Yuan Ye, Sun Chuan, Tang Xiuchuan, Cheng Cheng, Mombaerts Laurent, Wang Maolin, Hu Tao, Sun Chenyu, Guo Yuqi, Li Xiuting, Xu Hui, Ren Tongxin, Xiao Yang, Xiao Yaru, Zhu Hongling, Wu Honghan, Li Kezhi, Chen Chuming, Liu Yingxia, Liang Zhichao, Cao Zhiguo, Zhang Hai-Tao, Paschaldis Ioannis Ch, Liu Quanying, Goncalves Jorge, Zhong Qiang, Yan Li
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Engineering (Beijing). 2022 Jan;8:116-121. doi: 10.1016/j.eng.2020.10.013. Epub 2020 Nov 28.
Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.
2019年冠状病毒病(COVID-19)已成为全球大流行疾病。COVID-19住院患者死亡率很高,这促使人们开发方便实用的方法,以便临床医生能够迅速识别高危患者。在此,我们利用来自中国武汉同济医院1479名住院患者的临床数据(开发队列)制定了一个风险评分,并在另外两个中心的数据上进行了外部验证:中国武汉金银潭医院的141名住院患者(验证队列1)和中国深圳第三人民医院的432名住院患者(验证队列2)。该风险评分基于常规血液样本中易于获取的三种生物标志物,并且能够轻松转化为死亡概率。该风险评分能够提前12天以上预测个体患者的死亡率,在所有队列中的准确率超过90%。此外,Kaplan-Meier评分显示,患者入院时可被明确区分为低风险、中风险或高风险,曲线下面积(AUC)评分为0.9551。总之,一个简单的风险评分已被验证可预测严重急性呼吸综合征冠状病毒2(SARS-CoV-2)感染患者的死亡情况;它也在独立队列中得到了验证。