Li Daowei, Zhang Qiang, Tan Yue, Feng Xinghuo, Yue Yuanyi, Bai Yuhan, Li Jimeng, Li Jiahang, Xu Youjun, Chen Shiyu, Xiao Si-Yu, Sun Muyan, Li Xiaona, Zhu Fang
Department of Radiology, The People's Hospital of China Medical University & The People's Hospital of Liaoning Province, Shenyang, China.
Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
JMIR Med Inform. 2020 Nov 17;8(11):e21604. doi: 10.2196/21604.
Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection.
This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes.
A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients' CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results.
We present a prediction model combining patients' radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients' laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81.
To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.
2019年冠状病毒病（COVID-19）导致的死亡大多与重症相关。由于缺乏对感染的早期检测，重症病例的有效治疗仍然是一项挑战。
本研究旨在通过将影像学结果与临床生化指标相结合，开发一种用于预测COVID-19严重程度的有效模型。
共检查了46例COVID-19患者（10例重症，36例非重症）。为构建预测模型，从这些患者中收集了27例重症和151例非重症的临床实验室记录及计算机断层扫描（CT）记录。我们利用最近发表的卷积神经网络从患者的CT图像中提取特定特征。我们还训练了一个将这些特征与临床实验室结果相结合的机器学习模型。
我们提出了一种将患者影像学结果与临床生化指标相结合的预测模型，以识别重症COVID-19病例。该预测模型在受试者工作特征曲线下面积（AUROC）的交叉验证得分是0.93，F得分是0.89，与仅基于实验室检测特征的模型相比，分别提高了6%和15%。此外，我们基于患者在被分类为重症病例之前进行的实验室检测结果，开发了一种预测COVID-19严重程度的统计模型；该模型的AUROC得分为0.81。
据我们所知，这是第一份基于实验室检测和CT图像的综合分析来预测COVID-19临床进展以及严重程度的报告。
