Early prediction of mortality risk among patients with severe COVID-19, using machine learning
Early prediction of mortality risk among patients with severe COVID-19, using machine learning
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利用机器学习早期预测重症 COVID-19 患者的死亡风险
DOI：
10.1093/ije/dyaa171
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发表时间：
2020-12-01
影响因子：
7.7
通讯作者：
Chen, Xingdong
中科院分区：
文献类型：
--
作者：
Hu, Chuanyu;Liu, Zhenqiu;Chen, Xingdong
背景由严重急性呼吸综合征冠状病毒2型感染引起的冠状病毒病2019 （COVID-19）已在全球蔓延。我们的目的是建立一个临床模型来早期预测重症COVID-19感染患者的预后。方法采用武汉市同济医院中法新城分院收治的183例COVID-19重症感染患者（存活患者115例，未存活患者68例）的人口学、临床和入院后首次实验室检查结果建立预测模型。使用机器学习方法来选择特征并预测患者的预后。采用受试者工作特征曲线下面积（AUROC）来比较模型的性能。选取武汉市同济医院光谷分院新冠肺炎重症感染者64例，对外验证最终的预测模型。结果幸存者和非幸存者的基线特征和实验室检查有显著差异。5种模型均选择年龄、高敏c反应蛋白水平、淋巴细胞计数和d-二聚体水平4个变量。考虑到模型之间的相似性能，由于逻辑回归模型的简单性和可解释性，我们选择逻辑回归模型作为最终的预测模型。外部验证集的auroc为0.881。当以50%的死亡概率为临界值时，验证集的敏感性和特异性分别为0.839和0.794。基于所选变量的风险评分可用于评估死亡风险。该预测模型可在[https://phenomics.fudan.edu.cn/risk_scores/]]上获得。结论患者入院时年龄、高敏c反应蛋白水平、淋巴细胞计数和d-二聚体水平是影响患者预后的重要因素。
Abstract Background 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. Methods 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. Results 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/]. Conclusions 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.