Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China.
Int J Epidemiol. 2021 Jan 23;49(6):1918-1929. doi: 10.1093/ije/dyaa171.
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early.
Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients' outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models' performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model.
The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/].
Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients' outcomes.
由严重急性呼吸综合征冠状病毒 2 感染引起的 2019 年冠状病毒病（COVID-19）已在全球范围内传播。我们旨在开发一种临床模型，以尽早预测严重 COVID-19 感染患者的结局。
使用 183 名严重 COVID-19 感染患者（来自武汉同济医院中法新城院区的 115 名幸存者和 68 名非幸存者）的人口统计学、临床和入院后首次实验室检查结果来开发预测模型。采用机器学习方法选择特征并预测患者的结局。采用受试者工作特征曲线下面积（AUROC）比较模型的性能。来自武汉同济医院光谷院区的 64 名严重 COVID-19 感染患者用于外部验证最终预测模型。
幸存者和非幸存者之间的基线特征和实验室检查结果有显著差异。五个模型均选择了 4 个变量（年龄、高敏 C 反应蛋白水平、淋巴细胞计数和 D-二聚体水平）。由于模型的性能相似，选择逻辑回归模型作为最终预测模型，因为它简单且易于解释。外部验证集的 AUROCs 为 0.881。使用死亡概率为 50%作为截断值时，验证集的灵敏度和特异性分别为 0.839 和 0.794。基于所选变量的风险评分可用于评估死亡率风险。预测模型可在 [https://phenomics.fudan.edu.cn/risk_scores/] 上获得。
COVID-19 患者入院时的年龄、高敏 C 反应蛋白水平、淋巴细胞计数和 D-二聚体水平对患者的结局有提示作用。
