The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.
【저자키워드】 viral infection, Infectious diseases, 【초록키워드】 SARS-CoV-2, coronavirus, Prognosis, Pneumonia, SARS-COV-2 infection, discharged patients, fibrinogen degradation products, outcome, Laboratory, sensitivity, specificity, White blood cell, Algorithm, death, receptor, SARS-CoV-2 pneumonia, Myoglobin, Laboratory features, urea, acute respiratory syndrome, logistic regression model, operator, feature, non-survivor, Seven, selected, were used, feasible, indirect bilirubin, laboratory feature, patients with SARS-CoV-2, 【제목키워드】 outcome, Laboratory, SARS-CoV-2 pneumonia,