Early prediction of mortality risk among patients with severe COVID-19, using machine learning.
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.
['Hu|Chuanyu|C|', 'Liu|Zhenqiu|Z|', 'Jiang|Yanfeng|Y|', 'Shi|Oumin|O|', 'Zhang|Xin|X|', 'Xu|Kelin|K|', 'Suo|Chen|C|', 'Wang|Qin|Q|', 'Song|Yujing|Y|', 'Yu|Kangkang|K|', 'Mao|Xianhua|X|', 'Wu|Xuefu|X|', 'Wu|Mingshan|M|', 'Shi|Tingting|T|', 'Jiang|Wei|W|', 'Mu|Lina|L|', 'Tully|Damien C|DC|', 'Xu|Lei|L|', 'Jin|Li|L|', 'Li|Shusheng|S|', 'Tao|Xuejin|X|', 'Zhang|Tiejun|T|', 'Chen|Xingdong|X|']
D000368:Aged D000086382:COVID-19 D016022:Case-Control Studies D005260:Female D006760:Hospitalization D006761:Hospitals D006801:Humans D000069550:Machine Learning D008297:Male D008875:Middle Aged D010343:Patient Admission D012372:ROC Curve D018570:Risk Assessment D000086402:SARS-CoV-2 D012680:Sensitivity and Specificity
COVID-19 machine learning predictive model death fatality rate