Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

2019 年冠状病毒病 (COVID-19) 患者的数据对于指导临床决策、进一步了解这种病毒性疾病以及诊断建模至关重要。在这里，我们描述了一个开放资源，其中包含来自 1,521 名肺炎（包括 COVID-19 肺炎）患者的数据，其中包括胸部计算机断层扫描 (CT) 图像、130 种临床特征（来自血液和尿液样本的一系列生化和细胞分析）以及实验室确认的严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 临床状态。我们使用深度学习算法展示了该数据库在预测 COVID-19 发病率和死亡率结果方面的实用性，该算法使用来自 1,170 名患者的数据和 19,685 个手动标记的 CT 切片进行训练。在一个由 351 名患者组成的独立验证队列中，该算法区分了阴性、轻度和严重病例，受试者工作特征曲线下面积分别为 0.944、0.860 和 0.884。该开放数据库可能在 COVID-19 患者的诊断和管理方面有进一步的用途。