection procedure, six features were chosen to obtain the best performance in discriminating the two groups with a linear kernel support vector machine. The mean accuracy was 91.38%, with a sensitivity of 0.90 and a specificity of 0.94. The six features included interleukin-6, high-sensitivity cardiac troponin I, procalcitonin, high-sensitivity C-reactive protein, chest distress and calcium level. With the data collected at admission, the combination of one clinical characteristic and five laboratory findings contributed the most to the discrimination between the two groups with a linear kernel support vector machine classifier. These factors may be risk factors that can be used to perform a prognostic prediction regarding the severity of the disease for patients with moderate COVID-19 in the early stage of the disease.
为了评估武汉中度 COVID-19 患者入院时的特征，并探讨与疾病严重预后相关的危险因素，以便进行预后预测。在这项回顾性研究中，根据世界卫生组织-中国 COVID-19 联合考察组的报告，将中度和重度疾病定义为中度和重度疾病。2020 年 2 月 13 日至 2 月 25 日期间，收集了 172 例经实验室确诊为中度 COVID-19 的患者入院时的临床特征和实验室检查结果。该队列随访至 2020 年 3 月 14 日。将出院为轻症或发展为重症的结局分为两组。采用单因素逻辑回归比较和分析两组间差异有统计学意义的特征。基于机器学习算法，进一步进行特征选择，以确定对疾病严重程度预测贡献最大的特征。在 172 例患者中，112 例出院为轻症，60 例发展为重症。在统计学检验（<0.01）和单因素逻辑回归分析（<0.01）中，两组间有 4 项临床特征和 18 项实验室检查结果有显著差异。在进一步的特征选择过程中，使用线性核支持向量机选择了 6 个特征来获得最佳的两组区分性能。平均准确率为 91.38%，敏感性为 0.90，特异性为 0.94。这 6 个特征包括白细胞介素 6、高敏心肌肌钙蛋白 I、降钙素原、高敏 C 反应蛋白、胸部不适和钙水平。使用入院时收集的数据，线性核支持向量机分类器对两组的区分贡献最大的是一个临床特征和五个实验室检查结果。这些因素可能是风险因素，可用于对疾病早期中度 COVID-19 患者的疾病严重程度进行预后预测。
